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Analysis of bridge health index in the city and county of Denver, Colorado

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Title:
Analysis of bridge health index in the city and county of Denver, Colorado
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Jiang, Xin
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English
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xii, 93 leaves : ; 28 cm

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Bridges -- Maintenance and repair -- Colorado -- Denver ( lcsh )
Bridges -- Inspection -- Colorado -- Denver ( lcsh )
Bridges -- Evaluation -- Colorado -- Denver ( lcsh )
Bridges -- Evaluation ( fast )
Bridges -- Inspection ( fast )
Bridges -- Maintenance and repair ( fast )
Colorado -- Denver ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Includes bibliographical references (leaves 92-93).
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by Xin Jiang.

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University of Colorado Denver

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t"
/
EXPLORING RELATIONSHIPS BETWEEN THE LIVED EXPERIENCES OF
TEACHERS WHO ARE CULTURALLY COMPETENT AND
THEIR SUCCESS WITH DIVERSE STUDENTS
by
Kimberly Ann Kennedy White
B.A., Metropolitan State College, Denver, 1994
M.A., University of Oregon, Eugene, 1997
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Educational Leadership and Innovation
2006


This thesis for the Doctor of Philosophy
degree by
Kimberly Ann Kennedy White
has been approved
by
Deanna J. Iceman-Sands
Honorine Nocon
Nancy Leech
Lupe Martinez
IH o ok
Date


White, Kimberly Ann Kennedy (Ph.D., Educational Leadership and Innovation)
Exploring Relationships between the Lived Experiences of Teachers who are
Culturally Competent and Their Success with Diverse Students
Thesis directed by Professor Deanna J. Iceman-Sands
ABSTRACT
This study investigated relationships between the lived experiences of highly
effective teachers who are culturally competent and their success with diverse
students. The focus questions for this study were: (a) What do effective teachers
who exhibit cultural competence know about their own family, social, and cultural
backgrounds? (b) What are their experiences with diversity? (c) What are teachers
perceptions of the relationships between their lived experiences and their success with
diverse students? Specifically, this study sought to identify various characteristics of
effective teachers lived experiences that contribute to their cultural competence in
working with diverse students; and uncover teachers attitudes about the relationship
between those experiences and their success with diverse students.
The major components of data collection included an extensive nomination
process of teachers who fit the criteria of the study, identification of critical cases,


classroom observations, and semi-structured interviews. Teachers described details
about their childhood experiences, family beliefs and values, core beliefs and roles as
educators, and perceptions of self as highly effective. Classroom observations
revealed teachers classroom environments, teaching practices, and interactions with
students. The study found that these critical cases, identified as highly effective
teachers who are particularly successful with diverse students, demonstrated varying
degrees of cultural competence. Furthermore, for these critical cases, success with
diverse students developed more from their personal values, beliefs, and attitudes
than from exposure to or preparation for working with diversity.
This abstract accurately represents the content of the candidates thesis. I recommend
its publication.
Signed
Deanna J. Iceman-Sands


DEDICATION
I dedicate this thesis to my amazing husband, best friend, and soul mate, Arlo, and
my two wonderful children, Eamon and Alisonyou are the light of my life and the
center of my universe. Thank for you putting up with me and for your incredible
support during this long journey. I love you!!


ACKNOWLEDGEMENT
I want to thank my advisor, Dr. Deanna Sands, who is an amazing role model and
inspiration. Thanks to all of my committeeDr. Honorine Nocon, Dr. Nancy Leech,
and Dr. Lupe Martinezfor supporting my work and providing much needed
feedback. I also wish to thank the Urban Schools Doctoral Lab for many
opportunities for learning and discussion. Many thanks to the excellent faculty at
Metropolitan State College of Denvers Department of Englishyou encouraged me
as an undergraduate, inspired me to follow my dreams, and welcomed me back as a
colleague. Finally, I want to thank my supportive and loving parentsMom and Leo,
Dad and Darlene, and Suzanne and TimI appreciate your cheering me on and ~
believing in me. Thanks also to the support of my friends, especially Jessica Parker
and Paul and Cheri Humphreyyou rock! Many blessings to everyone Ive
encountered along the way.


TABLE OF CONTENTS
Figures...............................................................xii
Tables...............................................................xiii
CHAPTER
1. INTRODUCTION....................................................... 1
Focus Questions................................................. 5
Conceptual Framework.............................................5
Methodology......................................................6
Researcher Role and Assumptions.................................10
Results and Conclusions.....i...................................12
Organization of the Dissertation................................13
2. REVIEW OF THE LITERATURE............................................15
Challenges of Urban Schools.....................................17
Discontinuity between School and Home Cultures..................19
Characteristics of Effective Teachers in Urban Settings.........23
Attributes of Teachers' Cultural Competence.....................27
Cultural Responsiveness...................................28
Cultural Relevance........................................30
vn


Reflective Teachers,
32
Conceptual Framework............................................34
3. METHODOLOGY.........................................................40
Research Design: Qualitative Case Studies.,.....................42
Site Selection............................................47
Participant Selection.....................................50
Data Collection.................................................60
Classroom Observations....................................60
Semi-Structured Interviews.............................. 64
. Data Analysis..................................................66
Within-Case Data Analysis.................................68
Cross-Case Data Analysis..................................69
. Conclusion.....................................................71
4. WITHIN-CASE RESULTS: CLASSROOM OBSERVATIONS OF
TEACHERS.............................................................74
Valley Elementary...............................................75
Ms. Ramirez...............................................76
Mr. Miller................................................81
Ms. Casadas...............................................87
Mountain Elementary.............................................92
Ms. Kendall...............................................94
viii


Ms. Snider,
100
Ms. Platte.............................................106
5. WITHIN-CASE RESULTS: SEMI-STRUCTURED INTERVIEWS OF
TEACHERS.........................................................113
Valley Elementary.............................................114
Ms. Ramirez............................................114
Mr. Miller.............................................121
Ms. Casadas............................................127
Mountain Elementary...........................................132
Ms. Kendall...............................!............132
Ms. Snider.............................................138
Ms. Platte.............................................145
6. CROSS-CASE ANALYSIS: CLASSROOM OBSERVATIONS AND SEMI-
STRUCTURED INTERVIEWS............................................153
Demographic Data: Overview of Critical Cases..................154
Classroom Observation Data....................................155
Classroom Environment..................................156
Teaching Practices.....................................158
Student Interactions.................................. 159
Evidence of Cultural Competence........................161
Semi-Structured Interview Data................................163
IX


Childhood Experiences.....................................165
Family Beliefs and Values.................................167
Experiences with Diversity................................171
Educator Core Beliefs....................i...............173
Roles as an Educator..................................... 176
Evidence of Cultural Competence......................... 177
Perceptions of Self as a "Highly Effective" Teacher.......181
Conclusion.............-.......................................182
7. DISCUSSION OF FINDINGS.................:..........................'...187
Lessons Learned: Addressing Findings and Assumptions............190
Teachers' Lived Experiences...............................191
Teachers' Experiences with Diversity......................195
Teachers' Perceptions of Lived Experiences.............;... 197
Elements of Teacher Effectiveness and Cultural
Competence.............................................. 198
Dealing with Anomalous Findings: Expanding the View of
Cultural Competence............................................203
Limitations of the Study........................................208
Implications and Areas for Further Study........................210
Conclusion......................................................214
APPENDIX...............................................................216
A. FAMILY NOMINATION FORM.......................................216
x


B. INFORMED CONSENT: CLASSROOM OBSERVATIONS.....217
C. CLASSROOM OBSERVATION TOOL...................218
D. INFORMED CONSENT: INTERVIEWS.................221
E. INTERVIEW PROTOCOL...........................222
REFERENCES...........................................224
xi


LIST OF FIGURES
Figure
1.1 Celtic knot demonstrating the human experience...............................37
3.1 Critical Case Data Collection................................................73
Xll


LIST OF TABLES
Table
3.1 School Demographics According to State Department of Education Data..50
3.2 Themes of Cultural Competence........................................53
3.3 Family Nominations................................................... 56
3.4 Nominated Teachers...................................................58
3.5 Characteristics of Cultural Competence and Tool......................59
3.6 Teacher Demographic Data.............................................63
6.1 Emerging Themes from Classroom Observation Data......................156
6.2 Emerging Themes from Interview Data..................................165
Xlll


CHAPTER 1
INTRODUCTION
Ten years following the publication of her personal account of life as a
teacher, Vivian Paley (2000) reflected:
When I began writing White Teacher, I thought I knew
certain children best because our backgrounds were similar,
and that it was my task to open up the classroom, to explore
and welcome differences. I have since discovered that all
the children have more in common with one another than
any one of them has with me. The major source of
incongruity is between their thinking and mine. (p. 36)
Teachers bring with them a collection of lived experiences and conceptions of self, as
well as a multitude of cultural and social experiences that shape their identities
(Gollnick & Chinn, 2002). At the same time, schools are a meeting place of diverse
peoplestudents, families, teachers, and school staffwho also bring with them
various backgrounds, beliefs, values, and experiences. Current research on schools
acknowledges disconnections between home and school cultures, particularly in
urban areas where student populations tend to be economically disadvantaged and/or
ethnically diverse (Delpit, 1995; Howard, 1999; Kozol, 1991; Nieto, 1996).
Oftentimes, teachers in these urban areas are White and from middle class
backgrounds, yet many of their students come from cultures and experiences that
1


differ dramatically from their own (Banks, Cookson, Gay, Hawles, Irvine, Nieto,
Schofield, & Stephan, 2001). Furthermore, teachers are often unaware of their own
cultural and historical biases (Delpit, 1995). While the population of culturally
diverse students is growing in public schools, the teaching population continues to be
culturally and ethnically homogenous (Capella-Santana, 2003; Thomas, 1994), and
significant numbers of teachers feel unprepared to teach students from diverse
backgrounds (Futrell, Gomez, & Bedden, 2003; Villegas & Lucas, 2002).
An overwhelming number of urban schools struggle to meet federal standards
and are continuously under pressure to meet state achievement goals. In their
comprehensive analysis and report on urban schools, the Pew Charitable Trusts
(1998) found that most students attending urban schools in about half of the states
with large cities failed to reach minimum achievement goals on state standardized
tests. Yet within these urban schools are teachers who show a different picture.
Within the walls of some schools are teachers who are successfully meeting
achievement goals and making positive connections with their students.
Previous research identified several characteristics of teachers who are highly
effective with students from diverse backgrounds and needs (Clark, 2003; Sobel &
Taylor, 2003). Effective teachers included those who had positive attitudes toward all
students and their abilities, teachers who honored and valued diversity and promoted
equity, and teachers who were able to adapt their teaching to meet the needs of all
students. Furthermore, culturally relevant teaching offers teachers the opportunity to
2


bring studentsespecially those who are at risk and attend urban schoolsto the
highest levels of achievement' (Gay, 2002; Ladson-Billings, 2000). According to
Ladson-Billings (1992), culturally relevant teaching is defined as instruction and
curriculum that is culturally congruent to a schools community. In practice, it is
designed not merely to fit the school culture to the students
culture but also to use student culture as the basis for
helping students understand themselves and others,
structure social interactions, and conceptualize knowledge.
(P- 314)
Many educators work to develop culturally competent teachersteachers who exhibit
both culturally relevant and culturally responsive teaching practices-who are able to
make important connections with students different from themselves and who use
instructional strategies that help their students succeed (Gay, 2002; Ladson-Billings,
1995; Middleton, 2002).
Villegas and Lucas (2002) argued that teachers must know themselves before
they can learn about and integrate their students cultures into the classroom. Nieto
(2003b) pointed out that autobiography is part of teaching (p. 123). Other
researchers contend that studying teachers lived experiences prior to and during their
tenure as teachers is vital in understanding the ways in which they develop their
identities as teachers and the impact their lived experiences have on their classroom
practices (Goodson, 1992; Kohl, 1984; Powell, 1996). Questions still exist as to the
role of lived experiences in teachers abilities to be highly effective. Why are certain
teachers highly effective while others are not? How do these teachers develop
3


positive attitudes about students from diverse backgrounds? In what ways do their
lived experiences contribute to their ability to be highly, effective? Furthermore,
are highly effective teachers also culturally competent? This study extends current
research by considering the ways in which teachers lived experiences contribute to
their being highly effective, as well as culturally competent, with students whose
family, cultural, and social backgrounds differ from their own.
The purpose of this qualitative study was to identify highly effective
teachers in low performing schools in order to explore their lived experiences.
Specifically, I hoped to investigate relationships between the lived experiences
(including family, social, and cultural backgrounds and personal experiences with
diversity) of highly effective teachers who are culturally competent (i.e., they
exhibit both culturally relevant and culturally responsive teaching practices) and their
success with diverse students. In the context of this study, I defined culturally
competent teachers as those who:
Know students families and communities;
Value and honor students cultures and backgrounds;
Have positive attitudes towards all children;
Challenge students with high expectations for learning; and
Provide schoolwork that reflects the diverse backgrounds of students.
I sought to identify various characteristics of effective teachers lived experiences that
contribute to their cultural competence in working with diverse students and uncover
4


teachers attitudes about the relationship between those experiences and their success
with diverse students. The goal of this study was to better understand the ways in
which highly effective teachers draw from their lived experiences to foster successful
classrooms and relationships with students from diverse backgrounds.
Focus Questions
Teachers can be and are effective in a multitude of ways and, like all people,
draw upon their lived experiences within socio-cultural contexts (Rogoff, 2003). This
study centered on human experience and the stories that people recall, tell, and
interpret at particular moments in time. Like all people, the teachers in this study are
constantly changing, influenced by experience and time (Clarke, 2003; Goodson,
1992). This study addresses three main focus questions: a) What do effective
teachers who exhibit cultural competence know about their family, social, and
cultural backgrounds? b) What are their experiences with diversity? c) What are the
teachers perceptions of the relationships between their lived experiences and their
success with diverse students?
Conceptual Framework
In the conceptual framework for this study, I describe lived experiences as a
woven mesh of intertwining cultural, socio-economic, and personal experiences,
much like that of a Celtic knot (described in detail in Chapter Two). Lived
5


experiences are ever changing and inherently complex. Experience differs for every
individual; collectively, experiences, including ones family, cultural, and social
background, contribute to a persons identity, or the distinct personality of an
individual (Dictionary.com, 2006). Identities are constructed within social and
cultural backgrounds and further shaped through interactions with others (Holland,
Lachicotte, Skinner, & Cain, 1998).
Within the context of this framework, lived experiences are related to the
development of a persons identity and shape beliefs, values, and attitudes. Lived
experiences, therefore, influence a persons ability to be culturally competent.
Furthermore, the amalgamation of lived experiences contributes to an individuals
ability to work well with diverse students.
Methodology
The nature of the focus questions in this study required in-depth information
about the participating teachers lived experiences. The goals of this study were met
through case studies (Yin, 2003). Creswell (1998) identified case studies as an
exploration of a bounded system or a case (multiple cases) over time through
detailed, in-depth data collection involving multiple sources of information rich in
context (p. 61). 1 gathered data about participating teachers through two major
sources and within the context of purposefully selected sites (Strauss & Corbin,
1990); Yin (2003) argued that case studies should include at least three types of data;
6


a limitation of this study was that I gathered only two types of data. The components
of data collection included classroom observations and semi-structured interviews.
In the context of this study, I defined highly effective teachers as those who
met the following criteria: (a) their classes are consistently high performing according
to state-level, student achievement data; (b) they have been teaching in the school for
at least three years (and therefore, would be more likely to understand and/or
participate in the culture of the school and community); (c) they were nominated by
the schools principal and instructional support staff as being particularly successful
in their work with diverse students; and (d) they were nominated by the families at
the school. The goal was to identify highly effective teachers in high performing
classrooms within schools that are low performing overall.
In order to identify teachers, I first sought out area school districts that had
schools rated as low performing in their student achievement scores, according to "
state-level, student achievement report cards; and schools that have a high percentage
of diverse students and/or high poverty students. I narrowed the field even further to
include only elementary schools to increase the chances of identifying critical cases
who worked within similar contexts. The two buildings included in this study were
identified as urban schools. My doctoral lab, the Urban Schools Research Lab,
defined urban schools as buildings that are situated in urban areas and have a
majority of students (60% or more) who qualify for free and reduced lunches and/or
7


have populations that are made up of a majority of students from diverse socio-
economic and ethnic backgrounds.
In the first stage of identifying critical cases within these two buildings, I
verbally asked for nominations of highly effective teachers from the two schools
principals and learning-support staff (including coaches who worked with teachers
and students and a coach who supported English language learners). Specifically, I
asked them to nominate the teachers in the building who: (a) have consistently high
performing classrooms according to state achievement data; (b) have been teaching in
the school for at least three years; and (c) are culturally competent teachers according
to the criteria I identified for the Family Nomination Form.
The second stage of the nomination process was to determine families views
on teachers in the building. I developed a Family Nomination Form (Appendix A)
that identified characteristics of teachers who are culturally competentteachers who
exhibit both culturally relevant and culturally responsive teaching practices. The form
asked family members to help identify teachers in their school who know students
families and communities, value and honor students cultures and backgrounds, have
positive attitudes towards all children, challenge students with high expectations for
learning, and provide schoolwork that reflects the diverse backgrounds of students.
In order to verify the nomination results and to collect data about culturally
competent teaching practices, I engaged in classroom observations (Creswell, 1998).
The observation tool, adapted from Sobel, Taylor, and Andersons (2003) Diversity-
8


Responsive Teaching Observation Tool (Appendix C), was developed from their
research in urban schools, their work with teacher candidates, and their review of the
literature on dissonance between home and school cultures and culturally relevant
instruction.
Finally, semi-structured interviews allowed me to collect specific data about
lived experiences from critical casescases that uniquely possess characteristics that
contribute to understanding the focus area of the study (Clare & Hamilton, 2003).
Interview prompts were organized into the following categories: (a) family, social,
and cultural background; (b) experiences with diversity; and (c) beliefs about the
impact of these experiences on work with diverse students (Appendix E). Further
questions emerged from the on-going data collection and analysis process.
Prior to data collection, I obtained clearance to conduct research with human
subjects from both the participating school district and the university. Participants
read and signed consent forms prior to data collection (Appendix D). Furthermore, I
provided compensation with gift certificates to each participant. I transcribed
interview data, entered the data into NVIVO (QSR, 1998-2002), and then coded and
analyzed these data through constant comparative analysis (Strauss & Corbin, 1990).
Researcher Role and Assumptions
In the context of this study, my role is both as an insider and outsider within
the broad culture of teaching. I am a White woman who is native to the area; in my
9


extended family, I was the first woman to graduate from college. Although I grew up
in the dominant White culture, I had an economically diverse upbringing. My
childhood (including my parents divorce) provided me the opportunity to live in both
poverty and wealth. My mother and fathers families were very different
agricultural roots from Scotland and Germany on one side; working class, Irish-
Catholics on another; and upper-middle class professionals on another.
In the course of this study, I found that while, my lived experiences differed
from the teachers identified in this study, I also shared a great deal with them. Like
most of these teachers, my immediate family was middle class, moderately liberal,
and Catholic when I was growing up; like all of the teachers in this study, I am
married, and like half of them, I have children.
As a teacher, I have over ten years of teaching experience with students from
kindergarten through college and in both public and private classroom settings. My
current teaching position is in a four-year college preparing students to teach writing
and literacy in elementary schools. Previously, I was an instructor in the universitys
teacher preparation program teaching a graduate classroom management course. I
also bring prior research experience as a coordinator for a faculty research center, a
position that involved extensive work in interview and survey development, data
collection, and analysis.
As part of my doctoral program, I had the opportunity to conduct research
with peers and faculty in a doctoral research laboratory that focused on urban schools.
10


Although I conducted research in the district included in this study, I have never
taught in this particular urban district. Furthermore, this district enrolls high numbers
of students who speak Spanish; I had only a basic understanding of Spanish, although
I did have knowledge of several other languages.
In the process of identifying teachers for this study, I came with certain
assumptions; these pre-conceived notions were not necessarily supported by the data.
In a previous study, in which I conducted interviews, the data revealed that
participating teachers, all of whom but one were White, identified themselves as
having no culture or as American (White, 2005). I came to the present study
with the assumption that teachers who are culturally competent know about their own
cultural backgrounds and realize the complexities of cultural constructs. I assumed
that high performing teachers would also be culturally competent, or, in other words,
that cultural competence is necessary to be a highly effective teacher. ^
Furthermore, because of current statistics on the teacher population in urban
schools (Capella-Santana, 2003; Thomas, 1994), I anticipated identifying White
females as the critical cases; half of the cases for this study, however, were not White
femalesone teacher was male (White), and two teachers were females from
Mexico. The accuracy of these assumptions will be discussed in Chapter Seven;
however, to limit researcher bias, I collected nominations from multiple sources in
order to identify the critical cases before collecting data in the context of the studys
focus questions. A rigorous process in both identification of cases and in data
11


collection allowed me to address the focus questions as independent from
assumptions and biases as possible.
Results and Conclusions
The data from this study demonstrated the complexities of lived experiences
from which teachers drew as they developed as teachers. The results revealed six
critical casesteachers who I determined to be highly effective and culturally
competent. I learned about these teachers lives through classroom observations and
semi-structured interviews; these teachers demonstrated knowledge about their
backgrounds and articulated connections between their values and beliefs, shaped
since childhood, and their success with diverse students. The teachers in this study
were highly effective in different ways and demonstrated a range of culturally
competent classroom behaviors and attitudes towards teaching and their students.
Based on my analysis of the data, I found that teachers who are highly
effective are not necessarily culturally competent. Strong teacher identities, stable
backgrounds, flexibility in teaching, and focus on learning rather than achievement
were all characteristics of these teachers. Furthermore, these teachers perceived that
their success with diverse students developed more from their personal values,
beliefs, and attitudes than from exposure to or preparation for working with diversity.
12


Organization of the Dissertation
In Chapter Two, I review the literature on the challenges of teaching in urban
schools, characteristics of effective teachers who work with diverse students,
attributes of cultural competence, and ways to support the development of successful
teachers who are effective in working with diverse students. In developing my
conceptual framework, I discuss the literature on teachers lived experiences and
teacher identity. In Chapter Three, I provide the detailed methodology for this
qualitative study, including the development of the research toolsthe teacher
nomination and consent forms, the classroom observation tool, and the interview
protocoland the processes for data collection and analysis.
In Chapter Four, I provide details of teachers classroom environments,
teaching practices, and interactions with students through the within-case results of
the classroom observations. Teachers demonstrated a variety of highly effective
teaching practices, as well as a range of culturally competent behaviors; overall, these
teachers had clear expectations and were flexible with students. In Chapter Five, I
present in-depth biographical data from the semi-structured interviewsteachers
family backgrounds, values, and perceptions of their success with diverse students
in a within-case presentation of the results. Teachers shared their lives and discussed
their values and beliefs; they demonstrated stable backgrounds and were articulate
about the impact of their experiences on their teaching.
13


The details of Chapter Six include a cross-case analysis of interview and
classroom observation data by presenting themes that emerged from analysis of the
compiled data. Finally, in Chapter Seven, I discuss the studys overall conclusions
and lessons learned. The findings of this study revealed strong connections
between lived experience, teacher identity, and classroom effectiveness.
The teachers success with diverse students developed more from personal values,
beliefs, and attitudes than from exposure to or preparation for working with diversity.
14


CHAPTER 2
REVIEW OF THE LITERATURE
Teachers face particular challenges in working in urban schools, including
discontinuities between their own backgrounds and values, those of their students,
and between schools and communities (Delpit, 1995; Howard, 1999; Kozol, 1991;
Nieto, 1996). Current research details the ways in which teachers can be effective
with students from diverse backgrounds (Clark, 2003; Sobel & Taylor, 2003; Villegas
& Lucas, 2002), including teachers abilities to be culturally competent (Gay, 2002;
Ladson-Billings, 2000). Lived experiences could offer details for the ways in which
teachers who are highly effective and who are culturally competent develop as
teachers (Goodson, 1992; Kohl, 1984; Powell, 1996).
In this chapter, I review the literature on the challenges of teaching in urban
schools in order to provide a context for this study. Next, I detail what researchers
know about the characteristics of effective teachers who are working with diverse
students and the attributes of cultural competence. Finally, I provide my conceptual
framework for this study in order to explore teachers backgrounds, identities, and
lived experiences. This framework lays the groundwork for my examination of the
15


relationships these experiences play in developing highly effective teachers who are
successful with diverse students.
In this review of the literature, I found that the definitions of what it means to
be highly effective and culturally competent are often subjective or based on
personal experience rather than defined through research-based methods. While
some scholars identify lived experience as an important factor in urban teacher
development, research-based studies are sparse. Few studies examine the impact of
these experiences in shaping urban teacher identities, in developing positive teacher
attitudes about their work with diverse students, and in integrating effective and
culturally competent teaching practices in the classroom.
Challenges in Urban Schools
Teachers, school administrators, students, families, and communities face
numerous challenges in improving urban schools. The Pew Charitable Trusts (1998),
in their comprehensive analysis of 74 large city districts, found that two thirds of
students living in poverty, most of whom attend urban schools, fail to reach basic
achievement levels. Voltz (1998), in her study of 340 urban educators perceptions of
the challenges of teaching in urban schools, found academic underachievement, high
dropout and low attendance rates, issues with discipline, substance abuse, violence,
and depressed motivation for school success (p. 212) as vital areas of concern.
16


Urban schools present additional challenges such as diverse cultural and
socio-economic student populations, large classes, high teacher burnout, and lack of
resources (Bowers, 2000; Nieto, 2003b). In her monograph on educating African-
American students, Ladson-Billings (1994) found that poor economic and social
conditions negatively impact student achievement and success in schools; and Kozol
(1991), in his landmark book Savage Inequities, argued that heavy teaching loads and
low salaries contribute to high teacher burnout.
Further challenges include teacher shortages, particularly in urban schools, as
Hussar (1999) found in his quantitative study projecting the need for teachers in the
next three years, and high numbers of teachers leaving the profession within the first
five years of their careers (Boser, 2000; Ingersoll, 2000). In his Quality Counts 2000
report, Boser (2000) found that one in five novice teachers leave the profession within
three years. Ingersoll (2000), in his policy report on teacher shortages, discovered
that despite large teacher turnover in urban schools, few researchers have sought
research-based explanations.
Issues around diversity continue to be a major challenge to teachers in urban
schools (Delpit, 1995; Kozol, 1991; Powell, Sobel, Hess, & Verdi, 2001). Most
scholars writing about the state of urban schools point to changes in school
demographics as a key challenge for teachers. Numerous researchers found that
students with backgrounds that differ from the dominant culture in the United States
make up more than 80% of students in urban schools, yet most of the teaching force
17


remains homogenous (Gay, 2000; Howard, 1999; Ladson-Billings, 1994; Nieto,
2003a; Powell, Sobel, Hess,'& Verdi, 2001; Taylor & Sobel, 2001). The National
Center for Education Statistics (NES) (2000) found that more than one in every three
public school students comes from a non-White background. Furthermore, the NES
projected that students who come from diverse ethnic backgrounds will make up the
majority of students in all schools by the year 2035.
Nieto (2005), however, pointed out that in 1996, 90.7% of the teaching
population was White, and most teachers entering the field are White, English-
speaking females. White pre-service teachers as a whole ... bring very little cross-
cultural background, knowledge, and experience (Sleeter, 2001, p. 95). Sleeter
(2001), in her review of 80 research-based studies on pre-service teacher preparation,
found that the overwhelming presence of Whiteness can be silencing (p. 101).
These teachers often lack role models in addressing diversity (Delpit & Dowdy,
2002). Taylor and Sobel (2001) surveyed pre-service teachers regarding their beliefs
about diversity and acknowledged that many pre-service teachers have limited
interactions with persons whose backgrounds and needs differ from their own (p.
497).
Furthermore, Nieto (2003a), based on a year-long study of an inquiry group of
urban teachers, argued that many teachers working in urban schools have the least
amount of professional experience and are among the least prepared in teaching
subject matter. Students attending urban schools are likely to encounter the least
18


qualified teachers (David & Shields, 2001). In their final report of a four-year grant
on standards-based reform in urban districts, David and Shields (2001) identified role
models as an important teacher support; the authors stated that a key finding was that
improvements in teaching practice do result when teachers have a clear idea of
what effective instruction looks like, together with sufficient professional
development and support to learn new ideas and put new practices in place.
(ii)
Finally, increased diversity within classrooms, particularly in urban schools,
makes it. likely that teachers will encounter different students with various beliefs,
attitudes, and worldviews (Banks, 2001; Pettman, 1996; Taylor & Sobel, 2003).
Howard (1999) in his lifelong work supporting White teachers development as
multicultural educators, argued that White teachers are often placed in settings with
students from diverse backgrounds and are expected to behave in ways that are not
consistent with their own life experiences, socialization patterns, [or] worldviews (p.
4). In other words, many teachers enter classrooms to work with students with whom
they are largely unfamiliar.
Discontinuity between School and Home Cultures
Based on personal experience and stories in relation to culturally
responsiveness, Gay (2000) and Pang (2001) argued that teachers behaviors and
attitudes greatly influence the academic success of children from diverse
backgrounds. These-behaviors and attitudes are an integral part of teachers
19


identities; when teachers and children interact, the possibility arises for incongruent
cultural perspectives. In her ground breaking monograph on minority and White
teachers who teach minority students, Delpit (1995) argued that cultural
disconnections can occur when individuals from two or more different cultures
interact and encounter miscommunications or gaps in understanding other
perspectives and experiences.
Schools in which the cultural backgrounds of teachers differ significantly
from those of their students because of ethnic, social, religious, or economic reasons
are especially vulnerable to cultural disconnect or cultural incongruence
(Grossman, 1995; Kozleski, Sands, & French, 1993). Taylor and Sobel (2001), based
on their survey of pre-service teachers beliefs about diversity, argued that chances
are high for there to be a discontinuity of culture, language, experiential backgrounds,
expectations, values, and patterns of communication and interaction (p. 497) among
both new and experienced teachers who work with students from diverse
backgrounds. Irvine (2003) analyzed the conditions and reforms in urban schools and
found that many teachers lack knowledge and understanding of their students lives
and experiences. McIntyres (1997) action research study of racism and White
teacher education students, and Olsens (1997) personal experience as a school
reform advocate and her in-depth study of one urban school both identified a similar
problem.
20


Tatums (1997) life work as a teacher on racial identity and racism, found that
White teachers with cultural and social backgrounds from the dominant culture often
identify themselves as free from any sort of cultural identity. He shared one young
White teachers response when she was asked about her ethnicity; she responded,
Im just normal (p. 93). Similarly, in my study of teachers cultural self-
knowledge, most of the White teachers in the focus groups described themselves as
American, just White, or as having no culture (White, 2005). These responses
demonstrated examples of the dichotomy between children from diverse backgrounds
and people from White cultural groupsteachers views that they lack culture
implies that one group has culture while another group (i.e. those from the
dominant White culture) does not.
These differences in cultural perceptions can lead to divisions between
teachers and their students. Delpit (1995) identified two ways in which this clash
affects classrooms. First, cultural disconnect can impact teachers interpretations of
students aptitudes, intent, or abilities as a result of the difference in styles of
language use and interactional patterns (p. 167). A second major impact emerges in
the ways teachers instruct children from diverse backgrounds, as teachers may
incorporate teaching and discipline styles that are at odds with community norms
(p. 167). Gutierrez and Rogoff (2003) associated ideas of cultural hegemony with
deficit syndrome in which teachers attribute school failure to what students of
color dont have and cant do (p. 23). But perhaps it is many teachers, and not
21


students, who are culturally deprived as they often fail to understand and value the
backgrounds of'diverse students.
Nieto (1996), in her argument for the value of multicultural education,
described an example of cultural disconnect when a new teacher, who was White,
began her career in a school with students who were primarily Puerto Rican. While
this teacher was committed to her students and a well-prepared teacher, she was
struggling to communicate with her students. When she explained a particular lesson,
the students were silent but wrinkled their noses; she did not understand that this body
language signified that the students did not understand the lesson. An opportunity
was lost for connecting with her students. Nieto (1996) argued further that
Students whose culture, verbal or nonverbal, is
unacknowledged or misunderstood in their classrooms are
likely to feel alienated, unwelcome, and out of place.
(p. 146)
Teachers benefit from understanding the backgrounds of their students.
Failing to do so, warned Gay (2000), breeds negative attitudes, anxiety, [and] fears
(p. 23). Gay (2000), drawing from her lifes work, further asserted that the greatest
obstacle to effective culturally responsive teaching is mainstream ethnocentrism and
hegemony (p. 208). When teachers believe the dominant cultural background is
right or better than other backgrounds (i.e. hegemony), chances of discontinuity
between teachers and students, schools and communities substantially increase.
22


Teachers who make assumptions about the homes, communities, and lives of their
students further increase the risk of discontinuity.
Characteristics of Effective Teachers in Urban Settings
Evidence is growing that schools with higher numbers of teachers from
diverse backgrounds positively impact student achievement. Dee (2000) conducted
an empirical study of teachers and students randomly placed together in schools
participating in the study. He then analyzed test score data and found that student
assignment to a teacher of their own ethnicity significantly raised math and reading
scores. Similarly, Irvine (2003) argued that students with teachers from diverse
backgrounds not only benefited from these teachers ability to be role models but also
because these teachers bring diverse perspectives and strengths to their teaching. In
Star Teachers of Children of Poverty, renowned teacher, policymaker, and scholar
Haberman (1995) identified several attributes of teachers who are successful with
diverse students, based on his personal experience: they are often older, have
children, bring a variety of other work experiences, and are themselves from diverse
backgrounds.
Powell (1996), in his review of research on teacher effectiveness, found that
teachers do not necessarily have to share ethnicity or culture with their students in
order to be effective. Rather than identifying specific attributes that make teachers
likely candidates for success in urban schools, much of the literature focuses on
23


teachers innate personality traits, and teachers abilities to be sensitive to students
from diverse backgrounds. In his study of four teachers identified via informal
nominations as successful with diverse students, Powell (1996) examined their
teaching strategies, as well as their personal backgrounds, and found that none of
them had training around issues of cultural diversity; much of their success was due
to personal attributes and a care and concern for students that went beyond
achievement scores. According to Kozol (1991), teachers who are successful with
diverse students are often warm, energetic, and bring humor to the classroom. Other
traits of effective teachers include empathy, caring, responsiveness to students
(McAllister & Irvine, 2002; Powell, 1996), and the ability to develop positive self
esteem among students (Delpit & Dowdy, 2002).
In an award-winning dissertation study of 34 teachers beliefs about the role
of empathy and its effectiveness with diverse students, McAllister & Irvine (2002)
conducted a content analysis of more than 125 documents from teachers in a
professional development course on multiculturalism. They found three themes about
these teachers practices: increased positive interactions with culturally diverse
students, supportive classroom climates, and increased student-centered teaching
practices. The researchers concluded that teacher dispositions for empathy can be
developed and nurtured through professional development.
Teachers who are effective in supporting student achievement take a personal
interest in the lives of their children, are flexible and open in learning about students
24


families, and work to develop positive relationships with students (Clarke, 2003;
Nieto, 2005). Honoring variety in home languages and dialects, religious and
spiritual beliefs, and childrens family life allows teachers to positively connect with
their students (Watkins-Goffman, 2001). Furthermore, teachers who learn about the
history and experiences of diverse peoples more readily acknowledge different
experiences in their classrooms as having value (Powell, 1996).
Richards, Brown, and Forde (2004), in their research brief for the National
Institute for Culturally Responsive Educational Systems, recommended eight
activities to support teachers abilities to be culturally responsive to diversity:
engage in reflective thinking and writing; explore personal
and family histories; acknowledge membership in different
groups; learn about the history and experiences of diverse
groups; visit students families and communities; visit or
read about successful teachers in diverse settings; develop
an appreciation of diversity; participate in reforming the
institution. (4-6)
Teachers preparing to work with students from diverse backgrounds must be willing
to learn about students and families (Cochran-Smith, Davis, & Fries, 2004; Wink &
Putney, 2002).
, Delpit (2002) related her own experience with learning about her students
interests and integrating them into the classroom. She worked in a particular school
that was struggling with a group of African American girls who insisted on combing
each others hair rather than completing their school work. Delpit described how she
awoke one night, and thought, Okay, if those kids want to do hair, were going to do
25


hair! (p. 43). She proceeded to work with the other teachers in the building to
develop science, math, history, and business lessons around the topic, of hair
something in which the students were interestedand argued that the object [was]
not to lower standards or just teach what is interesting to the students, but to find the
students interests and build an academic program around them (p. 45).
Delpits experiment provided a specific example of integrating a culturally
relevant teaching approach into her curriculum in order to positively connect with
students. Teachers can create culturally congruent classrooms when they make
instruction and classroom materials personally meaningful to their students (whose
identities extend beyond cultural and ethnic contexts) (Gay, 2000). Students are able
to scaffold their learning more easily when instruction is personally meaningful
(Vygotsky, 1978).
Growing evidence suggests teacher quality as the key to promoting student
success. An issue paper from ETS (2004) defined teacher quality as based on
teachers knowledge of content and how to teach it. The National Commission on
Teaching and Americas Future (1996) argued that what teachers know and can do is
one of the most important influences on what students learn (p. 1), and Haycock
(1998) posited that half of the achievement gap would be eliminated if only students
in urban schools had access to highly qualified teachers. Components of teacher
quality relate to those of cultural competence, but do not necessarily take into account
the role of students cultural, linguistic, and socio-economic backgrounds.
26


Attributes of Teachers Cultural Competence
In order to become culturally competent, teachers must develop cultural
responsiveness and integrate culturally relevant skills in the classroom. Culturally
responsive refers to the ability to recognize, acknowledge, and understand that
people come from different cultural backgrounds with varied norms, values, beliefs,
and practices (Gay, 2000; Delpit, 1995; Ladson-Billings, 1994; Villegas & Lucas,
2002). Additionally, teachers who practice culturally relevant teaching strategies
consider and integrate their students cultures and backgrounds into their classroom
practices and curriculum.
Ladson-Billings (2001) based her theoretical framework for culturally
competent pedagogy (p. 144) on three propositions: successful teachers focus on
students academic achievement, successful teachers develop students cultural
competence, and successful teachers foster students sense of sociopolitical
consciousness (p. 144). Furthermore, she articulated four specific areas that indicate
a teachers cultural competence:
The teacher understands culture and its role in education; the teacher takes
responsibility for learning about students culture and community; the teacher
uses student culture as a basis for learning, and the teacher promotes a flexible
use of students local and global cultures, (p. 98)
Teachers who are culturally competent view cultural differences as strengths; they
empathize with people whose experiences may be challenging; and they integrate
27


knowledge of their students cultures into the curriculum and their teaching practices,
including classroom management.
Cultural Responsiveness
Gay (2000) defined what she calls culturally responsive teaching as
using the cultural knowledge, prior experiences, frames of
reference, and performance styles of ethnically diverse
students to make learning encounters more relevant to and
effective for them. (p. 29)
Furthermore, she identified four critical attributes of culturally responsive instruction
as caring, communication, curriculum, and instruction (p. xiv). Based on her
synthesis of research findings, theoretical claims, practical experiences, and personal
stories (p. 106) of educators working with students from diverse backgrounds, Gay
(2002) argued further that diverse students need culturally responsive teachers who
can significantly improve their students educational outcomes. Similarly, Villegas
and Lucas (2002) identified six characteristics of culturally responsive teachers: a)
they understand that their behavior and thinking is influenced by socio-cultural
contexts; b) they have positive attitudes towards students from diverse backgrounds;
c) they are committed to change; d) they hold constructivist views of learning and
understand learning as an interactive process; d) they take the time to learn about the
lives of their students; and e) they integrate culturally responsive teaching strategies
into their classrooms.
28


Weinstein, Tomlinson-Clarke, and Curran (2004) and Brown (2004) both
argued that cultural responsiveness extends beyond curriculum and teaching
strategies; effective teachers who are culturally competent must also integrate
classroom management practices that are culturally responsive. Weinstein, et. al
(2004), based on the authors body of work, proposed five components of culturally
responsive classroom management: recognition of ones own ethnocentrism;
knowledge of students cultural backgrounds; understanding of the broader social,
economic, and political context; ability and willingness to use culturally appropriate
management strategies; and commitment to building caring classrooms (p. 1).
Furthermore, in Browns (2004) interviews on the classroom management
practices of 13 teachers in urban schools, he found that culturally responsive
classroom management practices include: development of personal relationships with
students; creation of caring communities; establishment of business-like learning
environments; use of culturally and ethnically congruent communication processes;
and demonstrations of assertiveness to be key characteristics in effective culturally
responsive practice. Brown argued that adequately preparing teachers to implement
effective culturally responsive classroom management strategies is essential to their
success with diverse students.
Successful classroom management strategies allow students to self-regulate
their behavior (Freiberg, 1999) and are not based on fear of punishment or reward but
on personal responsibility (McCaslin & Good, 1998; Weinstein, et. al, 2004). But
29


further studies are necessary to fully understand ways in which teachers can develop
culturally responsive classroom management practices. Powell, McLaughlin, Savage,
and Zehm (2001) argued that there is a pressing, unprecedented need for a kind of
management that could be described as culturally responsive. What the shape of this
management might be, however, is illusive and clearly difficult to define (p. 254).
Teachers who are culturally responsive in their interactions with students, teaching
practices, and classroom management have the opportunity to positively and
significantly impact student achievement.
Cultural Relevance
Based on my review of the literature, teachers abilities to be successful with
children from diverse backgrounds closely connect to the tenets of cultural relevance.
Culturally relevant teaching is that which honors, supports, and integrates students
culture (and in the context of the present study, students socio-economic, family
background, language, and individual interests in addition to culture) into classroom
activities (Bennett, 1995; Ladson-Billings, 1994). Teachers who use culturally
relevant practices integrate a variety of instructional strategies and activities into their
classrooms based on students socio-cultural and personal backgrounds (Burnette,
1999). Culturally relevant practices require that teachers provide rationales for school
work and feedback to ensure student understanding of learning objectives and clearly
communicate classroom expectations (Burnette, 1999).
30


Ladson-Billings (1994), in her ethnographic study of teaching African
American students.found that culturally relevant teaching strategies improved the
academic success of diverse student populations; Howard (2001) argued that
culturally sensitive teaching styles improved African-American student performance
on achievement tests. Furthermore, Powell (1996) pointed out that teachers intuitive
strategies enhanced diverse student success, and Bowers (2000) found that
collaborative teaching that honors background differences also positively helped to
transform urban schools from pedagogy of poverty to pedagogy of success.
Furthermore, Ladson-Billings (1994) noted that educators who engage in
culturally relevant teaching practices see teaching as an art rather than as a technical
skill (p. 25). They believe all students can succeed, and they view themselves as
being part of a community. In their research brief on culturally responsive educators,
Kea, Campbell-Whatley; and Richards (2004) found that teachers who integrate
cultural relevance into their practices have more confidence in their abilities to work
with students from diverse backgrounds.
Reflective Teachers
Based on their work with diverse students in which they argued that students
develop a cultural critical consciousness, Gay and Kirkland (2003) identified one
essential element in developing cultural competence as a teacher is the ability to be
reflective of ones teaching identity and practices. Jalongo and Isenberg (1995)
31


defined reflective teachers as those who actively think about their behaviors in the
classroom, those who are open to analyzing themselves, and those who take
responsibility for their teaching and its impact on children. Reflective teachers
explore their own personal and family backgrounds, analyze their own classroom
behaviors, and actively integrate their findings into changing their practice in
classrooms and further developing relationships with their students (Richards, Brown,
& Forde, 2004).
Through their personal ethnographies, Delpit (2002) and Paley (2000)
provided exemplars of reflective practice. For example, in The Skin That We Speak,
Delpit considered the ways in which her past experiences with professionals formed
her beliefs about authority. Paley, in White Teacher, described her discomfort as a
White teacher working with students from diverse backgrounds and how she used
those experiences to positively change her pedagogical and emotional approaches to
teaching. In both works, the authors strive to make meaning of their experiences to
further develop their identities as teachers and scholars.
Gay and Kirkland (2003) argued that a key component to developing cultural
competence is that teachers develop not only the ability to be reflective but to have a
cultural critical consciousness about diversity (p. 181). Coupled with self-
reflection, teachers who are conscious of their own backgrounds, beliefs, biases, and
cultural identities are more likely to positively influence their work with diverse
students. Scholars support the view that culturally responsive teachers are analytical
32


about their teaching and professional identities and have knowledge about their
students and the best ways to teach them (Danielwicz, 2001; Gay, 2000; Ladson-
Billings, 2001). Gay and Kirkland advised that
teachers knowing who they are as people, understanding
the contexts in which they teach, and questioning their
knowledge and assumptions are as important as the mastery
of techniques for instructional effectiveness, (p. 181)
In a study of three effective teachers (Clarke, Davis, Rhodes, & DeLott Baker,
1996), Clarke (2003) articulated what he called coherence as an ideal state in which
teachers take reflection a step further and align their intentions with classroom
practices. A key finding in the study is that successful teachers can look very
different from one another. They can bring to teaching a multitude of experiences
and ideas as well as various teaching styles, methods, and practices and still be
effective with all students. Clarke (2003) described this revelation:
We were amazed at the similarities of their success despite
dramatic differences in their methods and materials. We
concluded that what mattered was the relationships they
established with their students and their ability to work
from a set of core values as they responded to different
situations and events. (p. 7)
Finally, teachers should think reflexively about their own experiences [as] a valid
source of some knowledge and insight (Segal, 1990, p. 122).
Through the literature review, I identified challenges in urban schools,
disconnections between school and home cultures, and characteristics of effective
teachers. These characteristics include teachers abilities to be culturally
33


competentteachers who are culturally responsive with students and who integrate
culturally relevant teaching strategiesand reflective in their teaching practice. The
literature discusses some characteristics of teachers who are highly effective in
working with diverse studentstheir demographics, their dispositions, their teaching
strategies, and their interactions with students. But how do these characteristics
develop? Do lived experiences impact teachers abilities to be successful? How do
their values and beliefs develop? And in what ways do these lived experiences
impact their work in classrooms? The following conceptual framework considers the
lived experiences of highly effective teachers in order to understand the role these
experiences play in their success with diverse students.
Conceptual Framework
Over time, teachers build identities that involve complex intersections of
family, ethnicity, religion, and economic and socio-cultural backgrounds as well as
personal values and beliefs (Clare & Hamilton, 2003; St. Pierre & Pillow, 2000;
Tatum, 1997)or, in other words, the compilation of their ever-changing life
experiences. Figure 1.1 shows how these experiences intertwine and are inextricably
linked, similar to the complex weaving of a Celtic knot. The knot has no linear
beginning or end; instead strands of experiences are woven in and continually build
on each other in layers upon layers.
34


At the center of an individuals inner core are lived experiences that arise
from these intersections and include a multitude of characteristics including gender,
sexual orientation, nationality, age, and wellness (St. Pierre & Pillow, 2000, p. 7).
These intersections of lived experience make up a persons personality and identity
and are based on individuals interactions with others, situations that arise in their
lives, and the choices they make and are, therefore, constantly altered.
Holland, Lachicotte, Skinner, and Cain (1998), drawing on the work of
Vygotsky, Bakhtin, and Bourdieu, argued that identities are constructed within an
individuals social and cultural background and are then further shaped through
interactions in socio-cultural worlds and lived experiences. The authors argued that
identity is one way of naming the dense interconnections between the intimate and
public venues of social practice (p. 270). In addition to elements that contribute to
individual identity development, teachers continually develop professional identities
as educators within the subcultures of their profession and schools (Oring, 1986).
Professional identities, too, are constantly changing based on experiences within the
field and development and reflection of self as an educator (Watkins-Goffman, 2001).
Furthermore, teacher identities are influenced by interactions with other
teachers and teacher role models (Knowles, 1992). Cooper and Olson (1996) argued
that teacher identity is constructed and reconstructed through historical, sociological,
psychological and cultural influences (p. 78). A vital component in teacher identity
is the recognition and development of teachers cultural and ethnic identities (Banks
35


et al., 2001; Carson & Johnston, 2001; Clark & Flores, 2001; Gallavan, 2002; Genor
& Schulte, 2002; Williams & Okintunde, 2002) since the ways in which teachers
view themselves has a ripple effect on the pedagogies which they will incorporate
into their classrooms (McDermott, 2002, p. 54).
Some scholars pointed to the importance of developing professional identities
prior to teachers full time work in the classroom. Clark and Flores (2001) argued
that pre-service teachers need to understand the importance of identity in the
classroom, since the social construction of identity is at the core of how teachers will
come to understand the role of culture (p. 70) in shaping relationships with students.
Successful teachers must have a strong identitythey must know who they are and
what they believein which they are confident, knowledgeable, and committed to
improving the profession (Walling & Lewis, 2000, p. 63). Knowles (1992) found
that teachers with positive school experiences had strong identities and were better
able to deal with the rigours of teaching (p. 136). Teachers identities are shaped by
their lived experiences.
36


Economic
Socio-Culturai
Beliefs/Values
Family
Interactions/
Relationships
Attitudes
Assumptions
Learning
FIGURE 1.1: Celtic knot demonstrating the human experience
Students, too, bring their lived experiences with them every time they enter,
leave, and re-enter the classroom, and those experiences continually change. When
researchers, teachers, and schools talk about ethnically, culturally, and linguistically
diverse students, they are considering only a portion of the constructs that make up
identity, or a persons sense of self. Cultural differences can contribute to
disconnections between schools and families and teachers and students (Banks, et al.,
2001; Delpit, 1995). But the concept of culture is in itself problematic.
Culture is an enormous concept; Levinson (2000) pointed out that scholars
have debated its definition for decades. In the old anthropological model, culture was
viewed as static elements about a particular group of people that are merely
37


transmitted from one generation to the next. In reality, cultural groups are no longer
easy to identify and define (Eisenhart, 2000). Scholars from a variety of fields still
disagree over specific definitions; most would concur that culture is a continual
process of creating meaning in social and material contexts (Levinson & Holland,
1996, p. 13).
While scholars recognize that culture is intrinsic in creating individual
identity, supporting community values and structures, and in building both formal and
informal schooling models, the term culture still remains elusive, subjective, and
problematic, resulting in a concept that has no absolute categories for individuals or
groups (Clare & Hamilton, 2003). Nieto (1996) pointed out that even within cultures,
there are countless differences among people from the same cultural group (p. 147).
Gutierrez and Rogoff s (2003) work considered culture within a cultural-
historical construct rather than culture as static regularities. This theoretical approach
offered a way to get beyond a widespread assumption that characteristics of cultural
groups are located within individuals as carriers of culture (p. 19). Furthermore,
they argued that
equating culture with race, ethnicity, language preference, or
national origin results in overly deterministic, static, weak, and
uncomplicated understandings of both individuals and the
community practices in which they participate (p. 21).
Instead, Gutierrez and Rogoff suggested a cultural-historical approach that accounts
for individual past experiences as key factors in teaching and learning. This approach
38


shifted thinking to individuals experiences participating in cultural practices, rather
than .treating culture as a static trait within an individual. They argued that the
structure and development of human psychological processes emerge through
participation in culturally mediated, historically developing, practical activity
involving cultural practices and tools (p. 21). Lived experiences are the result of
participation in socio-cultural contexts.
When researchers study teachers lived experiences, the data provides only
snapshots of particular moments in time. Furthermore, the details that teachers
provide about their lives are from their immediate perspective. These perceptions and
insights are provided within the context of the constant change of human experience
(Clark, 2003). In the present study, I delved into the lived experiences of highly
effective teachers, and thus examined attributes of their personal and professional
identities, in order to develop a model that illuminates the role of teachers lived
experiences in promoting culturally relevant practices in the classroom and success
with diverse students. The present framework for this study considers a more
complete picture of individual identity as a woven mesh of (a) socio-cultural and
economic contexts such as cultural background, social status, or economic class; (b)
family background and heritage; (c) personal beliefs and values; and (d) physical,
psychological, and cognitive attributes (Cooper & Olson, 1996; Goodson 1992;
Gutierrez & Rogoff, 2003; Holland, Lachiotte, Skinner, & Cain, 1998; Kohl, 1984;
Kozol, 1991; Powell 1996; Tatum, 1997).
39


CHAPTER 3
METHODOLOGY
In this qualitative study, I investigated relationships between the lived
experiences (including family, social, and cultural backgrounds and personal
experiences with diversity) of highly effective teachers who were perceived by
their principals, school learning staff, and families of children attending the school as
culturally competent (i.e., they exhibit both culturally relevant and culturally
responsive teaching practices). In the context of this study, I defined culturally
competent teachers as those who:
o Know students families and communities;
o Value and honor students cultures and backgrounds;
o Have positive attitudes towards all children;
o Challenge students with high expectations for learning; and
o Provide schoolwork that reflects the diverse backgrounds of students.
Specifically, I sought to identify various characteristics of highly effective teachers
lived experiences that contribute to their cultural competence in working with diverse
students; and uncover teachers attitudes about the relationship between those -
40


experiences and their success with diverse students. The focus questions to further
this study were:
(a) What do highly effective teachers who exhibit cultural competence
know about their own family, social, and cultural backgrounds?
(b) What are their experiences with diversity?
(c) What are teachers perceptions of the relationships between their lived
experiences and their success with diverse students?
As I identified teachers for this study and launched into the data collection,
my research focus changed. Based on teachers self-reporting and interpretations of
their own lived experiences, I altered the focus questions to clarify that understanding
relationships between lived experience and student success came from teachers own
perceptions. Furthermore, I assumed that highly effective teachers would also be
culturally competent. Although principals, school staff, and families identified
teachers who were both highly effective and culturally competent, I discovered from
the classroom observation and interview data that these six teachers demonstrated a
range of culturally competent behaviors. Based on my observations of teachers
behaviors in their classrooms and multiple examples from the interviews, I
determined that three of the teachers consistently met all five of the criteria that
define cultural competence in the context of this study, while two teachers
consistently met three of the criteria and one teacher consistently met only two of the
criteria.
41


The nature of the focus questions in this study required in-depth information
about the participating teachers lived experiences; furthermore, this study focused on
particular individuals and their unique qualities. Therefore, the goals of this study
were met through case studies (Yin, 2003). Yin (1992) described case studies as an
empirical inquiry in which the researcher explores a phenomenon within its real-
life context (p. 123) via multiple sources of data. Creswell (1998) identified a case
study as an exploration of a bounded system or a case (multiple cases) over time
through detailed, in-depth data collection involving multiple sources of information
rich in context (p. 61). I gathered data about participating teachers through two
major sources and within the context of purposefully selected sites (Strauss & Corbin,
1990); the components of data collection included classroom observations and semi-
structured interviews.
Research Design: Qualitative Case Studies
As outlined in the conceptual framework for this study, teachers bring to the
classroom a lifetime of experiences, as well as a collection of beliefs, values, and
attitudes that are constantly changing. This project investigated six critical case
studies (Yin, 2003) of highly effective teachers who are successful with diverse
students. Onwuegbuzie and Leech (in press) identified critical cases as those
selected individuals who bring to the fore the phenomenon of interest such that the
researcher can learn more about the phenomenon than would have been learned
42


without including these critical cases (p. 12). Case studies allowed me the
opportunity to collect, examine, and analyze in-depth information about critical cases
and the contexts in which they work (Clare & Hamilton, 2003; Creswell, 2002; Yin,
2003). v
The processes of qualitative research are recursive and interpretive in nature
as researcher and participants undertake a learning process and work together to make
sense of the data (Goodson, 1992; Knowles & Holt-Reynolds, 1991). In the context
of this study, the critical cases were related to teachers in the two buildings who were
nominated by the principals, learning-support staff, and families at the school as
being highly effectiveteachers who have high performing classrooms, according
to district standards, and teachers who exhibit cultural competence (teachers who
have cultural responsive behaviors and who engage in culturally relevant teaching
practices).
The success of this study depended on identifying critical cases of individuals
who met the criteria for the studys focushighly effective teachers who exhibit
culturally competent behaviors in-schools that are low performing overall and yet
have high performing classrooms. After careful selection of these critical cases, my
purpose in this study was to explore their lived experiences to understand the possible
relationships between the experiences of these teachers and their success with diverse
students.
43


In order to adequately and appropriately explore the studys focus area,
sampling was particularly important. Onwuegbuzie and Leech (in press) contended
that qualitative power analysis represents an analysis of the ability or capacity to
perform or act effectively with respect to sampling (p. 18). This power analysis
was particularly useful in the present study as I undertook a multi-staged purposeful
sampling approach to identifying critical cases (Onwuegbuzie & Leech, in press, p.
15). My first step was to identify particular schools that fit the criteria of the study
through purposeful sampling (Miles & Huberman, 1994); then, within those identified
buildings, I sought nominations of critical cases from the principal, the learning-
support staff, and families at the school, and thus identified critical cases via a
funneling sampling sequence (Erickson, 1986). Onwuegbuzie and Leech (in press)
suggested that
if the goal is not to generalize to a population but to obtain
insights into a phenomenon, individuals, or events. .then
the researcher purposefully selects individuals, groups, and
settings that maximize understanding of the phenomenon.
(p. 11)
I worked one-on-one with principals to examine the nomination data to
identify critical casesin this instance, teachers in the school who were considered
highly effective. In the context of this study, highly effective teachers were those
who met the following criteria: (a) their classes were consistently high performing
according to state-level, student achievement data; (b) they had been teaching in the
school for at least three years (and therefore, would be more likely to understand
44


and/or participate in the culture of the school and community); (c) they were
nominated by the schools principal and instructional support staff as being
particularly successful in their work with diverse students; and (d) they were
nominated by the families at the school.
The goal here was to identify highly effective teachers in high performing
classrooms within schools that are low performing overall. I worked with the
principal to obtain details about each of the top nominees. Together, we talked about
each person to confirm nominations as well as eliminate nominees who didnt fit the
criteria. Nominees were eliminated for three reasons: they did not have high
performing classrooms according to state achievement goals; they had not taught in
the building for three years; or they were not classroom teachers.
The teachers identified as critical cases uniquely possess characteristics that
contribute to understanding the focus of the study (Clare & Hamilton, 2003). In order
to identify these characteristics, I collected data about their classrooms and work as
teachers from the principal. For example, principals provided me with demographic
information about the number of years the teachers had been in the building, the
students and classes they teach, and their classroom achievement data. Next, I
collected data about teachers culturally competent teaching practices through
classroom observations. Finally, semi-structured interviews allowed me to collect
specific data about lived experiences. Creswell, Plano, Gutmann, and Hanson (2003)
argued that semi-structured interviews allows researchers to give voice to diverse
45


perspectives, to better advocate for participants, or to better understand a
phenomenon or .process that is changing as a result of being studied (p. 228).
In this study, I delved into the lived experiences of participating teachers to
explore, learn, and understand their development as highly effective teachers and
the ways in which their experiences connect to success with children. Clare and
Hamilton (2003) argued that life history and biographical research inextricably link
data collection, interpretation, and writing in ways that support the authenticity of
the personal insights and experiences of the narrator (p. 103). Investigating the
lived experiences of critical cases can help me, participants, and readers recognize a
multiplicity of perspectives (Jalongo & Isenberg, 1995, p. 14).
Prior to data collection, I secured human subjects clearance from both the
university and the school district in which the research took place. This process
included an examination of the studys potential benefits and risks. Although the
study presented no direct benefits, teachers participating in this study may better
understand the relationships between their lived experiences and their work with
diverse students. Furthermore, this knowledge and understanding may positively
impact their classroom work. Overall, the studys findings have implications for
preparing pre-service teachers, particularly around working with diverse students;
supporting the professional development of teachers; and understanding the
relationships between home and school cultures.
46


The study presented no physical risks to participants. Teachers identified for
this study were asked questions about their life experiences that may have been
difficult to answer or caused discomfort and anxiety. Furthermore, participants
relationships with their peers may have been altered, and other teachers may treat
them differently if they know participants have been identified as highly effective.
In order to reduce the chances of this happening, I indicated to teachers that their
participation was not communicated to others in the building, and I asked the schools
principal to refrain from sharing the information about the study or participants with
others. Additionally, I made every effort to ensure that the data from the interviews,
as well as participant demographic information, were kept confidential. The data
presented here do not include identifying information; participants are referred to
only by pseudonyms, and the interviews took place at non-school locations.
Site Selection
One of the key components of this study was to examine teachers who are
highly effective with children from diverse backgrounds (i.e. cultural, ethnic,
linguistic, socio-economic, religious, or other), the majority of whom are schooled in
urban areas. The critical cases in this study were those teachers who were identified
as being particularly successful with diverse populations of students; the rationale
was to examine critical cases (Miles & Huberman, 1994) of teachers who work within
struggling schools but who were also successful with diverse student populations. I
47


first sought out area school districts that had schools that are rated as low
performing in their achievement scores, according to state report cards; and schools
that have a high percentage of diverse students and/or high poverty students.
Creswell (1998) advised researchers to gain access to study sites and build
rapport with cases via gatekeepers who are members of or have insider status with
a cultural group (p. 117). The school of education in which I am a doctoral student
houses a teacher education program based on partnerships with area urban schools; as
an instructor in the teacher education program, I had an established relationship with
the director of the program and other site professors. The district included in this
study was one in which the schools met the studys criteria, and the schools shared
similar demographics. I narrowed the field of schools even further to include only
elementary schools to increase the chances of identifying critical cases who worked
within similar contexts.
The schools in this selected district were situated in urban areas, defined by
my doctoral lab (a group of professors and doctoral students who conduct research on
urban education) as large cities that have a core business district, diverse ethnic
populations, a majority of the population who are low income (with some of the
population at high income levels and very little middle income), and transportation
issues. Urban schools within these areas are defined as those that exist in urban
areas and have a majority of students (60% or more) who qualify for free and reduced
48


lunches and/or have populations that are made of up a majority of students from
diverse socio-economic and ethnic backgrounds (Urban Schools Research Lab).
After securing human subjects clearance from both the university and the
district, I determined individual elementary schools within one district that fit the
established criteria for this study. This selected school district included seven
elementary schools; Table 3.1 provides specific data for each building. These schools
each enrolled an average of 450 students. Of these students, an average of 84%
qualified for free and reduced lunch. Ethnically, 78% were identified as Hispanic,
17% as White, and 5% as Other, a category that includes, according to state
reports cards, American Indian, Alaskan Native, Asian or Pacific Islander, and Black.
Of this number, all but two of these schools were rated as low performing
according to state school accountability reports. The two schools rated as average
performing no longer fit the criteria and were eliminated from consideration.
Next, I contacted the remaining five schools by sending the principal (via
email and postal mail) a brief letter describing the study, the type of contacts I would
make, the time involved, and the necessary consent forms; I also included the tools
that would be used for the classroom observations and interviews. In the week
following initial contact, I followed up with phone calls to each principal. Three
principals did not respond, and two principals committed to participating in the study;
both were willing to help identify critical cases for the study and were interested in
learning about the nomination data I would be gathering from families of students in
49


the school. These two schools, Valley Elementary and Mountain Elementary,
(identified by pseudonym) became the two sites in which I identified critical cases for
this study.
Table 3.1
School demographics according to State Department of Education data fall 2004
School Student Population (n) Free and Reduced Lunch (% of n) Hispanic Students (% of n) White Students (% of n) Non-White/Non- Hispanic Students (% of n)
*Valley Elementary 550 81% 74% 22% 4%
*Mountain Elementary 440 83% 77% 17% 6%
Participant Selection
The next sampling step was to identify highly effective teachers who are
culturally competenti.e., teachers who are culturally responsive to their students
and who engage in culturally relevant teaching practiceswithin the two selected
schoolsValley Elementary and Mountain Elementary. This involved a two stage
process; first, I requested nominations of teachers who were high performing and
culturally competent from the principal and learning-support staff at each school. In
the second stage, I surveyed families and asked them to nominate teachers who they
considered to be culturally competent. I drew from non-random sampling to identify
50


participants (Onwuegbuzie & Leech, in press) for both stages of the nomination
process.
The teachers participating in these case studies were selected according to
criterion sampling (Miles & Huberman, 1994) that allowed me to identify critical
case (Onwuegbuzie & Leech, in press) teachers as being highly effective with
students from diverse backgrounds. In the context of this study, critical cases were
teachers in the two buildings who were nominated by the principals, learning-support
staff, and families at the school as being highly effectiveteachers who have high
performing classrooms, according to district standards, and teachers who exhibit
cultural competence.
Development of Cultural Competence Nomination Form. I identified highly
effective teachers who exhibit culturally competent by asking for nominations from
the families of students in the buildings. The voices of the families were crucial in
determining highly effective teachers in each building. Families are part of the
larger community and the ones who spend time with their children; they communicate
with the school and develop perceptions of teachers from their children and from their
interactions with teachers (Barrera & Warner, 2006; Protheroe, Shellard, & Turner,
2003).
In order to develop the nomination form (Appendix A), I reviewed the
literature on teacher attributes that contribute to their being culturally competent with
51


diverse students (Clark 2003; Gay, 2000; Gay, 2002; Kea, Campbell-Whatley, &
Richards, 2004; Ladson-Billings, 1992; Montgomery, 2001; Nieto, 2005; Richards,
Brown, & Forde, 2004; Sobel, Taylor, & Anderson, 2003; Villegas & Lucas, 2002). I
entered these key attributes into Nvivo (QSR, 1998-2002), organized them into
categories, and developed open codes; then, I combined similar attributes into axial
codes or categories (Creswell, 1998).
These categories, including culturally responsive classroom practices, cultural
sensitivity, and family connections, were narrowed into five statements for the family
nomination form. Table 3.2 shows examples of these themes and the ways in which
they correlate to the nomination criteria provided to families. The nomination form
asked family members to help identify teachers in their school who know students
families and communities; value and honor students cultures and backgrounds; have
positive attitudes towards all children; challenge students with high expectations for
learning; and provide schoolwork that reflects the diverse backgrounds of students.
These criteria were included in the nomination process with the schools principals
and learning staff as well.
52


Table 3.2
Themes of cultural competence developed from the literature on culturally competent
teacher attributes and the subsequent criteria for the family nominations
Themes and examples of attributes of teachers who are culturally competent Family nomination criteriateachers in the school who are highly effective and who:
Culturally responsive classroom practices Fosters a student-centered classroom Facilitates constructivist views of learning Builds on cultural strengths of students Adapts lessons to students cultures Challenges students with high expectations Teaches about and celebrates diversity Challenge students with high expectations for learning Provide schoolwork that reflects the diverse backgrounds of students
Cultural sensitivity Acknowledges differences Values diversity Positive attitudes that all students can learn Treats students with equity and respect Value and honor students cultures and backgrounds Have positive attitudes towards all children
Family connections Committed to building caring relationships with students Knows about students families and backgrounds Home environments are values and honored Know students families and communities
53


Process of Critical Case Nominations. In the first stage of identifying critical
cases, I verbally asked for nominations of highly effective teachers from the two
schools principals and learning-support staff (that included coaches who worked
with teachers and students and a coach who supported English language learners).
Specifically, I asked them to nominate the teachers in the building who: a) have
consistently high performing classrooms according to state achievement data; b) have
been teaching in the school for at least three years; and c) are culturally competent
teachers according to the criteria that I identified for the Family Nomination Form. I
developed these nomination criteria under the assumption that the schools principal
and learning-support staff would have access to performance data on teachers in the
building and would have firsthand knowledge of their teaching abilities. At Valley
Elementary, the principal and five learning-support staff clearly nominated three
teachers. In the other building, Mountain Elementary, the principal and four learning-
support staff members nominated eight different teachers.
The families nominations were also an important contribution to identifying
critical cases. In the second stage of the process, I surveyed families in each school
and asked for nominations of teachers who are culturally competent. I compared the
school staffs nominations of highly effective and culturally competent teachers
with the families nominations of teachers who were culturally competent as part of
the critical case identification process. I created the nomination forms with the list of
criteria in English on one side and Spanish on the other and then placed them into
54


envelopes. Because Spanish is the predominant language in both schools, I placed
the Spanish side to the front so families would see it when the envelope was opened.
The school secretaries provided me with the number of students in each classroom; I
then placed enough envelopes for each class in the teachers mailboxes along with a
brief note notifying teachers that envelopes were being sent home as part of a
dissertation and asked that teachers return them to the office unopened. I stamped
each envelope with a request that it be returned to the office.
Several issues arose that were beyond my control. First, families with
multiple children at the school may have completed more than one survey since
classroom teachers sent a nomination form with each student. Second, the number of
nominations sent out reflected the number of students in those buildings rather than
the number of families; the schools could not provide a definitive number of families.
Third, the nominations may have included the views of parents, extended family
members, and/or their student(s) attending the school. In other words, I was unaware
of the elements influencing the nomination choices (i.e. to what degree the family felt
a teacher was culturally competent); furthermore, families may only know some of
the teachers in a building, thus limiting their choices of nominations. Finally, the
nomination forms did not come from the school administration; the results may have
changed if the principal authored the request.
55


Table 3.3
Family nominations for each of the schools participating in the study
School Total nomination forms sent Nomination forms returned English (n) Spanish (n) Percentage two names per form (% of n)
Valley Elementary 550 130 72 58 69%
Mountain Elementary 440 70 36 34 81%
The families at Valley Elementary submitted 130 nomination forms out of 550
that were sent home, and at Mountain Elementary, 70 nomination forms were
returned out of 440 forms that were sent home; of this number, slightly more than half
of the families returned the forms in English and slightly less than half in Spanish.
The forms provided two lines so that families could write down more than one
teachers name; the specific instructions asked them to write down the names of
teachers (plural) who they think are highly effective. Although many families wrote
in only one name and several wrote in three, four, or even five names, the majority of
respondents in both buildings wrote in two names (see Table 3.3). At both schools,
families nominated classroom teachers, learning-support staff, and paraprofessionals.
The nomination form specified teacher, but not specifically classroom, teacher.
The nominations varied for each school. At Valley Elementary, with nearly
twice as many nomination forms submitted than Mountain Elementary, a total of 40
different names were written down, eight of them securing only a single nomination.
The Mountain Elementary nominations also revealed 40 individuals nominated with
56


more than half garnering a single nomination. For the purposes of this qualitative
study, I narrowed down the field to include only those individuals with the highest
number of nominations down to the last nomination by the principal and the support
staff (see Table 3.4); for Valley Elementary, the top individuals had anywhere from
10 to 19 nominations, and for Mountain Elementary, the nomination numbers were
from 6 to 16. Table 3.4 provides details of the top nominations; I included all
nominations within the range even if they were not nominated by the principal and
support staff so that readers could get a sense of the nomination data. I identified a
sample size of three teachers in each building for a total of six critical cases.
The sampling was not arbitrary (Onwuegbuzie & Leech, in press); on the
contrary, the data reached saturation when I identified teachers who met all of the
criteriathey were nominated by the principal and learning-support staff; they were
nominated by families; and they had high performing classrooms according to the
principals verification that these teachers students demonstrated satisfactory and
advanced achievement levels. I asked these top nominated teachersor six critical
cases-to participate in the study.
57


Table 3.4
Nominated teachers: Individuals nominated by family and staff nomination
Valley Elementary Top nominations Number of family nominations (n) Nominations by principal and learning- support staff (5) Inclusion in study
Ramirez (Kindergarten All Day) 19 Nominated by all five staff; principal supported Included
Teacher One 19 Not nominated Not high perform
Miller (5lh grade math) 16 Nominated by two staff; principal supported Included
Non-Teacher One 15 Not nominated ELA coach; not a classroom teacher
Teacher Two 13 Not nominated Not in the building three years
Teacher Three 13 Not nominated Not high perform
Teacher Four 12 Nominated by principal Not high perform
Teacher Five 11 Not nominated Not in building three years
Teacher Six 11 Not nominated Not high perform
Casadas (1st grade bilingual) 10 Nominated by two staff; principal supported Included
Mountain Elementary Top nominations Number of family nominations (n) Nominations by principal and learning- support staff (4) Inclusion in study
Teacher One 16 Not nominated Not high performing
Kendall (1st grade) 11 Nominated by principal; supported by one staff Included
Non-Teacher One 11 Nominated by principal and two staff ESL specialist; not classroom teacher
Snider (2n<1 grade) 9 Nominated by four staff; principal supported Included
Teacher Two 9 Not nominated Not high performing
Non-Teacher Two 7 Not nominated Not classroom teacher
Teacher Three 7 Not nominated Not high performing
Non-Teacher Three 6 Not nominated Not classroom teacher
Platte (3rd grade) 6 Nominated by four staff; principal supported Included
58


Participation in this study was voluntary for identified teachers. To ensure
confidentiality, I assigned a pseudonym to each participant known only by me. Prior
to data collection, I gave teachers consent forms to read and sign in which they agreed
to participate in this study. Teachers signed consent letters for both the classroom
observations (Appendix B) and the semi-structured interviews (Appendix D); each
teacher received a gift certificate for their participation. All materials for this study
will be kept on my personal computer for at least three years. Furthermore, data was
saved on a memory key that will be kept in a safety deposit box. Table 3.5 provides
demographic information of the teachers who were asked to participate in this study.
Table 3.5
Teacher demographic data
Age/Gender Ethnicity Original Home Marital Status Children Religion Class
Mountain
Ramirez 38 Female Hispanic Mexico M 2 Catholic Middle
Miller 40 Male White Local M 3 Catholic Middle
Casadas 33 Female Hispanic Mexico M 0 Catholic Middle
Valley
Kendall 60+ Female White East Coast M 2 Protestant Middle
Snider 33 Female White Local M 0 Catholic Middle
Platte 28 Female White Local M 0 Catholic Middle
59


Data Collection
Following identification of the critical cases for this study, I embarked on
classroom observations and semi-structured interviews. In qualitative studies, the
data collection and data analysis often occur simultaneously (Creswell, 1998); the
classroom observations, therefore, influenced the semi-structured interviewsI asked
some of the interview questions in the context of the teachers classroom practices as
well as from information that I had gathered in the nomination process.
Classroom Observations
Again, the purpose of this qualitative study was to explore relationships
between the lived experiences of highly effective teachers who are culturally
competent and their success with diverse students. Through the classroom
observations, I sought to examine the teaching practices of nominated individuals in
order to identify the use of culturally relevant and responsive behaviors in the
classroom and to explore teachers attitudes towards their students and the ways in
which they connect to diversity in the classroom.
Sobel, Taylor, and Anderson (2003) developed a Diversity-Responsive
Teaching Observation Tool (Appendix C) based on their research in urban schools,
their work with teacher candidates, and their review of the literature on dissonance
between home and school cultures and culturally relevant instruction. Taylor and
Sobel (2001) contended that in order to be successful in todays classroom, teachers
60


must develop positive attitudes about their students, develop the ability to
differentiate instruction, and learn about various cultural perspectives and
developmental needs. Furthermore, teachers should celebrate diversity, and promote
equity for all and well as learn to apply these kinds of understandings to
classrooms (Sobel, Taylor, & Anderson, 2003, p. 47).
The authors developed the tool in collaboration with faculty who represented
the fields of special education and bilingual education (p. 46); these faculty
members formed a critical friends group who reviewed and critiqued the initial draft
of the observation tool. The authors also developed the tool in conjunction with their
work in an urban district; the observation criteria met district standards and supported
teachers development in diversity-responsive teaching. The goal of the tool was to
foster an articulate discussion between supervisor and teacher regarding the
teachers effectiveness in diversity-responsive teaching (p. 47).
The authors then tested the tools effectiveness by piloting the observation
tool with general and special education teachers, pre-service teachers, principals, and
teacher supervisors and mentors. The authors did not test the validity and reliability
of the measure, instead they obtained critical feedback from teachers who piloted the
observation tool and conducted focus groups with pre-service teachers to get their
feedback on the contents of the tool. Finally, the authors asked principals,
supervisors, and mentors who were implementing the pilot tool with staff members to
record their responses about the usefulness of the tool. The components of the
61


diversity-responsive classroom observation tool are supported by the literature on
cultural competence. Furthermore, the tool supports a broad definition of diversity to
include culture, language, ethnicity ... ability, gender, socioeconomic level,-
religion, age, and sexual orientation (Sobel, Taylor, & Anderson, 2003, p. 47).
I adapted this tool in order to observe the classroom in two major areas: the
overall tone of the classroom, including environmental print and the groupings and
interactions with students; and the level of teacher cultural competence, including
delivery of culturally relevant instruction and demonstrated understanding and
knowledge of diversity and equity issues. I also used the tool to determine to what
degree a teacher was or was not culturally competent. Some of the questions in the
observation tool provided a rating scale to determine the competency of the teacher
observed (1little to no competency observed; 2fair to adequate competency
observed; 3strong competency observed), while other parts of the tool provided
areas of inquiry in which the researcher described and tallied interactions. Table 3.5
shows the components of the classroom observation tool that correlate with the
characteristics of culturally competent teachers.
62


Table 3.6
Characteristics of cultural competence and the classroom observation tool
Characteristics of Cultural Competence Classroom Observation Tool
Know students families and communities Teachers grouping strategies value diversity Teacher adapts lessons for individual students Teachers distribution of attention to students demonstrates respect for students diverse abilities
Value and honor students cultures and backgrounds Environmental print values diversity Instructional materials value and promote diversity Teacher makes physical and psychological environment safe and conducive for learning
Have positive attitudes towards all children Teachers interactions with student demonstrate respect for all individuals regardless of race, ethnicity, ability, language, gender, sexual orientation, age, or religion Teacher demonstrates consistent positive standards for classroom behavior
Challenge students with high expectations for learning Teacher implements differentiated instructional methods Teacher encourages social and intellectual interactions and promotes meaningful relationships across diverse groups
Provide schoolwork that reflects the diverse backgrounds of students Instructional content is relevant to students experiences and learning styles
The classroom observations of the six critical cases took place over the course
of a month. I first contacted each teacher via phone and when possible, by email
(four out of six of the teachers had a functioning email address) to explain that they
were nominated as highly effective teachers and to ask if they were interested in
participating in the study. After finalizing a convenient schedule for each teacher, I
obtained consent forms for each individual involved (Appendix B). I then observed
each teacher three times in one-hour increments for a total of three hours; to cover as
63


much of the school week as possible, I scheduled observations to see teachers on
different days of the week and varied times for each observation.
For example, I observed one teacher on a Tuesday morning and a Thursday
late afternoon one week and a Tuesday mid-moming the following week. I recorded
observations and descriptive notes on the protocol and wrote reflective notes on the
back of the form (Creswell, 1998). During the first round of observations, I sketched
the layout of the classroom and environmental print, including the number of children
and their seating arrangements. I then continued with the classroom observations,
tallying teacher interactions with students and recording the events and impressions
of the classroom activities. I analyzed the classroom observation data prior to
conducting the semi-structured interviews.
Semi-Structured Interviews
The final phase of the data collection process involved semi-structured
interviews with each of the critical cases. In order to delve into and understand the
relationships between highly effective teachers lived experiences (Creswell,
1998, p. 51) and their success with diverse students, I gathered personal information
about individual participants life experiences, as well as participants reflections on
the meanings of these experiences. Life experience work through the telling of
personal narratives allows researchers to locate the teachers own life story
alongside a broader contextual analysis (Goodson, 1992, p. 6).
64


The protocol for the semi-structured interviews (Appendix E) stemmed from
and was organized around the studys focus questions. Prompts within each major
question allowed participants to identify and discuss their lived experiences in the
context of these focus questions (Carter, 1993; Connelly & Clandinin, 1999;
Goodson, 1992). I generated the interview prompts and areas of inquiry from
multiple sources. Various studies identified early teacher role models, prior teaching
experience, family background, personal experiences, and self-reflection about beliefs
and values as sites of narrative investigation (Carter, 1993; Delpit & Dowdy, 2002;
Goodson, 1992; King & Goodwin, 2002; Knowles & Holt-Reynolds, 1991; Powell,
1996; Watkins-Goffman, 2001). Other researchers identified particular teacher traits
that indicate culturally relevant (Kea, Campbell-Whatley, & Richards, 2004;
Richards, Brown, & Forde, 2004) and reflective and effective teachers with students
from diverse backgrounds (Powell, 1992; Sobel & Taylor, 2003; Taylor & Sobel,
2001).
First, I obtained informed consent from each of the six critical cases
(Appendix D); following the completion of the interviews, I gave each participant a
gift certificate. Interview prompts were organized into the following categories: (a)
family, social, and cultural background; (b) experiences with diversity; and (c) beliefs
about the impact of these experiences on work with diverse students; these categories'
correlate to the studys focus questions.
65


Each interview lasted an hour to an hour and a half with brief follow up
questions either via phone or email. To accommodate the busy schedules of teachers
and to interview outside of the school day (which in half the cases, took place on the
weekend), I conducted the interviews by phone. Using a tape recorder specifically
designed for use with the telephone, I recorded the interviews on cassette tapes,
transcribed them onto Microsoft Word, and then loaded the data into NVivo (QSR,
1998-2002).
Data Analysis
The data analysis occurred simultaneously with data collection as a means of
developing a complete picture of the case studies (Merriam, 1988; Stake, 1995; Yin,
1989). For example, I gathered data about teachers during and after the nomination
process; I gathered classroom observation data, and the classroom observation data
informed the interview prompts. Data collection focused on individual experiences;
therefore, I inductively coded and analyzed data through constant comparative
analysis (Glaser & Strauss, 1967; Strauss & Corbin, 1990). Following the
nomination process of critical cases, I conducted classroom observations and semi-
structured interviews; these data provided both the within-case results and cross-case
analysis (Creswell, 1998) necessary to understand these critical cases.
The purpose of the classroom observations was to gather data about the
classroom practices and student interactions of these critical cases to further
66


understand highly effective teachers. Using a classroom observation tool, based on
a tool developed by Sobel, Taylor, & Anderson (2003), I collected data about the
classroom environment, interactions with students, and teaching practices. During
and after the classroom observations, I took field notes and recorded impressions and
reflections. Following the classroom observations and data analysis, I conducted
semi-structured interviews. The purpose of the interviews was to understand the
lived experiences of highly effective teachers including biographical information,
beliefs and values, and teaching experiences. I first asked participating teachers about
their own family background and life experiences that brought them to teaching. I
then asked teachers to articulate their beliefs about diversity and to describe their
interactions with students from diverse backgrounds. Finally, I asked them to talk
about their perceptions of why they were selected as highly effective teachers and
the ways in which their background impacts their success with diverse students. I
tape recorded, transcribed, and entered the interview data into Nvivo (QSR, 1998-
2002).
Within-Case Data Analysis
In order to present the within-case results, I analyzed individual teacher
classroom observation data and semi-structured interview. First, I organized material
into the categories from the observation tool: classroom environment, teaching
practices, and interactions with students. Next, I read through each category of data
67


to identity words and phrases that captured the main themes and characteristics of
that category. For example, in the classroom environment category, I found several
codes in the analysis of each case: environmental print, classroom materials, and
classroom organization. In the teaching practices category, I found information
around attention to multiple intelligences; and in the interactions with students
category, I found examples of classroom and .behavioral management and teachers
attitudes towards students.
Similarly, I organized the interview data of individual teachers into categories
developed from the studys focus questions: family background and success with
diverse students. Again, I read through each category and identified words and
phrases that captured the experiences of that individual. For example, key
components within the family background category included description of
families beliefs, values, and attitudes and key events that shaped teachers sense of
self and their own attitudes and beliefs. Key components within the success with
diverse students category included teachers attitudes and beliefs about their
students, their knowledge of students lives and families, and their self perceptions as
highly effective teachers. From these data, I wrote narratives for the within-in case
results that provided a composite picture about the classrooms and lived experiences
of individual critical cases. These narratives included key pieces and examples that
highlight the codes developed from analysis of individual teachers classrooms and
lived experiences.
68


Cross-Case Data Analysis
For the cross-case analysis, I compiled the data of all six critical cases into the
three categories of the classroom observation tool: classroom environment, teaching
practices, and interactions with students. Then, I read through each category in order
to identify patterns and themes through categorical aggregation in which I sought
a collection of instances from the data, hoping that issue-relevant meanings will
emerge (Stake, 1995, p. 45).
From the categories of the classroom observation tool and initial readings, I
identified three codes: classroom environment, teaching practices, and student
interactions. Within each code, I identified themeskey words or phrases that
demonstrated examples within each code. I found two themes within the classroom
environment codeenvironmental print and student choice; four themes in the
teaching practices codeteachers acknowledging multiple intelligences, teachers
acknowledging and attending to differentiated instruction, teachers employing various
instructional delivery methods, and teachers incorporating workshop models; and
three themes in the interactions with students codeteachers had positive attitudes
about students, teachers had clear and high expectations for behavior, and teachers
used positive reinforcements to support students behavior. I then coded all of the
data for that categoryby word, phrase, or paragraph. I continued this work until I
reached saturation and all of the data could be placed into these codes.
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Following the classroom observations and data analysis, I conducted semi-
structured interviews. I developed the protocol for the semi-structured interviews
from the studys focus questions and the literature on lived experience. (Carter, 1993;
Connelly & Clandinin, 1999; Goodson, 1992). In order to address the studys
goalsidentifying characteristics of highly effective teachers lived experiences
and uncovering teachers attitudes about the relationship between those experiences
and their success with diverse studentsI developed and analyzed themes across the
cases via categorical aggregation (Strauss & Corbin, 1990).
First, I created a category for each set of interview questions that correlated to
the studys three focus questions: teachers lived experiences, experiences with
diversity, and perceptions of the relationships of these lived experiences and success
with diverse students. Then, I compiled all responses from the six interviews by
moving the textwords, phrases, and paragraphs1into one of the three categories in
order to organize responses from critical cases. Following this process, all of the
interview data was separated into these three broad categories. Then, within each
category, I read through the data to identify codeswords and phrases that stood out
as particular components within each category. My analysis yielded seven codes,
including childhood experiences, family beliefs and values, experiences with
diversity, educator core beliefs, role as an educator, evidence of cultural competence,
and perceptions of self as a highly effective teacher. I coded all of the data for that
category by compiling all of the words, phrases, or paragraphs from the data into one
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of these seven codes. I continued this work until I. reached saturation and all of the
data could be placed into a code.
I continued my analysis to identify themes within the seven codes by reading
through the data within each code. I identified overall themesor repeated ideas
across the cases. For example, one focus question asked about teachers family
backgrounds. Two codes for this category emerged including childhood experiences
and family beliefs and values. Two themes emerged from my analysis of the
childhood experiences code: teachers described families as stable and caring, and
teachers identified their families as middle class. Four themes emerged from the
family beliefs and values code: teachers strong work ethic as a key value;
discipline in teachers homes was based on respect and responsibility; teachers
families valued their children earning a college education; and teachers held positive
views of diverse peoples. I provided details of all seven codes and emerging themes
in Table 6.2 in Chapter Six.
Conclusion
In order to explore the focus questions for this study, I sought to identify
highly effective teachers in high performing classrooms within schools that are low
performing overall. I gathered nominations from within the school and the
community; first, I compared the nomination data from the principal and learning-
support staff with the nomination data from families. I worked with the principals to
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examine this data and identify the critical cases. Yin (2003) defined case studies as
requiring multiple data sources. A limitation of this study, however, was that I
gathered information about critical cases from only two sources. Data collection
involved classroom observations and semi-structured interviews.
In the process of analyzing the discrete details of the data, I took an
interpretive approach to analysis through coding and theme development according to
Miles and Hubermans (1994) data reduction, data display, and conclusion drawing
and verification model. This approach allowed me to engage in concurrent activity
(Miles & Huberman, 1994, p. 10) throughout the study, thus providing a more
complete exploration and understanding of the recursive and varied processes in
which these highly effective teachers developed.
Figure 3.1 demonstrates the process of identifying critical cases for this study.
I present the results of the classroom observation data in Chapter Four and the
interview data in Chapter Five. In Chapter Six, I provide cross-case analyses of the
classroom observation and interview data. As Stake (1995) suggested, I provide
extensive descriptions of each case and detail the results of data collection. Finally,
in Chapter Seven, I present my interpretations, the studys implications, and the
lessons learned (Lincoln & Guba, 1985).
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Figure 3.1: Critical Case Data Collection
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CHAPTER 4
WITHIN-CASE RESULTS:
CLASSROOM OBSERVATIONS OF TEACHERS
Urban District sits in the middle of interstate highways and major rail lines,
borders an international airport, and is the heart of industry for the metropolitan area.
At the same time, this urban/industrial city has experienced significant increases in
residential growth and is located next to a major metropolitan city. In the midst of
this hustle and bustle sit Valley Elementary and Mountain Elementary, two of seven
elementary schools in the district and home to nearly 1000 students. More than 80%
of the schools populations qualify for free or reduced lunch; around 75% of the
students are Latino (students whose primary language is Spanish) and 20% are White;
another 5% are identified as African American or Asian American. State report cards
rate both schools as low performing.
But appearances can be deceiving. Underneath the ratings and statistics are
vibrant school communitiesand effective teachers who are committed to the
success of all students. I investigated teachers perceptions of the relationships
between their lived experiences and their success with diverse students. Following
the nomination process, I gathered demographic data about the teaching careers,
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students, and classrooms of the critical cases. Classroom observations followed in
which I spent three hours with each of the six critical cases. In this chapter, I provide
the within-case results (Creswell, 1998; Miles & Huberman, 1994) of the classroom
observations; the teachers are arranged by school and then by the highest number of
nominations.
Information for this chapter came from the classroom observations. The
classroom observation tool allowed me to examine three major aspects of these
teachers classroomstheir classroom environment, including the physical layout and
tone of the classroom; their teaching practices, including their established classroom
processes and activities in which students engaged; and their interactions with
students, including the ways in which they respond to students and promote and
reinforce learning. The within-case results of the classrooms of these six teachers
provide snapshots of these teachers and their practices.
Valiev Elementary
Valley Elementary is home to more than 550 students. Eighty one percent of
the schools children qualify for free and reduced lunch. The students at Valley
Elementary are ethnically and linguistically diverse; according to district-provided
data, nearly 75% of these students are Hispanic, another 20% are classified as White,
and 5% are identified as African American and Asian American. First, I requested
nominations of teachers who are highly effective and culturally competent from the
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principal and learning-support staff (five individuals who support teachers and
student learning; one of these members is responsible for supporting English
language learners). Next, I distributed 550 nomination forms to the families at the
school asking them to nominate teachers who they considered to be culturally
competent.
Families returned 130 nomination forms72 were completed in English, and
58 were completed in Spanish. The principal and learning-support staff members
each nominated one teacher whom they believed to be the best teacher in the school;
all members supported the nomination of the three top choices. The nominations from
principals, learning-support staff, and families at the school revealed three strong
teachers: Ms. Ramirez, a full-day bilingual kindergarten teacher; Mr. Miller, a 5th
grade math teacher; and Ms. Casadas, a 1st grade bilingual teacher. All three of these
teachers provided students with stimulating learning experiences without the
disruption of behavioral problems. Each teacher brought his or her own style to the
classroom and demonstrated effective teaching practices and warm and caring
interactions with students.
Ms. Ramirez
Anyone walking into Ms. Ramirezs kindergarten class would immediately
sense a bubbling over of energy and activity. An excited buzz of little voices fill the
room as the children move around in groups from center to center engaging in hands-
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on activities and games. The kindergarten class is smalleight girls and seven boys;
they work together in groups of three or four children. Ms. Ramirez, a para-
professional, and another classroom helper work with each group of students.
Classroom Environment. Ms. Ramirezs classroom is clearly bilingual;
letters, numbers, colors, months of the years, and sight words are written in both
English and Spanish and posted throughout the room. Books in both languages line
the shelves, and a Celebrate Diversity banner graces the top of the main
chalkboard. When I first walked into her classroom, Ms. Ramirez immediately
noticed the comfortable, group-oriented classroom atmosphere.
Tables, rather than individual desks, are placed around the room; two circular,
one rectangular, a triangular art table, and a half moon desk around which the
teachers chair is surrounded by several student chairs. The teachers desk is placed
in a back comer; a large bookshelf divides the room with books and materials filling
each side. Tubs on the walls organize her classroom materials. For individual
reading time, children can relax on the couch or choose one of the many pillows. The
classroom exudes the air of organized chaos; the room is colorful, and the children
constantly move around but in a purposeful manner.
Teaching Practices. Ms. Ramirez uses a variety of kinesthetic, tactile, aural,
and visual materials to teach concepts to her students. For example, at one table
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during literacy time, Ms. Ramirez approaches each student and draws a letter on
their back with her finger. The children have to figure out what letter she is drawing
and then create it. They giggle with delight as they work to make their secret letter
out of clay. Centers, in which small groups of students rotate around three or more
activities, are the main vehicle by which children learn particular subjects in Ms.
Ramirezs classroom.
On one particular day in Ms. Ramirezs classroom, the children are working
on their math in number centers. In one group, she supervises as the children play
i
bingo. One student reaches into a bag, pulls out a number tile, and calls it out in
Spanish. The other students in the group look over their cards carefully for the
number and then cover it with a bean. In another group, one child at a time stands at.
the blackboard on which eight numbers are written. As the para-professional calls out
a number in English, the child at the board finds it and slaps the number with a
flyswatter. In a third group, the children have piles of colored disks; they organize
them by color into a tray and then count each group.
Although her full-day kindergarten includes students who face the greatest
challenges, based on a checklist of various needs and situations that may hinder their
success in school, observers of her classroom would be unable to identify childrens
specific needs on the surface. Ms. Ramirez treats each student respectfully and as
individuals. For example, she makes plans for a centers activity, and the para-
professional asks, Would you like me to take the Tow ones? Ms. Ramirez says
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that she will group the students and asks each student by name to form a group.
While she is purposeful in forming these groups of students, she does not indicate to
students that they are being divided by ability.
In the flyswatter number activity, she writes down eight numbers for one
group of students. When the students rotate to the next center, she erases the numbers
and writes in more difficult ones for the next group. At each center, and with each
group of children, Ms. Ramirez alters the activity slightly to accommodate the
learning process for each group of students without indicating which children are
low or high in a particular subject. She gives directions clearly and repeats them
to ensure that each student hears her and understands the expectations.
After the children go to lunch one day, Ms. Ramirez and I sit down to talk.
Ms. Ramirez indicates that she does have some frustration with a new district-
mandated reading program; she feels that some of these materials are inappropriate
for English Language Learners and for children with learning difficulties. She says
she believes children should leam their native languages in addition to English and
tries to help them develop fluency in both. Furthermore, she works hard to support
the learning needs of children who are advanced and ready for the next challenge and
those children who struggle with learning. For example, in reference to her four
students with identified speech difficulties, she says, The best way is to let them play
and interactand talk! That is what will make them more comfortable and willing to
leam.
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Interactions with Students. Ms. Ramirez is comfortable, relaxed, and patient
with her students. She relies on organized class activities and positive reinforcements
to help students stay on task; occasionally, she redirects a student who needs
reminding of what they are supposed to be doing. Her students move about the
classroom in an organized manner. They are clear about the rules and confidently
move to each center to complete their work.
Behavioral disruptions appear to be minimal. In one particular situation, one
boy bothers two other boys as they work in the centers. Ms. Ramirez tells him, You
are having a difficult time today. Come over here and be with me. The boy stays
with her for a time and continues to work on his activity. He seems happy to stay
with her; Ms. Ramirez cuddles and nurtures the child like a mother until he is ready to
rejoin the group: Ok, can you write it by yourself now? Are you ready to learn the
next one? When he returns to his group, the other children turn their attention to
him. Ms. Ramirez tells the students, Lets not talk to him. He is working. Lets
help him by letting him work alone. All together, we can do it! When there is a
problem such as this one, Ms. Ramirez handles it quickly and with minimal
disruption.
She points out positive role models for what students should be doingLook
at Miguel; he is sitting in crisscross applesauce and is ready to learn. To another
student who is wandering over to another center, she says, You need to finish the
activity before you can come to this station. You need to try so hard; I know you can

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do it! And still to another student who is playing around with his materials, she
points out, If you dont take care of your tray, you will not be able to play at recess.
Do you understand? Come on, are you ready to learn?
She encourages the entire class to stay on task with positive reinforcements.
Frequently, she praises the class with applause and tells them, Great job! Give
yourselves a pat on the back! or everyone is special! and I know you can do it!
Verbal interactions with students may be in English or in Spanish. She moves easily
from one language to the next, and the activities in which students participate are also
bilingual. For example, two centers might be run in English, while the third may be
in Spanish. Rather than mixing the languages too much within an individual center,
the directions and activity are in one consistent language.
Ms. Ramirez treats all of her students with respect. For the center work, she
smoothly alters the materials to accommodate the ability of various groups of
students. She spends time working with and supporting each student. Overall, her
classroom exudes a feeling of a caring community; no shouting, raised voices,
frustration, or threatsinstead, kind words, positive feedback, and a sense of support
in helping children to be their best.
Mr. Miller
When students walk into Mr. Millers 5th grade classroom, they enter into the
orderly world of math. Students are surrounded by numbersplace values, charts,
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measurements, and figuresand a supportive and nurturing environment. In Mr.
Millers class, students have the opportunity to question, explore, and calculate math
in a fun and engaging way.
Classroom Environment. Unlike most classrooms that are situated in a
building, Mr. Millers classroom is in one of the four modular trailers that allow
Valley Elementary to have the room they need to teach all students. After leaving the
confines of the main school building, I walk over to the trailer, up the stairs, and into
Math World.
In addition to the math-related environmental print hanging around the room,
students are greeted with signs of encouragement and reminders about the rules and
class expectations. One sign urges students to Reach for the Stars! and another
says, Welcome to Our School Where no Child is Left Behind with a visual of :
childrens hands from multi-ethnic backgrounds. The Pledge of Allegiance is posted
in both English and Spanish next to a class code of conduct and grading scale. In the
back comer of the small classroom sits Mr. Millers desk, covered with papers.
Posted on the wall are the districts calendar, meeting dates, and other notes. Framed
in the center of the wall is Mr. Millers teaching statement:
My mission as a teacher is to be a catalyst in building a
life-long learner. I will maintain high expectations of my
students while instilling a sense of self-worth, respect, and
academic pride. I will provide students with critical
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thinking skills and social skills which will enhance their
lives.
All 5th grade students spend time in Mr. Millers class, and since I observed
during varied times, the students are different each time I visit. Students enter the
classroom and sit at two long tables with both girls and boys mixed together;
typically, Mr. Miller has 20-25 students for each class period. The first thing students
do is to place their caddy under their seats and take out a notebook and pencil. The
caddy is basically their personal desk and contains their notebooks, books, pens,
pencils, and any other needed items. Students settle in and chat quietly until its time
for Mr. Miller to take the helm and begin class.
Teaching Practices. At the start of each class, Mr. Miller provides large group
instruction. He talks about where they left off from the previous class and introduces
the concepts and exercises for the day. He clearly articulates ideas and specific ways
to solve problems; the numbers and concepts are clear. Students indicate that they
understand the task and then work for a time on the assignments. They are allowed to
quietly work together and talk in a comfortable and relaxed manner. Students seem
to be enjoying working on their problems and are visibly excited when they figure out
solutions.
While students work, he walks around and checks in with each student. He
suggests that they talk with your partner to figure out the next one. Frequently, he
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reminds students that if they have a question to raise their hand, and he quickly
responds. No question is silly or unreasonable. For example, one student is reading a
story problem and asks, What is snail mail? Mr. Miller takes the opportunity to
open the question to the class, and they talk about the difference between snail mail
and email. When moving around and helping students, he gives his attention to each
students problem, and rather than just giving them the answer, helps them to think of
ways they can find answers.
Mr. Miller supports a student-centered environment by encouraging them to
find various ways to solve problems. In one instance, he asks the class for examples
of divisibility. One student suggests the Pledge of Allegiance, You know; the part
about one nation, under God, indivisible. He responds saying, Youre right, thats
one answer. Whats another example? He also looks to his students to provide
ideas. After working on an activity one day, student volunteers come up to the board
to write down their solutions. He asks them to explain what they did and why; he
then asks the class at large for other ways. His concern is that students not only come
to a correct solution, but that they understand how and why it works and makes sense.
As a class community, he often polls them and asks for their opinion: Who liked
this way of solving it the best? Which way was easiest? What about the other way?
Mr. Miller is conscientious of the different needs, styles, and abilities of his
students. He provides enough time for students to complete tasks and offers
additional challenges for those who are ready to move on: I know some of you
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know this, and thats great; you can be our experts to help! Students do not sit
dormant in their chairs. Although they remain seated, they work across from each
other and next to each other at a shared table. Sometimes the students work with
hands-on manipulativessuch as tiles, puzzles, or number wheels. He gives clear
and specific directions verbally and then writes relevant information on the board,
repeats it, and then asks for questions or clarifications.
One day, Mr. Miller has two volunteers come up to the board to work out the
problems as hes teaching about them. Kids work on the same problems at their
desks. He tells them, Be doing it on your own paper, too. If youre having trouble,
look up here to see how theyre doing it or work with your partner. One boy starts to
shout out the answer, but Mr. Miller redirects him: GoodIm glad you already
know it, but lets wait for our friends to figure it out. As he walks around, he notices
one student who is close to the solution: Good, Myra, youre using what we learned
last week. At the end of the exercise, Mr. Miller asks, How many of you feel you
can do it? Most of the class raises their hands. He then asks, How many can try to
do it? All the students raise their hands. Ok, lets try some on your own; compare
your answer with the person next to you. If your answers dont agree, see which one
is right. At the end of the exercise, they talk about possible answers and why some
are better than others.
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Interactions with Students. Student behavior in Mr. Millers class is excellent.
Students are alert, on-task, and follow the rules. These students .are working the
entire class period and have little time for idle chatter. When Mr. Miller needs to
speak to the entire class, he first asks them to give him their attention. The second
time, he asks them to please quiet down. By the third time, all eyes are on him, and
students are listening. A formal system of behavior management is not apparent.
When students are not on task, he nonchalantly reminds them about what they
should be doing: Are you looking up there? To me, it looks like youre looking off
in space. Watch what I am doing here, and then work to solve the first few problems
in your notebook. If students are talking rather than working, which is minimal in
Mr. Millers class, he just needs to give the eye and the students return to their
work. One way that students stay focused is that they are engaged with the problems
most of the timethey watch what he does on the board, they look to their peers for
support, and they work on the problems in their own math notebook. Sometimes, Mr.
Miller reminds students what he wants to see: I hope you are writing down the
answers in your notebook, i know a few of you arent.
Mr. Miller talks comfortably with his students. During class period changes,
students talk with Mr. Miller about their homework, the days assignments, things
going on their lives, or the local football team. He acknowledges each student by
name and if necessary, gets down on his knees by their chairs to work with them on
their level. Both teacher and student are respectful of each other. Mr. Millers style
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is to encourage critical thinking and successful completion of work through positive
feedback. To one student, he says, I know you can do it, girl; I want you to show
me! As he walks around examining student work, he frequently says, Youre right
on track or Excellent day today, guys! He recognizes that another student is
having trouble with the assignment: Its ok if its hard; it can be, and its ok. Well
get it together.
Overall, Mr. Miller provides a classroom that is caring and has a relaxed
atmosphere. Students genuinely enjoy working on the math problems and are
motivated to do well. He is concerned that students understand what they are
learning and how it connects to larger concepts rather then just getting the right
answers; he teaches them how to articulate and explain their ideas and is gentle with
students, but commands respect.
Ms. Casadas
Unlike all the other teachers for this study, Ms. Casadass classroom is
uniqueshe teaches entirely in Spanish. Students spend part of their day with
another teacher learning in English. I have limited understanding of Spanishyet
anyone visiting Ms. Casadass 1st grade classroom would unmistakably find an
organized and well-run class; the children are happy, engaged, well-behaved, and
appear to be enjoying their teacher and their studies. The body language of both
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Full Text
had the most experience in classrooms. Her family background, beliefs, values,
politics, and religion differed from the other teachers.
Despite individual differences, however, all of these teachers were identified
as highly effective. The classroom observations and semi-structured interview data
illuminated some of the attributes of highly effective teachers lives and
experiences. The classroom observation data revealed attributes of these teachers
classroom practices and interactions with students from diverse backgrounds, and the
semi-structured interviews detailed their life experiences, their views about diversity,
and their perceptions of their success with diverse students.
From the classroom observations, several elements emerged that demonstrated
teachers effective practices in the classroom. They created learning environments
that encouraged positive classroom connections. Most of them valued diversity,
attended to multiple intelligences, provided differentiated instruction in order to meet
the needs of all students, and aligned lessons to state standards. Their classroom
processes and expectations for behavior were consistent, yet most of them
demonstrated flexibility in their teaching practices. They ran organized classrooms,
but allowed students choice in their learning. Overall, these teachers interactions
with students were positive; they held high expectations for their students, their
students were happy to be there, teachers listened to students, and they treated
students with respect.
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The semi-structured interviews also revealed elements of teachers care and
concern for their students. All of these teachers identified their families as a key
influence in shaping their beliefs and values. Their families held high expectations
for behavior, were consistent with discipline, and valued education. All of these
teachers had positive experiences in school when they themselves were students.
These teachers demonstrated a range of experiences with diversity; most of these
teachers identified further trainingin the form of graduate school or professional
development opportunitiesas an influence in their ability to successfully work with
diverse students.
While these teachers were all identified as highly effective, they
demonstrated a range of culturally competence. The three teachers from Valley
Elementary consistently demonstrated behaviors, attitudes, and skills that fell within
all five criteria according to my definition of cultural competence. Based on the
classroom observations and interviews with these teachers, each of them
demonstrated extensive knowledge of their students and families. They valued and
honored students cultural backgrounds; for example, they had books in their
classrooms in Spanish. They had positive attitudes towards all children,
demonstrated by their treatment of students in the classroom and belief that all were
capable of succeeding. They challenged students with high expectations for learning,
and they provided schoolwork that reflected the diverse backgrounds of students.
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On the other hand, the three teachers from Mountain Elementary
demonstrated only some of the criteria for culturally competent behaviors, attitudes,
and skills according to my definition of cultural competence. Based on the classroom
observations and interviews, all three of the teachers demonstrated knowledge of their
students backgrounds and families and challenged students with high expectations
for learning. Two of the teachers had positive attitudes towards all children, but their
classroom management systems contrasted with the literature on culturally responsive
classroom management practices that encourages intrinsic rewards (Frieberg, 1999;
McCaslin & Good, 1998; Weinstein, et. al, 2004). The third teacher did not always
have positive attitudes about her students, evidenced in her frustration and impatience
with students in the classroom.
Some of the classroom activities of these three teachers attended to multiple
intelligences and differentiated instruction, but the activities themselves were not
necessarily meaningful to students or connected to their diverse backgrounds. Two of
these teachers valued and honored students cultures and backgrounds with some of
their actions and attitudes, but did not always understand or feel prepared to address
students differences. The third teacher often privileged her own opinions, ideas, and
ways of doing things without consideration for her students. Finally, the value of
cultural diversity was less apparent in these three teachers classrooms as evidenced
in their environmental print and integration of students cultures and backgrounds
into classroom practices.
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Overall, these teachers brought positive attitudes about their students to the
classroom and empathized with them even when they could not personally relate to
their students circumstances. They knew what their students needed, they
encouraged students to learn about themselves, and they brought their own
experiences into the classroom when appropriate. Finally, a key finding in this study,
consistent with Powells (1996) finding, was that although these teachers have the
highest achieving classrooms, they did not focus solely, or even mainly, on student
achievement. Instead, their main goal was making strong connections with students
by creating safe environments in which students have positive learning experiences.
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CHAPTER SEVEN
DISCUSSION OF FINDINGS
In the conceptual framework for this study, I described lived experience as a
woven mesh of intertwining cultural, socio-economic, and personal experiences,
represented as a Celtic knot. These ever changing lived experiences are inherently
complex and differ for every individual; collectively, they contribute to a persons
identity, or the distinct personality of an individual (Dictionary.com, 2006).
Holland, Lachicotte, Skinner, and Cain (1998), drawing on the work of Vygotsky,
Bakhtin, and Bourdieu, argued that identities are constructed within an individuals
social and cultural background and are then further shaped through interactions in
socio-cultural worlds and lived experiences. The data from this study demonstrated
the complexities of lived experiences, past and present, from which teachers have
drawn as they developed as teachers. Within this framework, every individual has
both unique experiences and experiences shared with others.
In order to explore the impact of the lived experiences of highly effective
teachers on their success with students, I developed focus questions for this study
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with the assumption that highly effective teachers would also be culturally competent.
My questions changed when I found.a range of culturally competent behaviors among
these highly effective teachers. The three questions that guided data collection were:
a) What do effective teachers who exhibit cultural competence know about
their own family, social, and cultural backgrounds?
b) What are their experiences with diversity?
c) What are teachers perceptions of the relationships between their lived
experiences and their success with diverse students?
After my data collection and data analysis, I found that the direction of my
focus questions changed slightly to a more specific direction. The first question was
really about childhood experiences and the ways in which teachers beliefs, values,
and attitudes initially developed. In the second question, teachers talked about their
experiences with and exposure to diversity. These experiences ranged from
childhood into adulthood and included a wide range of contextsthe socio-cultural
context of their own family (and extended family) backgrounds; their exposure to
diversity via where they lived and traveled; their interactions with other people; their
educational experiences; and their current situation in their families and careers. The
third question required teachers to articulate their perceptions, views, and
interpretations of their own lived experiences and the ways in which they relate to
their success with diverse students.
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This study revealed much about the personal beliefs, values, and experiences
.of teachers who have been identified as highly effective and who demonstrated
varying degrees of cultural competence. One of the challenges of studying lived
experience was that reported experiences provide only reinterpretations of moments
in time as individuals continually changed with the passing of time (Clarke, 2003).
The data from this study came from individual perspectives on a particular day and
time; the teachers in this study were self reporting about their versions of their
memories and interpretations of their experiences. These memories, in retrospect, can
be a tool for understanding current beliefs, attitudes, and values that have been shaped
over time, and potentially guide an individuals actions in the present.
In this study, I found that teachers who were identified as highly effective
came with all sorts of lived experiences. Variation existed within ethnicity, within
neighborhoods, and within extended and immediate families. Clarke, Davis, Rhodes,
& DeLott Baker (1996) also found that successful teachers can look very different
from one another. The lived experiences of the six teachers identified for this study
provided valuable insights into similarities and differences in the classroom practices
and life stories of teachers who are exceptional in their buildings.
Through analysis of the data, I found significant similarities among the lived
experiences of these teachers; for example, they all described themselves and their
lives as ordinary and came from loving and supportive families. All six teachers
identified experiences in their upbringing that influenced and shaped their beliefs and
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attitudes as teachers. For example, they all developed a strong work ethic based on
their families values. These teachers had varied experiences with diversity through
travel, education, and experiences working with diverse students. Furthermore, these
teachers demonstrated classroom behaviors that were consistent with literature on
highly effective teachers. This was typified by their classroom environments in
which teachers had positive attitudes about their students and students abilities to be
successful and held high expectations for all students. Surprisingly, however, these
teachers demonstrated a range of culturally responsive behaviors in the classroom and
attitudes considered in the literature to be culturally competent.
What do these findings mean in the context of illuminating the role of
teachers lived experiences in promoting culturally relevant teaching practices? In
this chapter, I discuss the lessons learned and attempt to make sense of the findings
in relation to the evolving focus questions. Finally, I identify key conclusions that
emerged from the study, the limitations of the study, and areas for further research.
Lessons Learned: Addressing Findings and Assumptions
Prior to the start of this study, I held several assumptions; some of these
assumptions were supported and some were not by the outcomes of the data analysis.
Here, I address the assumptions and consider the possible meanings of the studys
findings. The lived experiences of the teachers in this study shed light on the beliefs,
values, and attitudes of teachers who are particularly effective with diverse students.
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Furthermore, based on the findings from the classroom observations and semi-
structured interviews, I draw conclusions about these teachers lived experiences,
their experiences with diversity, their perceptions of lived experiences, and their
levels of cultural competence.
Teachers Lived Experiences
Prior to the start of this study, I assumed that the identified teachers would be
White women, as this group constitutes the majority of people who serve as teachers
in the current K-12 educational system (Capella-Santana, 2003; Thomas, 1994). The
nomination process, however, revealed a variety of teachers with diverse
backgrounds. Three of the teachers in this study were White women, two teachers
were from Mexico, and one was a White male. Dee (2000) found that teachers from
diverse backgrounds positively impacted the achievement of diverse students, and
Irvine (2003) argued that students not only benefited from having teachers with
cultural backgrounds similar to their own but also because these teachers bring
diverse perspectives and strengths to their teaching. However, the data from the
present study demonstrated that there were teachers in both buildings who shared the
Hispanic heritage of many of their students and yet were not nominated as highly
effective teachers.
From the data analysis, it appeared that highly effective teachers success with
diverse students could not be attributed solely to shared culture or ethnicity.
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Predictors of successful teaching may be more complex. As Powell (1996) found, the
present study suggests that teachers do not necessarily have to share ethnicity or
culture with their students in order to be effective. Lived experiences, teacher
dispositions, and teacher training may connect teachers and students more so than
ethnicity.
The interview data revealed details about teachers early lives and the ways in
which their beliefs and values developed. Despite the diversity among the teachers,
most shared characteristics with their Hispanic students that enabled them to make
strong connections in the classroom. Two teachers shared Hispanic ethnicity with
their students, and two White teachers each married a person who is Hispanic.
Furthermore, five out of the six teachers were Catholic. The non-Catholic teacher
shared the least with her students as a White, Protestant woman from another part of
the country. Further examination of the interview data, however, revealed similarities
among the backgrounds of these teacher, beyond what we might identify as
demographic categories: all six teachers described themselves as having been raised
in caring and supportive middle class families; they valued a strong work ethic;, they
were raised with a discipline style that was based on personal responsibility and high
expectations; and they had positive attitudes that all students could be successful.
These shared perspectives reflect shared values. Additionally, all of these
teachers reported having positive experiences in school when they themselves were
students. In their roles as teachers, they identified their core beliefs as a commitment
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to their students, a strong work ethic, high expectations for student work, and the
positive belief that all children can be successful. These beliefs and values are
consistent with the findings in the literature on teachers who are effective with
diverse students (Clarke, 2003; Kozol, 1991; McCallister & Irvine, 2002; Nieto,
2005; Powell, 1996).
Gutierrez and Rogoff (2003) would argue that a cultural-historical approach
accounts for individual past experiences as key factors in teaching and learning.
From this viewpoint, students and teachers cultures are not static traits; on the
contrary, individuals participation in cultural practices provides various sites of
intersections, based on shared experiences, between these teachers and their
studentsareas in which they share commonalitiesthat extend beyond ethnicity
and religion. The current views of these teachers as they perceived themselves as
educators reflected many of the values held by their parents and/or extended family
members. Possibly, their ability to be successful with diverse students stems from the
development of their core beliefs developed since childhood based on participation in
socio-cultural practices (Gutierrez & Rogoff, 2003).
These six teachers connected with students at various intersections of shared
experience and participation in socio-cultural contexts. For example, five of these
teachers were Catholicthey shared participation in this cultural context and value
system with their students. The teacher who was least like the others, and seemingly
shared very little with her students, grew up in a family that held high expectations,
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had an authoritarian approach to discipline that was based on respect and strict rules
of conduct, and held strong religious beliefs This may be an intersection of shared
experiences that she has with her students, in spite of the different context in which
she participated in these similar experiences. Additionally, one teacher articulated
that just because she is from Mexico, like many of her students, she and her students
may not have much in common because her students may come from very different
geographic, social, cultural, economic, and family backgrounds. Both intersections
and divergences arise.
Further study is warranted to understand the ways in which teachers who
differ considerably from their students can still be highly effective and demonstrate
cultural competence; similarly, further study within a cultural-historical frame would
reveal details about why some teachers who share demographics and backgrounds
with their students are not highly effective. Gutierrez and Rogoff (2003) argued that,
researchers thus need understanding of the relationship
between a communitys practices and the routine practices
in which an individual participates. They would check their
assumptions about an individuals familiarity with the focal
practices as well as seek further information about whether and
how an individual might participate in the practice, (p. 23)
In essence, shared and divergent socio-cultural backgrounds and lived experiences
between teachers and students are only partial factors in student success.
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Teachers Experiences with Diversity
The teachers in this study demonstrated a range of experiences with diversity.
Unlike Powells (1996) study, in which the highly effective teachers did not have
specific training around diversity, most of the teachers in the present study identified
further trainingin the form of graduate school or professional development
opportunitiesas an influence in their ability to successfully work with diverse
students. Several teachers admitted to lacking experience with diversity, consistent
with the literature (Howard, 1999; Taylor & Sobel, 2001), although they all indicated
a willingness to tap into whatever resources were necessary to leam more about
diversity in order to support their students.
The teachers understandings of dealing with diversity were different. For
one teacher, multicultural meant introducing literature about Native Americans or
stories from Mexico. In other words, she introduced diversity into the classroom in a
narrow waythrough ethnicity. Another teacher, who was learning Spanish, talked
about how she struggled to become fluent, but continued in her dedication to
becoming better with the language. Two teachers, however, furthered their
knowledge through formal education, earning masters degrees in multicultural
education and .bilingual education. All of them, however, suggested that training
around diversity and instruction as an area in which they needed further support in
order to continue their success with diverse students.
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In addition to the range of experience these teachers had with diversity, they
demonstrated varying confidence levels in their ability to address diversity issues in
their classrooms. In one example, the male teacher was the least comfortable talking
about diversity. He viewed himself as a regular American and had negative
experiences traveling to Mexico. He acknowledged that he had little formal training
around issues of diversity, yet he was a highly effective and culturally competent
teacher with his students. While he indicated that diversity was not that important to
him, he did make strong connections with studentshe knew them, treated them with
respect, and had high expectations for school work and behavior. Perhaps he was
successful because of his beliefs about dealing with people. These beliefs, based on
his upbringing and family values, suggested how all people should be treated.
These findings suggest that an area for further study is the role of beliefs and
values in teaching diverse students. As one teacher suggested, being an effective
teacher with students from diverse backgrounds is more about personal values and
beliefs than it is about exposure to or preparation for working with diverse students.
This comment is evidenced in the studys findings. Exposure to diversity of learning
about it in coursework had less of an impact on these teachers than did their core
beliefs and values about diversity that the teachers in the study brought with them to
the classroom.
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Teachers Perceptions of Lived Experiences
Walling and Lewis (2000) argued that successful teachers must have a strong
identity in which they are confident, knowledgeable, and committed to improving
the profession (p. 63). Based on literature (Gay & Kirkland, 2003) and my own
suppositions, I assumed that the teachers for this study would have strong teacher
identities and self awareness of themselves as highly effective teachers. Four of the
teachers were not surprised when they were nominated as among the best teachers in
their building, based on positive reinforcement from their students, families, peers,
and school support staff, as well as their students achievement data. Two of these
teachers expressed surprise that they were nominated, yet upon further consideration,
acknowledged that they had positive relationships with students and families, and
their students were among the highest achieving in the building.
Interestingly, all six teachers viewed themselves as ordinary people rather
than extraordinary; but as highly effective teachers in their buildings, they did stand
out as exceptional. Overwhelmingly, however, these teachers described themselves
as normal. Although some of these teachers had few shared experiences with their
students (socially, culturally, linguistically, economically), they all expressed
empathythe ability to understand and relate to others experiences. They used
differences to help relate to and understand their students; they all recognized that
experiences are both unique and shared. In other words, they did not view
themselves as coming from a place completely different from their studentsthey are
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first and foremost people, working together to teach and learn. This trait is consistent
with the literature on effective teachers (McAllister & Irvine, 2002; Powell, 1996),
which suggests that teachers who are effective with diverse students strive to
understand and empathize with all of their students.
The teachers in this study were somewhat analytical about their teaching
practice (Danielwicz, 2001; Gay, 2000; Ladson-Billings, 2001), they provided details
about their lived experiences, and they were able to talk to some extent about the
connections between these experiences and their success with diverse students.
During the interviews, I encountered several moments when teachers made
significant realizationsthat their beliefs and values, influenced by their families and
surroundings and shaped over time, did in fact relate to the ways in which they
approached teaching and interacted with students. This led me to believe that
teachers may or may not able to readily articulate the meaning of their early
experiences and identify the ways in which those experiences connectconsciously
or unconsciouslyto their current work with students, without specific prompts to
promote such reflection. The role of such prompts in professional development is a
potential area for further research.
Elements of Teacher Effectiveness and Cultural Competence
Going into this study, I assumed that highly effective teachers would also be
culturally competent; I found from the results of the study that the two do not always
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go together. Furthermore, my analysis of the data revealed a range of culturally
competent behaviors. I determined that half of the teachers in this study
demonstrated frequent culturally competent behaviors, while three demonstrated
intermittent behaviors, according to how the observation and interview data fell into
my criteria for cultural competence.
The nomination process yielded teachers who were highly effective in
classrooms. My definition of highly effective stemmed from both the literature and
state standards for successful classrooms. I attempted to provide a definition of
highly effective that considered the perspective of school administrators, whose
view generally translated to high achieving according to state achievement scores,
and families, whose view of highly effective teachers might have to do with a
teachers relationship with students in supporting successful learning rather than
achievement scores.
The teachers classroom practices, behaviors, and interactions with students
were consistent with the literature on effective teaching practices. They created
learning environments that encouraged positive classroom connections (Sobel &
Taylor, 2003). Most of them valued diversity (Gay, 2000), attended to multiple
intelligences (Gardner, 1993), provided differentiated instruction in order to meet the
needs of all students (Gregory, 2002; Tomlinson, 1999), and aligned lessons to state
standards. Their classroom processes and expectations for behavior were consistent,
yet most of them demonstrated flexibility in their teaching practices. They ran
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organized classrooms, but allowed students choice in their learning. Overall, these
teachers interactions with students were positive, their students were happy to be
there, and teachers treated students respectfully. In the context of this study and the
definition of highly effective, all of these teachers had high performing classrooms
in schools that were overall low performing; unlike most of their peers, they were
able to display progress in their students achievement.
While these teachers were all identified as highly effective, however, they
demonstrated a range of culturally competence. Overall, based on the data collected
for this study, these teachers brought positive attitudes about their students to the
classroom and empathized with them even when they could not personally relate to
their students circumstances; these findings are consistent with the literature on
cultural competence (Delpit, 1995; Gay, 2000; Ladson-Billings, 1992, 2000, 2001).
The teachers in this study knew what their students needed, they encouraged students
to learn about themselves, and they brought their own experiences into the classroom
when appropriate (Villegas & Lucas, 2002). The teachers provided lessons that
connected to students prior knowledge, or related to their interests, or attended to
multiple intelligencessuch as hands on, tactile learning opportunities. A key
finding in this study, consistent with Powells (1996) findings, was that although
these teachers have the highest achieving classrooms, they did not focus solely, or
even mainly, on student achievement. Instead, their main goal was making strong
200


connections with students by creating safe environments in which students have -
positive learning experiences.
In general, these teachers demonstrated culturally competent classroom
management practices that allowed students to self-regulate their behavior (Freiberg,
1999). Classroom expectations were not based on fear of punishment or reward but
on personal responsibility (McCaslin & Good, 1998; Weinstein, et. al, 2004). While
children had choices to regulate their own behavior, these teachers styles
significantly differed from each other. The three strongest teachers, based on my
analysis of the classroom observations and interview data, had established behavioral
norms in their classrooms that required little intervention. They demonstrated
consistently strong cultural competence in their management of classroom behavior,
in providing culturally relevant lessons, and in their interactions with students.
Three of the teachers demonstrated varying degrees of cultural competence.
Some of their lessons were workbook heavy or required students to work for long
periods with the teacher leading the class. This type of coursework attended to the
needs of only some of the students. For example, students who can quickly complete
a task must sit and wait until the entire class finishes. Furthermore, some aspects of
the classroom management systems of these teachers were in contrast to the literature
on cultural competence. For example, two of them relied heavily on a system of
behavior management designed to reward and punish studentssuch as turning
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cards when behavior was inappropriate, point systems for both positive and negative
group behavior, and rewards (treats or classroom money) for appropriate behavior.
These teachers were still able to run effective classrooms in which their
students excelled; however, these teachers behavior management systems conflicted
with the literature on culturally competent behavior management that resists reward
and punishment models. One reason for their success with classroom management
might be that their behavior management systems were aligned with students
expectations or family parenting styles; teachers and students may share views that
this type of management is socially and culturally expected in the classroom.
These teachers systems may have been based on those taught in their teacher
preparation programs. Furthermore, their actions could be the result of a school-wide
policy regarding behavior based on the principals management style. To shed light
on this issue, future research would require in-depth knowledge about the beliefs and
values of highly effective teachers and their students regarding culturally relevant
classroom management, as well as the schools culture, and information regarding
teachers pre-service training.
Another major finding of the present study was that teachers who have been
identified as highly effective and culturally competent have various strengths and
weaknesses. They are not highly effective in the same way or all the time. Their
levels of cultural competence varied considerably. For example, one teacher
provided students with wonderful, hands-on lessons that attended to the diverse needs
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in the classroom. This same teacher, however, did not always express positive
attitudes about her students. She was identified as being highly effective, but the
evidence was less clear as to her cultural competence. In the next section, I discuss
the anomalous findings from this study.
Dealing with Anomalous Findings: Expanding the View of Cultural Competence
The outcomes of this study in relation to predictable descriptors and behaviors
of highly effective and culturally competent teachers were mixed. Three of the
teachers demonstrated both high achieving'classrooms and culturally competent
behaviors. The other three teachers also had high achieving classrooms, but based on
interviews and classroom observations, they demonstrated varied levels of cultural
competence. I must consider that based on the nominations, these were the best
teachers in these two buildings. This means that its possible that particular buildings
may not have any teachers who exhibit cultural competence, and some buildings may
have teachers who are culturally competent, but who do not have high achieving
classrooms. The two identifiershighly effective and culturally competentdo not
have to exist together, and furthermore, can emerge as characteristics in teachers in a
variety of ways and degrees.
Another outcome of this study was that one teacher differed significantly from
the other highly effective teachers and exhibited behaviors that were inconsistent with
the literature on cultural competence. Her background was unlike those of her
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students and the other teachers in this study. She came from a different region of the
country; her familys religious, social, and political beliefs differed from the Catholic
backgrounds and moderate to liberal views of the other five teachers in the study.
She had also been in classrooms the longest and was more than twenty years older
than the next oldest teacher in the study.
This teacher ran a well-ordered classroom; her students worked hard, and she
had high expectations for them. In general, her students were well-behaved, and the
lessons she provided addressed the needs and pace of all of her students. In other
areas, however, she did not demonstrate culturally competent behaviors. She did not
always exhibit positive attitudes about her students. Sometimes, when a student
asked a question or told a story, this teacher voiced a biased opinion. Visitors
observing her classroom were clear about which students she found irritating, and she
did not mask her frustration even though I was present in her classroom for the
observations.
According to the literature, this teacher may be not viewed as culturally
competent. Despite these contradictions, however, her students were academically
successful. She was the nomination choice of the principal and the most highly
nominated teacher in her building by the families. The data collection and analysis
indicated that this teacher fits the criteria of a highly effective teacher. Perhaps she
does offer culturally relevant teaching strategies according to the expectations of
families and the principal. They may view a hierarchical, authoritarian style as a
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characteristic of a culturally competent teacher. Her students do not exhibit behavior
problems in the classroom. Families could view these attributes as those of a highly
effective and culturally competent teachersomeone who is traditional, is tough on
kids, and gets results. These findings contradict those in the literature on culturally
relevant pedagogy. Further studies on culturally competent behaviors in the
classroom from the perspective of familiesand their culture, attitudes, and
expectationsmay serve to re-examine and re-define what it means to be a culturally
competent teacher.
Researchers referred to culturally responsive as the ability to recognize,
acknowledge, and understand that people come from different cultural backgrounds
with varied norms, values, beliefs, and practices (Gay, 2000; Delpit, 1995; Ladson-
Billings, 1994; Villegas & Lucas, 2002). Weinstein, Tomlinson-Clarke, and Curran
(2004) argued that culturally competent teachers must know their students
backgrounds. The teachers in this study expressed confidence in knowing and
understanding their students. The two teachers from Mexico, in particular, pointed
out that shared ethnicity or nationality with their students was not the only reason for
this confidence. Instead, both of them talked about their experiences living in various
parts of Mexico and pointed to the social and economic diversity in their native
country. They felt that knowing students families and confidence in their ability to
use diverse teaching practices helped them become confident around issues of
diversity among people.
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Furthermore, these teachers explained that their comfort with diversity
increased the more they learned about their students and had actual experiences in
classrooms with students who were different from themselves. Gutierrez and Rogoff
(2003) would argue that it is their participation in cultural practices (p. 21) of
teaching diverse students that continue to shape their identities as teachers who are
confident in addressing issues of diversity in the classroom. Based on the findings of
this study, determining the relationship between lived experiences and cultural
competence may be difficult, especially since experiences, beliefs, values, and pre-
dispositions are intricately woven together. However, based on the teachers in this
study, lived experiences and interpretations of those experiences shape interactions
with students and beliefs about teaching.
New research has identified particular teacher dispositions, or personal
attributes, as key characteristics in excellent teachers who are successful with diverse
students (Cline & Necochea, 2006; Helm, 2006; Talbert-Johnson 2006). In Helms
(2006) discussion of assessing teacher dispositions, she identified several dispositions
of excellent teachers:
kindness, caring, having high expectations for students and
themselves, teaching students to think critically, having a
strong work ethic, and to be aware of, and having an appreciation
of cultural diversity, (p. 237)
These attributes are similar to the characteristics identified in the literature on cultural
competence. Furthermore, Talbert-Johnson (2006) identified a growing body of
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research that links teacher effectiveness with teacher dispositions (Delpit, 1995;
Irvine, 2002). Collier (2005) argued that teachers dispositions are integral to specific
belief systems. Talbert-Johnson (2006) pointed out that some researchers believe
that the personal belief systems of teachers significantly influence the behaviors
displayed in the classroom and the instructional decisions teachers make (152).
Reflection upon lived experiences may be a site of data collection for exploring the
development of teachers expressed beliefs and values, particularly in the context of
classrooms, and the influence these beliefs, values, and related dispositions have on
their teaching.
Research on dispositions raises questions. In what ways do dispositions differ
from cultural competence? Is it possible to be a highly effective teacher without
specific dispositions? What if a teacher exhibited only some dispositions? Can these
dispositions be learned? Further research in the area of dispositions is needed in
order to determine the ways they differ from cultural competence and the ways they
can be identified, taught, or introduced into teacher preparation and assessment.
Finally, I found that teachers who are identified as highly effective (and who
demonstrate cultural competence) may have teaching styles and behaviors that are
viewed as culturally competent by the school community but contradict the literature
on culturally competence. The definition of cultural competence and the way it
appears in classrooms (and to what degree) requires further scrutiny.
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Limitations of the Study
First, the nomination process for this study had some limitations. Nominating
excellent teachers was not as easy or obvious as I assumed it would be. Nominations
from families and school staff were mostly aligned with each other. However, some
teachers who were nominated were not chosen for the study because they did not
have high performing classrooms according to state standards. Furthermore, after I
received permission to move forward with the study, I began identifying schools. I
was only able to secure two schools; the principals of the other elementary schools in
the district did not respond to my request. The outcome of the study might have
changed if my sample size were larger and more buildings had participated and
identified their best teachers.
Within the nomination process, the principal and the learning support staff,
like every other person, had varied views about what makes a good teacher. For
example, a principal may have nominated one teacher, while a learning support staff
member may have nominated another. For future studies, it would be wise to gather
more details about highly effective and culturally competent teachers by talking
directly to families, community members, and students about whom they consider to
be excellent teachers. This would also allow researchers to compare and contrast the
responses of families, community members, students, and school support staff to what
the literature identifies as teachers who are highly effective, culturally competent, and
who display necessary dispositions.
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Other limitations arose during data collection. First, I collected only two
types of data; future studies would'require case studies to include multiple sources
and triangulation of data (Yin, 2003). Another limitation arose with the classroom
observation tool. Although the tool provided broad areas to observe, including the
classroom environment, teaching practices, and interactions with students, the rating
system (1little to no competency observed; 2fair to adequate competency
observed; 3strong competency observed) was not specific enough to determine to
what degree teachers were or were not culturally competent. In the observation tool,
only some of the questions on the classroom observation tool provided the
opportunity to rate competency; other areas of inquiry asked the researcher to
describe and tally interactions, but these questions did not correspond to the rating
system.
Furthermore, a limitation of this study was the time spent with teachers.
Three hours of classroom observations by one observer within a month time period is
a mere snapshot of these teachers classrooms. An ethnographic approach in which
researchers spent substantial time in classrooms would help further identify both
culturally competent behaviors and dispositions of exceptional teachers.
Finally, the lived experiences of teachers were self reported in one interview.
Follow up questions or other means of data collection, such as focus groups or
journals might illuminate the subtleties of experience and the ways they either
directly or indirectly relate to effective teaching and success with diverse students.
J
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Additionally, given the interpretive requirements of this type of study, the use of
member checks to confirm or guide the researchers analysis of .the data and
depictions of the teachers participating in the study would be advisable.
Implications and Areas for Further Study
This study has implications for understanding the ways in which teachers are
prepared for diverse classrooms and for professional development for established
teachers who seek to be successful with diverse students. Furthermore, the highly
effective teachers whose lived experiences were detailed in these case studies provide
role models for all teachers, but particularly for those, working with diverse students,
in urban teachers.
From the studys data, I found that lived experiences do influence the ways in
which teachers view themselves and their studentsincluding their beliefs and values
about children from diverse backgrounds and their views on the best ways to teach
children. I also found that teachers may need support in identifying and articulating
the ways in which their lived experiences impact their performance in the classroom
in order to improve their teaching and build stronger teacher identities. The value of
prompting reflection on lived experiences to enhance teaching is an area for further
research. Furthermore, although this is a case study with a small sample, it suggests
that because lived experiences may impact teachers work with all students, they
should be considered in both preparing teachers and in providing professional
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development for future teachers. Given the current demographics on teachers
shortages and teacher burnout (Boser, 2000), supporting teachers understandings of
themselves and their values may alter the outcome of their teaching careers.
In this study, teachers discussed their lived experiences and identified the a-
ha moments where they realized the connection of something in their past
experience that influenced something in their teaching lives. Asking teachers to
scrutinize their core beliefs as they developed from childhood to the present may help
them understand their views towards students and the ways in which they carry out
their teaching practices. In spite of the fact that the six cases in this study came from
caring and relatively stable, if not always traditional, families, given the studys other
findings, it is also possible that effective teachers come to teaching with negative
experiences and attitudes that may have changed over time. Several questions arose
that lead to areas for further study: how might lived experience impact teachers
choices in instructional strategies, behavior management, and student interactions?
How much impact do teacher preparation programs have in shaping teachers beliefs,
values, and attitudes about students from diverse backgrounds? What is the impact of
lived experiences on teachers attitudes about students from diverse backgrounds?
Researchers and educational scholars provide recommendations on cultural
competence, culturally responsive teaching, culturally relevance, and diversity
responsive teaching. But what do these terms really mean, and how are they
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developed and practiced in classrooms? What is their relationship to best practices
and teacher quality?
The studys findings have implications for supporting the development of pre-
service teachers identity development, particularly around their confidence and
efficacy in working with diverse students. As pre-service teachers move into the
profession, they must integrate new environments into their experience, and in the
process, create an identity as full practitioners within the field. Britzman (1991)
called this transformation a time of biographical crisis for pre-service teachers
whose past, present, and future[s] are set in dynamic tension. Learning to teach
like teaching itselfis always the process of becoming: a time of formation and
transformation, of scrutiny into what one is doing, and who one can become
(p. 8). Teachers who consider their lived experiences in shaping their beliefs and
attitudes, for example, may be better able to identify their own biases and
assumptions. Knowles (1992) argued that identity is impacted by biography, prior
experiences with teachers and schools, and preconceived images of self-as-teacher.
The findings of this study support this notion that lived experiences impact the
identity of teachers.
Furthermore, teacher preparation programs must strive to promote and
enhance positive pre-service teachers self-images (Clark & Flores, 2001) and
encourage them to reflect on their cultural identity and develop critical consciousness
early in their training (Genor, 2002). Teacher preparation programs could offer
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courses, workshops, and mentoring programs that would allow teacher candidates the
opportunity to uncover and reflect on their lived experiences, their assumptions and .
biases, and their teacher identities. Pre-service teachers need to not only be aware of
the role of culture and diversity in the classroom, but also need to confront bias and
create an anti-bias curriculum before entering classrooms full time (Williams &
Okintunde, 2002). Again, lived experiences may be vital in uncovering the biases
teachers bring with them to their professions.
Antonek, McCormick, and Donato (1997) examined portfolios of pre-service
teachers in order to identify portfolios as a tool for pre-service teachers to reflect on
themselves, their growth as teachers, and relationships to thoughtful practice. The
researchers, in a collective case study (p. 19), examined two foreign language ,
student teachers portfolios as evidence of the depth of reflection that developed over
time. The two very different student portfolios emphasized various elementsone
pre-service teachers portfolio focused on his students and concrete practices, while
the other student teacher focused on herself and considered her own actions and self-
assessment. In analyzing the types and content of portfolio entries, the researchers
found that portfolio reflections show the uniqueness and complexity of forming the
self as a teacher and the differences that emerge when action and belief are untied in
reflective practice (p. 24). Personal history and self-reflection can bring
understanding to pre-service teachers who are struggling with their own identities.
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Conclusion
This study investigated relationships between the lived experiences of highly
effective teachers who were identified as being culturally competent and their
success with diverse students. The focus questions for this study were: (a) What do
effective teachers who exhibit cultural competence know about their own family,
social, and cultural backgrounds? (b) What are their experiences with diversity? (c)
What are teachers perceptions of the relationships between their lived experiences
and their success with diverse students? Specifically, this study sought to identify
various characteristics of effective teachers lived experiences that contribute to their
cultural competence in working with diverse students; and uncover teachers attitudes
about the relationship between those experiences and their success with diverse
students.
The major components of data collection included an extensive nomination
process of teachers who fit the criteria of the study, identification of critical cases,
classroom observations, and semi-structured interviews. Teachers described details
about their childhood experiences, family beliefs and values, core beliefs and roles as
educators, and perceptions of self as highly effective. Classroom observations
revealed teachers classroom environments, teaching practices, and interactions with
students. Through this study, I found that in these critical cases, teachers identified as
highly effective, who are particularly successful with diverse students,
demonstrated varying degrees of cultural competence. These teachers demonstrated
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flexibility in their practices and focused their efforts on student relationships rather
than on student achievement. Although their experiences with diversity varied, all of
these teachers indicated a willingness to learn more about diversity in order to better
meet the needs of diverse students. Finally, for these teachers, success with diverse
students developed more from their personal values, beliefs, and attitudes than from
exposure to or preparation for working with diversity.
Based on my analysis of the data for this study, teachers who share relatively
little with their students may still be deemed highly effective according to the
definition of effective teaching in this study. Furthermore, teachers whose ethnicity is
similar to their students may not share similar cultural experiences, histories, or
beliefs. Finally, teachers can vary in terms of the degree to which they demonstrate
culturally competent behaviors and yet still be considered highly effective." The
findings of this study revealed strong connections between lived experience, teacher
identity, and classroom effectiveness. Further study of the lived experiences of both
pre-service teachers and professional teachers can support teacher preparation
programs as they strive to support teachers development as highly effective and
educators who are successful with all students.
215


APPENDIX A
FAMILY NOMINATION FORM IN ENGLISH AND SPANISH
Dear Family Member: I am conducting a study to better understand excellent teachers in your school district, and
I need your help. Please write down the names of teachers in your school who you think are excellent and who:
Know students families and communities.
Value and honor students cultures and backgrounds.
Have positive attitudes towards all children.
Challenge students with high expectations for learning.
Provide schoolwork that reflects the diverse backgrounds of students.
Teachers name:______________________________
Teachers name:_______________________________________________
Thank you! Your opinion is valuable! If you have questions, please contact me: Kim Kennedy White, 303-748-
9081
Querido Miembro de Familia: Me estoy condusiendo para el major entendimiento de exelentes maestros en su
distrito escolar, y nesesito su ayuda. Porfavor escribaabajo los nombres de los maestros en su escuela quien usted
piensa son exelentes.
Conosca Estudiantes, Familias y Comunidades.
Value y honore Estudiantes, Culturas y pasados.
Tenga una actitud positiva hasia los ninos.
Poner Estudiantes a prueba con grandes expectaciones para aprender.
Proveher trabajo de escuela que refleje el diverso pasado de los Estudiantes.
Nombre del profesor:_______________________________________________
Nombre del profesor:_______________________________________________
Grasias! Su opinion es muy valioso! Si tiene alguna pregunta, porfabor contacte me: Kim Kennedy White, 303-
748-9081
216


APPENDIX B
INFORMED CONSENT: CLASSROOM OBSERVATIONS
Spring 2006
Dear Teacher:
I am conducting classroom observations as part of my dissertation that explores relationships between
the lived experiences of highly effective teachers and their success with diverse students. I am inviting
you to participate in this study as you have been identified in your building as a teacher who is
particularly effective in working with students with diverse backgrounds and needs. I will visit your
classroom three to five times during the month of February.
Your participation in this study poses minimal risks; your relationships with your peers may be altered;
other teachers may treat you differently if they know you have been identified as a teacher who is
highly effective. In order to reduce the chances of this happening, I will not communicate with the
teachers and staff in your building about your participation in this study, and I have asked your
schools principal to refrain from sharing the information about the study to others.
Your active participation is extremely valuable and provides several potential benefits. The studys
findings have implications for: 1) preparing teachers, particularly around working with diverse
students; 2) supporting the professional development of urban teachers; and 3) understanding the
relationships between home and school cultures.
Your participation in this research is completely voluntary and may be terminated at any time. When I
analyze the data and report the results in my dissertation, your individual responses and personal
information will be kept confidential. I will assign to you a pseudonym that will be known only to you
and me.
If you have questions, comments, or concerns about the classroom observations, please contact me at
303-748-9081. For questions regarding your rights as a research subject, you may contact Dorothy
Yates at 303-556-4060. Thank you again for your contribution to this important study.
Sincerely,
Kim Kennedy White
Doctoral Candidate
University of Colorado at Denver and Health Sciences Center
NAME DATE
217


APPENDIX C
CLASSROOM OBSERVATION TOOL
Teacher:
School:
Grade:
Date:
1. Describe the environmental print displayed about the room that demonstrates a
valuing of diversity (e.g., visual supports, posters, banners, etc.).
2. Describe grouping strategies that enhance student achievement and promote non-
like group interaction (e.g., ability level, gender, etc.).
3. Sketch the room with attention to the instructional arrangements. What
conclusions would you draw from this arrangement?
BACK ROOM
FRONT OF ROOM
4. Describe specific instructional materials that illustrate valuing and promoting the
understanding of diversity factors (e.g., multicultural literature, manipulatives).
218


5. How is the teacher adapting the lesson for individual students (e.g., differentiating
instruction regarding diversity factors across content, delivery, or evaluation)?
Student (Identified by name or clothing, e.g., color of shirt) Explicit illustration that reflects a valuing of diversity factors




Rate each item with the scale: 1little to no competency observed; 2fair to
adequate competency observed; 3strong competency observed.
6. Demonstrates appropriately needed distribution of attention to all students.
Teacher attends to students in a manner that demonstrates respect for students
diverse abilities and experiences.
1 2 3 Comments:
7.. The teacher ensures that all students understand and can carry out the procedures
for instructional activities.
12 3 Comments:
8. The teacher makes instructional content relevant, linked to students practical
experiences, attends to learning styles, multiple modes of delivery, and checks for
understanding.
1 2 3 Comments:
...
...... -
SyS tra fcfiI yt t?i ft tft fyaft Tyyft mi n n fl*
1. Works well with and treats with dignity and respect all individuals regardless of
race, ethnicity, ability, language, gender, sexual orientation, age, or religion.
Tally the specific teacher comments and interact ions directec towards each student.
Student Praise Question Feedback Direction Giving Redirection Other


219


2. Describe the types of student-to-student and student-to-teacher interactions.
3. What does the teacher do to encourage social and intellectual interactions and
promote meaningful relationships to develop across diverse groups in the
classroom?
Rate each item with the scale: 1little to no competency observed; 2fair to
adequate competency observed; 3strong competency observed.
4. Establishes and maintains consistent positive standards for classroom behavior
that are equitable for all students. The teacher demonstrates the ability to change
and adapt his or her classroom plan after reflecting on changing student and
classroom needs.
1 2 3 Comments:
5. Makes the physical and psychological environment safe and conducive to learning.
The teacher uses the physical and psychological environment as a resource to
facilitate learning. Provisions are made to accommodate all students.
12 3 Comments:
* Adapted from Sobel, D.M, Taylor, S.V., & Anderson, R.E. (2003). Shared
accountability: Encouraging diversity responsive teaching in inclusive contexts.
Teaching Exceptional Children, 35(6), 46-54.
220


APPENDIX D
INFORMED CONSENT: INTERVIEWS
May 2006
Dear Teacher:
I am conducting interviews for my dissertation that explores relationships between the lived experiences of
excellent teachers and their success with diverse students. I am inviting you to participate in this study as you
have been identified in your building as a teacher who is particularly effective in working with students with
diverse backgrounds and needs.
In this part of my study, I will conduct a phone interview with you (60-75 minutes). You will be asked a number
of prompts regarding: 1) your family, social, and cultural background; 2) your personal experiences with diversity;
and 3) your beliefs and attitudes about the impact of these experiences on your success with diverse students.
Questions about your life experiences may be difficult to answer or may cause discomfort and anxiety.
Furthennore, your relationships with your peers may be altered; other teachers may treat you differently if they
know you have been identified as a teacher who is successful with your students and who engages in highly
effective teaching practices. In order to reduce the chances of this happening, I will not communicate with the
teachers and staff in your building about your participation in this study, and I have asked your school's principal
to refrain from sharing the information about the study to others.
Your active participation is extremely valuable and provides several potential benefits. The study may help you to
better understand the relationships between your lived experiences and your work with diverse students.
Furthennore, this knowledge and understanding may positively impact your classroom work. The studys findings
have implications for: 1) preparing teachers, particularly around working with diverse students; 2) supporting the
professional development of urban teachers; and 3) understanding the relationships between-home and school
cultures.
All interviews will be audio taped via a phone tape recorder. You will receive a $20 gift certificate for your
participation. Your participation in this research is completely voluntary and may be terminated at any time.
When 1 analyze the data and report the results in my dissertation, your individual responses and personal
information will be kept confidential. I will assign to you a pseudonym that will be known only to you and me;
although I will be using your direct quotes in my dissertation, the quotes will be connected only to your
pseudonym. The interviews will take place at non-school locations.
If you have questions, comments, or concerns about the interviews, please contact me at 303-748-9081. For
questions regarding your rights as a research subject, you may contact Dorothy Yates at 303-556-4060. Thank
you again for your contribution to this important study.
Sincerely,
Kim Kennedy White/Doctoral Candidate
University of Colorado at Denver and Health Sciences Center
221


APPENDIX E
INTERVIEW PROTOCOL
Kim Kennedy White/ Exploring Relationships between the lived experiences of Teachers who are
culturally competent and Their Success with Diverse Students
Sample Interview Promptsfollow up questions to these prompts will vary based on participant
feedback and conversations as these are open-ended interviews.
Research Question Sample Interview Prompts
What do highly effective urban teachers know about their family, social, and cultural backgrounds? Tell me about your familys social and cultural background. Where did you grow up? With whom did you live? Do you have siblings? What kind of work did your parents/caretakers do? What economic situation was your family in? Describe the neighborhoods in which you lived. What hobbies, interests, and talents did you have? In what did you or your family members participate? What did your family believe, value, and teach about people who were different from you (socially and culturally)? Describe your familys reaction to bringing home a friend whose cultural/ethnic/social background differed from your own. Describe your familys political views. What did your family believe about education? Describe your familys educational experiences. What was discipline like in your family? What were your familys beliefs about how children/people should behave? How do you identify yourself ethnically/culturally? Did your family participate in an organized religion? In what celebrations or practices did your family participate? What languages were spoken in your home? In your current life, do you continue to align yourself closely to the values your family had when you were growing up, or did you alter these values?
What are their experiences with diversity? How do you define diversity? What experiences have you had with diversity (travel, family, or friends)? What formal training have you had around diversity and/or working with diverse students? What do you believe makes a family?
222


- How do you encourage students to develop a knowledge base and appreciation regarding their socio-cultural and economic backgrounds?
What are the relationships between highly effective urban teachers lived experiences and their success with diverse students? How do you think your background impacts your success with your students? In what ways do you use your life history to inform classroom practice? Describe your role as a teacher in relation to your students. Describe your relationship with your students. Are your students diverse? In what ways? What are the benefits of working with diverse students? What are the disadvantages of working with diverse students? What are the similarities/differences between you and your students?
223


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ANALYSISOFBRIDGEHEALTHINDEX FOR THECITYANDCOUNTYOFDENVER, COLORADOby Xin Jiang M.S., College of Engineerin g and Applied Science, 2009 M.S., College of Engineering at Northeast Forestry University, 2006 B.S., College of Engineering atNortheast Forestry University, 2003 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Structure Engineering 2012

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ii This thesis for the Doctor of Philosophy degree by Xin Jiang has been approved for the Structure Engineering by Kevin L. Rens, Chair Kevin L. Rens, Advisor Chengyu Li Frederick Rutz Yunping Xi Ross B. Corotis Date:4-13-2012

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iii Jiang,Xin(Ph.D., Structure Engineering) Analysis of Bridge Health Index for the City and County of Denver, Colorado Thesis directed byProfessorKevin L. Rens. ABSTRACT The current Bridge Health Index (BHI) in th e Pontis BridgeManagement System applied to assess the health conditions for bridges located in the City and County of Denver (CCD) doesnot provide an accurateanalysis of the health condition of itsrelatively small bridgenetwork. The first stage ofthis study explores both the calculation results and the computing methodology of the current BHI. It wasconcluded that the current BHI is subjective to a municipalityÂ’s often imprecise cost data. Thefirst stage of this study developedan alternate diagnostic tool, th eDenverBridge Health Index(DBHI).Ithas already been adopted in thePontisBMS of th e CCD Public Works De partment. In order to utilize theDBHI to provide the CCD engineers valuable references in maintenance, repair, and rehabilita tion (MR&R) decisionmaking,the second stage of this study examinesboth the calculation results and the computing methodology of the DBHI. It was concluded that the current health index coefficients(ksand ks N) do not reflect actual deterioration levels of the c ondition states. The second stage of this study was to develop a historical inspection data basedmethodologyto determinethe actualks(ks J&R) coefficients for the current Pontis Bridge Management System. The form and content of this abstract are approved. I recommend its publication. Approved: Kevin L. Rens

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iv DEDICATION I dedicate thiswork to my wife, MingMa, for her love, support, friendship, and for her continuouslyencouraging me to achieve my success. To my two years oldboy, hewill be a joy and a blessing in my whole life.

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v ACKNOWLEDGMENTS I would like to thank Dr.Kevin L. Rens, chair of my thesis committee, for his knowledgeableguidance and support. Dr. Re ns gave methe excellent guidance throughout this thesis. I also would like to thank Dr.Chengyu Li,Dr. Frederick Rutz,Dr. Yunping Xi, and Dr.Ross B. Corotis for being committee members of my thesis. The CCD bridge management group led by James Barwick isacknowledged for several years of research support. Pers onnelfrom the CCD including James Hamblin, BretBanwart, and WilliamMelton provided va luable input on main tenance ma nagement ofCCD infrastructure throughout the years and areacknowledged.

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vi TABLE OF CONTENTS CHAPTER I.INTRODUCTION.......................................................................................................1 BridgeManagementHistory...................................................................................1 CurrentCCD Pontis BMS.......................................................................................4 II.BRIDGE HEALTH INDEX..........................................................................................9 CommonlyRecognized(CoRe) Elements..............................................................9 ConditionStates......................................................................................................9 BridgeHealthIndex..............................................................................................11 PontisBridgeHealthIndexMethodology............................................................12 Failure Cost-basedBHIÂ…...............................................................................14 RepairCost-basedBHI...................................................................................18 III.PROBLEMS................................................................................................................22 Problems with Computational Results..................................................................22 Problems in Computing Methodology..................................................................26 Element Valuein Pontis BHI Computation....................................................26 EffectsofElementValue on Pontis BHI........................................................29 Conclusion............................................................................................................30 IV.ANALYSIS................................................................................................................31 Weighting Point and Simplified BHI....................................................................31 Using Weighting Point Methodology in the Pontis BMS.....................................32 Conclusion............................................................................................................36 V.DENVER BRIDGE HEALTH INDEX.....................................................................38 Condition Index (CI) Zones..................................................................................38

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vii Nonlinear Health Index Coefficient ks N................................................................40 Weight Coefficient Adjustment Method...............................................................45 Denver Bridge Health Index.................................................................................48 Comparison between Pontis BHI and DBHI........................................................55 VI.ISSUE....................................................................................................................... .58 Background Knowledge........................................................................................59 Element Classification....................................................................................59 Dynamic Calculati on Report (DCR) ..............................................................60 Issue......................................................................................................................62 Sample BridgesÂ…...........................................................................................62 CCD Major and Minor Br idge Networks.......................................................68 Summary.........................................................................................................70 Analysis.................................................................................................................71 Mathematic Derivation...................................................................................71 Reason of Trend..............................................................................................74 Reason of Issue...............................................................................................76 Conclusion............................................................................................................80 VII.METHODOLOGY TO DETERMINE ACTUAL ksVALUE .................................81 Background Knowledge........................................................................................81 Element Inspection Data.................................................................................82 Element Transition Model..............................................................................85 Methodology.........................................................................................................86 Development of the Element Transition ModelÂ…..........................................86 Ideal Element Transiton Model......................................................................91 Methodology...................................................................................................95

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viii Conclusion..........................................................................................................101 VIII. METHODOLOGY IMPLEMENTATION AND ks J&R.........................................103 Background Knowledge......................................................................................103 Methodology Implementation.............................................................................104 Data Preparation............................................................................................105 Data Processing.............................................................................................109 Determination of the Average Initial Ages (Ts) for Every Element under Each Element Category...........................................................................112 Computation of the Ks J&Rfor Every Element under Each Element Category..................................................................................................114 Computation of the ks J&RforEach Element Category...........................115 Actual Health Index Coefficient ks J&R................................................................116 Generation of ks J&R.......................................................................................116 Application of ks J&R......................................................................................118 Comparison of ks, ks N, and ks J&R...................................................................120 IX.SUMMARY, CONCLUSION, AND RECOMMENDATIONS FOR FURTHER STUDIES........................................................................................................................ 124 Summary.............................................................................................................124 The First Stage..............................................................................................124 The Second Stage .........................................................................................127 Conclusion..........................................................................................................129 Recommendations For Further Studies...............................................................129 APPENDIXA.The 144 CoRe Elements in Pontis Bridge Inspection Coding Guide..131 APPENDIX B.Pontis BHI for 162 Major Bridges in CCD from 2000 to 2006..........137 APPENDIX C.DBHI for 162 Major Bridges in CCD from 2000 to 2006.................142

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ix APPENDIX D.The 350 Lowest-EHI Elements under Each Element Category in CCD Major Bridge Network....................................................................................................147 APPENDIX E.The 50 Lowest-EHI Elements under Each Element Category in CCD Minor Bridge Network....................................................................................................169 APPENDIX F.Methodology to Determine ks J&Rfor Elements with 3 CSs..................173 APPENDIX G.Methodology to Determine ks J&Rfor Elements with 5 CSs.................176 REFERENCES...............................................................................................................179

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x LIST OF TABLES Table II.1 Levels of deteriora tion of each CoRe element..........................................................10 II.2 CSs of steelopen girde r-painted(element key: 107)................................................11 II.3Health index coefficients ksof the CSs.....................................................................13 II.4Failure cost-based BHI for 10 major sample bridges................................................15 II.5Repair cost-based BHI for 10 major sample bridges................................................19 III.1 Distribution of Bridge Age for the Entire 615 CCD Bridges..................................22 III.2 Element Distribution for 8thAvenue Viaduct Br idge(D-03-V-150).......................27 III.3Total Element Value (TEV) for 8thAvenue Viaduct Bri dge (D-03-V-150)............28 III.4Current Element Value (CEV) for 8thAvenue Viaduct Brid ge (D-03-V-150)........29 IV.1Failure Cost-based and Repair Cost-based Element Weighting Point for 8thAvenue Viaduct Bridge (D-03-V-150)...........................................................................................33 IV.2Weight Coefficient-based Element Weighting Point for 8thAvenue Viaduct Bridge (D-03-V-150)................................................................................................................... .36 V.1Condition Index (CI) Scale.......................................................................................39 V.2Condition Index (CI) Zones......................................................................................39 V.3Linear Health Index Coefficients ks..........................................................................40 V.4EHI based on Linear ksfor 8thAvenue Viaduct Bridge (D-03-V-150)....................42 V.5Nonlinear Health Index Coefficients ks N..................................................................44 V.6EHI based on Nonlinear ks Nfor 8thAvenue Viaduct Bridge (D-03-V-150).............44 V.7Unadjusted Weight Coefficients for 8thAvenue Viaduct Bridge (D-03-V-150)......46 V.8 Weight Coefficient Adjustment Method for 8thAvenue Viaduct Bridge (D-03-V150)........................................................................................................................... ........48 V.9Element Distribution for 8th Ave nue Viaduct Bridge (D-03-V-150).......................50 V.10AdjustedWeight Coefficients for 8thAvenue Viaduct Brid ge (D-03-V-150)........51

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xi V.11 Calculation of DBHI for 8thAvenue Viaduct Bridge (D-03-V-150)......................51 V.12 DBHI for 10 Major Sample Bridges.......................................................................52 VI.1 Classification of 144 CoRe elements........................................................................59 VI.2 The 2008 elementinspection data for th e sample bridgeD-20-MB-785(Location: 56thAve. & W. Havana)..................................................................................................63 VI.3 The numerical calculation result fo r the sample bridge D-20-MB-785...................63 VI.4 The 2008 element inspection datafor th e sample bridgeD-03-V-180(Location: Evans over Sa nta Fe)........................................................................................................64 VI.5 The numerical calculation result fo r the sample bridge D-03-V-180.......................64 VI.6The 2006 element inspection datafor the sample bridgeF-16-DW(Location: I25 ML SBND)....................................................................................................................... .66 VI.7 The numerical calculationresu lt for the sample bridgeF-16-DW..........................66 VI.8Paired-samplesT test resultfrom SPSS program.....................................................67 VI.9 Element condition for bridge networks....................................................................68 VI.10 The numerical calculationresults for the major bridge network...........................69 VI.11 The numerical calculationresults for the minor bridge network............................69 VI DBHIbased on ks Nfor sample bridges................................75 VI DBHIbased on ks Nfor CCD major and minor bridge networks....................................................................................................................... .....75 VI.14 Average quantities in CS1 and all other CSs of total elements included in each element category for major and minor bridge networks...................................................77 VI.15 Average quantities in CS1 and all other CSs of damaged elements included in each element category for major and minor bridge networks...................................................78 VI.16 Linear and nonlinear health index coefficients ks(ks N)..........................................79 VI.17 Averages of intermediate ksand ks N.......................................................................79 VI.18 Differencebetween intermediate ksand ks N...........................................................79 VII.1 Element inspection data in the 2010 CCD Pontis BMS database...........................84 VII.2 Sample of element inspecti on data for the bridge railing........................................88

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xii VII.3Condition states of P/S concrete open girder(element key: 109)...........................96 VII.4 CDOT suggested CS scales for cracks and percentloss of bearing area in P/S concrete girder................................................................................................................ ..97 VIII.1 Element inspection data in the 2010 CCD bridge network..................................106 VIII.2 Data inaccuracies for sample bridges...................................................................107 VIII.3 Determination of the firs t ages for sample bridge................................................112 VIII.4 Computation of the Ks J&Rfor every element with 4 CSs.....................................115 VIII.5 Computation of the ks J&R.....................................................................................115 VIII.6 Data processing for elements with 5 CSs.............................................................116 VIII.7 Data processing for elements with 4 CSs.............................................................117 VIII.8 Data processing for elements with 3 CSs.............................................................117 VIII.9 Improved health index coefficients ks..................................................................118 VIII DBHIbased on ks J&Rfor sample bridges........................118 VIII DBHIbased on ks J&Rfor CCD major and minor bridge networks....................................................................................................................... ...119 VIII.12Linear health index coefficients ks.....................................................................121 VIII.13Nonlinear health index coefficients ks N.............................................................121 VIII.14Actualhealth index coefficients ks J&R................................................................121

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xiii LIST OF FIGURES Figure I.1 Flowchart of theBHI Analysis Procedure....................................................................8 II.1Distribution of the Entire 615 CCD Brid ges in 2006 based on Failure Cost-based BHI............................................................................................................................ ........16 II.2Distribution of 308 MajorCCD Bridges in 2006 based on Failure Cost-based BHI.17 II.3Distribution of 307 MinorCCD Bridges in 2006 based on Failure Cost-based BHI. ............................................................................................................................... ............17 II.4Distribution of the Entire 615 CCD Brid ges in 2006 based on Repair Cost-based BHI............................................................................................................................ ........19 II.5Distribution of 308 MajorCCD Bridges in 2006 based on RepairCost-based BHI.20 II.6Distribution of 307 MinorCCD Bridges in 2006 based on RepairCost-based BHI.21 III.1Photograph of the 8thAvenue Viaduct Brid ge (D-03-V-150).................................23 III.2Flexure Cracks at the Pier Cap.................................................................................24 III.3Buckle of the Web...................................................................................................24 III.4Broken Bearing Guides and Corrosion at the Pot Bearings.....................................24 V.1Trend of Health Index Coefficient of Condition States............................................41 V.2Comparison of Trends of Linear and Nonlinear Health Index Coefficient of Condition States............................................................................................................... .43 V.3A Linear Step Curve forCalaulating the Adjustment Factor in Weight Coefficient Adjustment Method..........................................................................................................47 V.4Distribution of the Entire 615 CCD Bridges in 2006 based on DBHI......................53 V.5Distribution of 308 Major CCD Bridges in 2006 based on DBHI............................54 V.6Distribution of 307Minor CCD Bridges in 2006 based on DBHI...........................54 V.7Distribution of the Entire 615 CCD Brid ges in 2006 based on Failure Cost-based BHI, Repair Cost-based BHI, and DBHI..........................................................................55 V.8Trend of BHI Related to the Age of Bridge..............................................................56

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xiv VI.1Dynamic Calcul ationReport....................................................................................61 VII.1 2010 CCD Pontis BMS data baseschematic diagram.............................................83 VII.2 Age range of elem ent inspection data.....................................................................89 VII.3 Distributions of historical bridge/e lement inspections in 2011 CCD Pontis BMS based on the age............................................................................................................... .91 VII.4 The ideal transition model of element with 4 CSs..................................................92 VII.5 Element conditions at t1(0), t2, t3, and t4.................................................................97 VII.6 The RSLC and SLC in the ideal transition model of element with 4 CSs............100 VIII.1 Flow chart of met hodology implementation........................................................105 VIII.2 Determination of elements repaired after 2000 by utilizing EHI variation trends ............................................................................................................................... ..........109 VIII.3 Element Inspection System..................................................................................111 VIII.4 A flow chart of a database program of determining the initial ages (ts)..............114 VIII.5 Distribution of the entire 862 CCD br idges in 2010 based on Pontis BHI, DBHI with ks Nand DBHI with ks J&R.........................................................................................120 VIII.6 Comparison of trends of ks N, ks, and ks J&Rfor n=5..............................................122 VIII.7 Comparison of trends of ks N, ks, and ks J&Rfor n=4..............................................122 VIII.8 Comparison of trends of ks N, ks, and ks J&Rfor n=3..............................................123

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1CHAPTER I.INTRODUCTIONBridge Management History For modern civilization, a countryÂ’sinfras tructure is essen tial to a countryÂ’s success.The United Statestransportation system has expanded to become thelargest and most modern highway system in the worl dsince the 1950s (Roberts and Shepard, 2000). The majority of the around600,000 bridges in the U.S. highwaysystem were built during two periods of time.The first period ofbridge construction occurred in the 1930s during the depressionyears and the second period of bridge construction happened inthe 1950s and 1960s (Hadavi,1998). As a consequence, th ebridges built in these two periods have grown old and may shortlyneedreplacement or major repairs. These facts illustratethe necessityof a rational procedure to determine which actions,and the costsassociated with them, are to be taken in order toprovi de safety and a satisfactory level ofbridge service(Rens et al., 2005). On December 15, 1967, the collapse of the 2,235-ft long PointPleasantBridge, also known as the Silver Bridge, over the OhioRiver betweenWest Virginia andOhio, illustrated the need forprograms of inspection and maintenance of bridges (Hartleet al., 1991). The Point Pleasant Bridge was built in 1928 and its failureoccurred without warning resulting in 46 fatalities. The collapse of the Point Pleasant Bridgewas precipitated by the stress corrosion failure of aneyebarlink.Because of its deadly consequences, the collapse exposedthe nece ssity of a rational program to conduct periodic inspectionsof the nationÂ’sbridges(R ens et al., 2005).Some reasons for bridge failure arecorrosion, fatigue, inappropriate design,wind, scour, earthquake,floods, and

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2 fire (Harik et al.,1990). In most cases, failures can beprevented by periodic maintenance inspections. Shortly sfter the collapse ofthe Point Pleasant Bridge, it led to a national concern about thesafety of each bridge in the UnitedStates and as a consequence,congress was urged to create a national bridge inspectionprogram(Hartle et al.,1991). Since that time, bridge infrast ructure managementhasbecome increasingly important in public agencies. The National Bridge Inventory Program (NBIP) was formed as a result of the Federal Highway Act of 1968(Czepiel, 1995) .The Federal Highway Administration (FHWA) requires each state to provideinforma tion about each bridge in their inventory as described inthe National Br idge Inspection Standard (NBIS), issued in April 1971. This information is used to generate a National Bridge Inventory(NBI).Before the NBIS was developed, therewas no clearly defined maintenance program in place to evaluate bridges; in fact, before 1968 the exactnumber of bridges was not even known (Hadavi, 1998).Due to the Federal Highway Act of 1970, the Special Bridge Replacement Program(SBRP)was formed. SBRP takes the NBI dataand provides funding to states to either he lp rehabilitate or replace an agencyÂ’sbridge.The bridge is analyzed based upon its sufficiency rating and its inadequacy(Czepiel, 1995). The FHWA uses aset of guidelines fo r allocating funds based on the conditions ofbridges determined by the NBI. Astudyon the guidelines for determining bridge needs toevaluate the accuracy of funding allocationwas conducted by the United States General Accounting Office(U.S. GAO).The study determined that thesufficiency ratings used by FHWA from the NBI todeter mine which bridges are eligible for federal funding are inadequate and indicate more brid ges asbeing deficient than are actually in

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3 critical need for rehabilitation(U.S. GAO, 1991). The NBIS data for a given bridge are limited and do not supply thedetailed information that can be used to predict a bridge’s futurecondition or provide an estimateon future maintenance and repair needs of an agency bridge inventory(WSDOT, 2010). Recognizing thata different strategy to wards future bridge preservation was needed,the NationalCooperative Highway Re search Program (NCHRP) published a reportin December 1987to provide the framework for a Bridge Management System (BMS).The overallobjective of this report wa s to developa model bridge management system thatcould be implemented by a st ate or localtransportation agency(WSDOT, 2010).Due to dwindling budgets for DOTs,an increasing pressure to minimize the cost ofmaintenance has created the necessity of BMS (Wolfgram, 2005).In 1993, it became required for all state agencies to have an operatingBMSaccording to the federal mandates for bridge managementoutlined in the American Association of State Highway and Transportation Officials(AASHTO)Guidelinesfor Bridge Management(AASHTO, 1993).Theseguidelines suggest that a BMSs houldcontain four basic components: data storage,cost and deterioration models,op timization models for analysis,and updating functions(Attoh-Okine, 2003). Darbani and Hammad (2007) state “ Governmental agencies in many countries started developing Bridge Management System s after a number of bridge collapses during the 70s ”.BRIDGIT,developed under an AASHTO-sponsored NCHRP, determinesproject-levelmaint enance, repair, and rehabilita tion(MR&R)for each bridge and is ideal for smaller bridge populations(Hawk, 1999).LIFECON LMS,Life Cycle Maintenance and Management Planning System, a EuropeanBMS, organizes planning,

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4 construction, maintain ing, repairing, rehabilitation, an dreplacing structures while considering safety, serviceability, economy, ecology, andother aspects of life-cycle planning (Vesikariand Sderqvist, 2003). Onta rio Bridge Management System (OBMS), a Canadian BMS, makes use of photos,docum ents and commentary in addition to a new element-level manual (Thompson et al., 2003). PontisBridge Management System(Pontis BMS) is comprehensive software tool initially developed by FHWA and now available from AASHTO as an AASHTOWareproduct (FHWA, 2011). Pontis BMS is now being used by forty-one States and five municipalities. Pontis BMS supports the completebridge management cycle,including bridge inspection and inventory data collection andanalysis, recommending anoptimal preservation polic y, predicting need and performance measures for bridges, anddeveloping project s to include in an agency's capital plan (Robert et al., 2003).Most notably, Pontis BM S provides a systematic procedure for the allocation of resources to the preservation a nd improvement of the bridges in a network by consideringboth the costs a nd benefits of maintenance po licies versus investments in improvements or replacement (FHWA, 2011). Th e latest version of the Pontis software, Pontis 4.4, is available from AASHTO. A new Web-based version of the software, Pontis 5.0, is currently under development. Current CCD Pontis BMS The definition of bridge management is a process of combining management, inspection, engineering, and economic informa tion in order to help determine the best actions to take on bridges in a network over a given peri od of time (AASHTO, 1993). A BMSisa tool for managing bridges and to help agencies meet their objectives. One main

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5 objective was to evaluate the health cond itionsof existing bridges and subsequentlyto makemaintenance, repair, a nd rehabilitationstrategies.The Pontis BMS, a product of AASHTO, is being used by41 DOTs in the United States (Thompson and Shepard, 2000).Countless other smallercity agencies pres umable use the system as well. The CCDPublic Works Department has been involved since 2000in the development and implementation of the PontisBMS.Pontis re liesonthebiennial visual inspection of major CCD bridges and triennial visual inspection of minor CCD bridges.Major structures are identified as those structures whosespan length exceeds 20 feet(6.1 meters) while minor structures are less.The implementation of Pontis as an active tool for bridge management has been evolving for the past 10 yearsin the CCD. Limited financial resources relative to the demand for overcoming existing deficiencies in bridges make it difficult for transportation agencies to carry out maintenance activities on bridge structures as frequently as necessary(Al-Wazeeret al., 2008). The 2009 Report Card for AmericaÂ’s Infr astructure reported that more than 26%, or one in four, of the nationÂ’ s bridges are either structurallydeficient or functionally obsolete. While some progress has been made in recent years to reduce the number of deficient and obsolete bridges in rural areas, the numberin urban areas is rising. A$17 billion annual investment is needed to substa ntially improve current bridge conditions. Currently, only $10.5 billion is spent annuall y on the constructionand maintenance of bridges(ASCE, 2009). It is significant to identify the bridges most in need of maintenanceand apply efficient rehabilitationstrategies to best utilize available financial resources.

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6 In the Pontis BMS, the Bridge Health Index (BHI)is adiagnostictool used to assess bridge healthcondition.Th eBHI can be calculated directly from element level inspection data. The BHI rangesfrom 0 to 100,which indicatesfrom the worst condition to the best conditionof a bridge.Itcan be developed for a single bridge or a network of bridges. Thus, the condition at any point in time can be obtained fr omthe health index trendsfor a single bridge.The list of bri dges in the worst condition can be identified from abridgedistributionnetwork. The BHIis an excellent diagnostictoolin bridge health assessment. Althoughthe BHIis being used by most states to trackbridgehealth condition and support decisionmaking,CCD engineers cons ider that the current BHI does not meet its needs. Jensen (2007) states “ Thecurrent BHIis not in line with all of the defects on a bridge.This is because the computation of this performance measureneglects “Smart Flags” elements in the bridge, which are dete rioration processes, such as scour, fatigue, and settlement ”.Engineers at the CCD are of the opinion that the current BHI neglects the effect of element damage on bridge h ealth, function, and safety(Jiang and Rens, 2010). The first stage of this study is from 2007 to 2009 and is introduced inChapters I throughVin this dissertation. It isbased on the 2007 Denver bridge network,which has 308 major bridges and 307 minor bridges. There are threegoals in the first stage of this study: (1) Review the current calculatingmethod of the Pontis BHI and demonstrate the influencing factors including cost.

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7 (2) ModifyPontis BHI which was termed the Denver Bridge Health Index (DBHI). The DBHI and its correspondingformul a consider the effect of element damage on bridge health and function. (3) Analyze the CCD network of bridgesand compare the original and modified BHI methodologies. To achieve these threegoals, a speci fic Denver major bridge (D-03-V-150) located at 8thAvenuebetween Mariposa St. and Vallejo St.wasusedas an example to illustrate the current calculation procedure of the BHI and to demonstrate the deficiency of the calculation results. This example wasalso used to calculatethe DBHI, a more rational BHI,withthe application of the nonlinear health index coefficient andusingthe weight coefficientadjustment factor. Figure I.1 is the research flowchart presenting the BHI analysis procedure in this thesis.

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8 Figure I.1 Flowchart of the BHI analysis procedure Equation V.3 Excellent Nonlinear health index coefficient (ks N ) Adjusted weight coefficient Experts determine element weightcoefficient WP w/o Quantity & Cost Equation IV.4 Good Equation IV.3 Equation IV.2 Weighting Points RC BHI FC BHI RC BHI FC BHI Element Value Misleading distribution Equations III.1 thru III.3 FC BHI Equation II.4 Equation II.5 RC BHI 8t h Ave Viaduct Health Or Unhealth General Pontis Rating Equations II.1 thru II.3 Equation V.4 Outstanding Perfect Actual ks J&R Equation VII.6 DBHI Non-subjective Actual condition Consistent with theoretical analysis

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9CHAPTER II.BRIDGE HEALTH INDEXCommonly-Recognized (CoRe) Elements TheAASHTOGuide for Commonly Recogn ized StructuralElements, often called the AASHTO CoReElement Manual,introducesthe definition of each element and the unit of measurementfor as ma ny as 108 elements(Thompson and Shepard, 2000).Most states have successfully used the AASHTO CoRe elements as the basis for data collection, performancemeasurement, resource allocation, and managementdecision support.State agencies may supplement the AASHTO CoRe Element Manual with their own element definitions. The Pontis Bridge Inspection Coding Guide was developed by the Colorado Department of Transporta tion(CDOT) in 1997.It is intended to supplement the AASHTO CoRe Element Manua l with clarifying information and additional elements unique to Colorado bri dges and structures (CDOT, 1998). Colorado adds 36elements such asprecast panel concrete deck, prestress (P/S) concrete floor beam, and bridge wingwalls.The 144CoReelements in the Pontis Bridge Inspection Coding Guide are included in AppendixA The CCDPublic Wo rks Department has collected element level inspection data base d on the Pontis Bridge Inspection Coding Guidefor 615total structures.The total stru ctures include 308major structures and 307 minor structures.The CCD, like many other public works entities, uses theAASHTO CoReelements as the basis for its Pontis BMS. Condition States The AASHTO CoRe Element Manual define seach structural and nonstructural element and thedescriptions forassociated c ondition states (CSs). The definitions and

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10 descriptions reflectthe most common proce sses of deterioration and the effect of deterioration on serviceability. AASHTO CoRe element manual generally defines the levels of deterioration of each CoReelementas follows(Thompson and Shepard, 2000): Table II.1Levels of deterioration of each CoRe elementDeterioration level Description 1. ProtectedThe elementÂ’s protective materials or systems are sound and functioning as intended to prevent deterioration of the element. 2. ExposedThe elementÂ’s protective materials or syst ems have partially orcomp letely failed, leaving the element vulnerable to deterioration. 3. AttackedThe element is experiencing active attack by physical or chemical processes, but is not yet damaged. 4. Damaged The element has lost important amounts of material, such that its serviceability is suspect. 5. FailedThe element no longer serves its intended function. The above levels in Table II.1are denoted Condition State 1(CS1)through Condition State 5(CS5)respectively and each CoRe element has a set of 3-5CSs.The element level inspection supplies the total quantity of each element and the quantity of the element in each respective CS.With thisin formation, the severity of the deterioration and the quantity of the deterioration can be determined for an individual element.Table II.2is an example of CSs for a structural element: Steel-Open girder-Painted.

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11 Table II.2CSs of steelopen gi rder-painted(element key: 107)CSCondition term 1There is no evidence of active corrosion and the paintsystem is sound and functioning as intended to protect the metal surface. 2There is little or no active corrosion. Surface orfreckledrust has formed oris forming. The paint system may be chalking,peeling, curling or showing other early evidence of paint system distress butthere is no exposure of metal. 3Surface or freckled rust is prevalent. The paint system isno longer effective. There may be exposed metal but there is no activecorrosion which is causing loss of section. 4The paint system has failed.Surface pitting may bepresent but any section loss due to active corrosion does not yet warrantstructural analysis of either the element or the bridge. 5Corrosion has caused section loss and is sufficient towarrant structural analysis to ascertain the impact on the ultimate strengthand/or serviceability of either the element or the bridge. Bridge Health Index Cheng and Melhem (2005) report that th e BHI is asingle number indicator ofthe structural health ofabridge. They go on to say that it is anintegral measurewhich meets bridge management engineersÂ’ need for measuringbridge health condition. This indicator is expressed as a percentagera nging from 0%(worstcondition)to 100%(best condition). The premise of the BHIis that each brid ge element has an initial asset value representing the best condition statewhen the bridge is new.Whenthe bridge deteriorates withage, the asset value of each bridge element reduces and represents a lower condition state.Afterrepair,maintenance, or rehabilitation, the a sset value of each bridge element increases and represents an improved condition state(Shepard and Johnson,2001).

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12 Inthe Pontis BMS, the BHIis calculatedby a series offormulas in two steps. Step onecalculatesthe element health i ndex(EHI)according to the condition rating distribution from the element-level inspecti on information. Step twocomputesthe entire BHIbased on the weighted EHI.The Pontis system usestwo weighting methods: the failure cost-based weighting method andthe repair cost-based weighting method.Each weighting method is dependent on cost. The following two items are next addressed: (1) Presenting the two BHI methodologies used in the Pontis BMS: failu re cost-based BHI methodology and repair cost-based BHI methodology,(2) Presenting the BHI results and the bridge distributions for the entire 615CCDbridgesusing two cost-based BHImethodologies. Pontis Bridge Heal th IndexMethodology According to AASHTO (2003), the health index of an individual element( He)is the ratio of the summation of the quantitie s in each condition state multiplied by a corresponding coefficient to the total quantity of the element.It can be calculated by the following formula: % 100 s s s s s eq q k HEquation II.1 Where Heis the health index of an individual element (EHI), s is the index of the condition state, qsis the quantity of the element inthesthcondition state, ksis health index coefficient corresponding to the sthcondition state.

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13 The health indexcoefficients of the CSsksare fractional values calculated as follows: 1 n s n ksEquation II.2 Where ksis the health index coefficient for the sthcondition state, n is the number of applicable condition states ( n =3, 4, and 5), s is the index of the condition state( s =1, 2,..., n ). The health index coefficientskstake the following values shownin TableII.3. Table II.3Health index coefficients ksof the CSsNumber ofCSs CS1CS2CS3CS4CS5 31.000.500.00 41.000.670.330.00 51.000.750.500.250.00 The health index( H )of the entire bridge can be evaluated as a weighted average of the health indexes of bridge elements based on element total quantity and relative importance.According toAASHTO (2003), it can be calculated by the following formula: % 100 e e e e e e eW Q W Q H HEuation II.3 Where H is the health index of the entire bridge (BHI),

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14 e is the index of an element, Qeisthe total quantity of element e, Weis the weighting factor of that element, and determined by either theelementÂ’s failure cost or an empirically assigned value as relative importance(AASHTO, 2003). Failure Cost-based BHI In the Pontis system,one of the elemen t weighting options that adopts element failure cost as weighting factor is the failu re cost-based weighting method. According to AASHTO (2003), the corresponding failure cost -based BHIis calculated by following formula: % 100 e e e e e e eFC Q FC Q H HEuqation II.4 Where FCeis total element failure cost calculated as a sum of its agency and user failure cost components(AASHTO, 2003). Equation II.4was applied tothe 162 majo r CCD bridgeswhich have complete inspection data from 2000 to 2006. The resu lts areshown in AppendixB.Among these results, 10 bridges were selected to be shown in Table II.4. This is because their BHIs during2000 to 2006 are all between 90% and 100% and have extremelyminimal decrease with age. In fact, with the exception of threeinspections, all inspection conditions are above 97%.Thisis despite the fact thatthey all have severe element

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15 damage. In addition, these 10 major bridges w ill be also used for repair cost-based BHI and DBHI. Table II.4Failure cost-based BHI for 10 major sample bridges Failure cost-based BHI(%) Bridge key2000200220042006 D-01-CC-01499.98498.97298.97298.086 D-01-CC-017100.000100.00099.09499.094 D-01-CC-019100.00099.39898.38298.382 D-03-V-04699.77499.76999.42699.074 D-03-V-09099.78299.47598.33298.252 D-03-V-15099.81096.88690.84490.424 D-04-BOST-5199.98299.44799.40799.320 D-31-PB-41099.44299.11497.68197.336 D-31-PB-65099.40499.15997.54197.541 D-31-PB-69099.90899.90899.81699.816 The 2006 data in Table II.4can be expande d to include the entire 615 CCD bridge network as illustrated in Figure II.1.

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16 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 80 90 100 1 0 2 2 2 11 12Percentage of total bridges (%)Bridge Health Index Bridge Distribution545 34 6FigureII.1 Distribution of the entire 615 CCD brid ges in 2006 based on failure costbased BHI As shown in Figure II.1, almost 90% of 615 bridges are in the highest BHIlevel. This result indicated the majority of the bridges were in a good health condition. However, 61% of bridges in the 90-100% rang e have served the CCD for over 20 years. Figures II.2 and II.3 show the distributionsfor 308 major CCD bridges and 307 minor CCD bridges in 2006 based on failure cost-based BHI.

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17 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 70 80 90 100 2 000 1 5 6Percentage of total bridges (%)Bridge Health Index Bridge Distribution265 25 4Figure II.2 Distribution of 308 major CCD br idges in 2006 based on failure costbased BHI 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 80 90 100 1 00 2 1 1 5 8Percentage of total bridges (%)Bridge Health Index Bridge Distribution280 9Figure II.3 Distribution of 307 minor CCD br idges in 2006 based on failure costbased BHI

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18 Repair Cost-based BHI Anotheroptionis called the repair cost -based weighting method.The weighting factor is the product of a weight coefficientand the cost of the most expensive element repair action.According to AASHTO (2003), th ecorresponding repair cost-based BHIis calculated by Equation II.5: % 100 e e e e e e e e ew RC Q w RC Q H HEquation II.5 Where RCeis the most expensive element repair action cost defined for eachelement, weis the weight coefficientassigned to each element according to elementÂ’s importance (AASHTO, 2003). Equation II.5was applied to162 major CCD bridgeswhich have complete inspection data from 2000 to 2006. The results are locatedin AppendixB.TableII.5 showstherepaircost-based BHI for previ ous 10 major sample bridges from 2000 to 2006,using Equation II.5.

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19 Table II.5Repair cost-based BHI for 10 major sample bridges Repair cost-based BHI(%) Bridge key2000200220042006 D-01-CC-01499.88697.88697.88196.801 D-01-CC-017100.000100.00097.83797.837 D-01-CC-019100.00099.81497.42997.428 D-03-V-04699.99499.99499.45299.009 D-03-V-09099.99699.60598.92198.908 D-03-V-15099.88699.49298.45197.762 D-04-BOST-15199.99099.81499.81098.820 D-31-PB-41099.96799.80199.09898.933 D-31-PB-65099.98399.81197.95497.954 D-31-PB-69099.77299.77199.54499.544 The 2006 data in Table II.5can be expa nded to the entire615 CCD bridges as shown in Figure II.4. 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 70 80 90 100 1 0 0 3 2 18 35Percentage of total bridges (%)Bridge Health Index Bridge Distribution501 50 5FigureII.4 Distribution of the entire 615 CCD brid ges in 2006 based on repair costbased BHI

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20 As shown in Figure II.4, 81.46% of 615 bridges are in the highest BHIlevel. This result is similar to the failurecost-base BHI result in Figure II.1. However, among 501 bridges, 56% of them have served the CCD for over 20 years. Figures II.5 and II.6 show the distributionsfor 308 major CCD bridges and 307 minor CCD bridges in 2006 base d on repair cost-based BHI. 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 3 6 9 12 70 80 90 100 5 0 0 0 1 1 21Percentage of total bridges (%)Bridge Health Index Bridge Distribution234 38 8Figure II.5 Distribution of 308 major CCD bridges in 2006 based on repair costbased BHI

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21 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 70 80 90 100 2 1 1 00 10 14Percentage of total bridges (%)Bridge Health Index Bridge Distribution267 12 0Figure II.6 Distribution of 307 minor CCD bridges in 2006 based on repair costbased BHI

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22CHAPTER III.PROBLEMSAlthough bridge management engineersusethe Pontis BHItoprioritize maintenance and rehabilitation, it is felt that the current methodology does not give an accurate portrayal of the condition of the ne twork(Jiang and Rens, 2010). To point out the deficiencies of the current BHI, the problems with the computationalresults and the computing methodology will be presented in th is chapter. In order to illustrate the problems, TablesII.4and II.5and Figures II.1 and II.4from Chapter IIare discussed basedon CCD expertise a nd actual inspection data. Problems withComputationalResults The Pontis BHI does not accurately represent the CCD bridge network.The accuracy of BHIcalculationis suspect.According to the current results,almost 90%of the CCD bridges lie in the highest BHI le vel between 90% and 100%as was shown in Figure II.1. This is despite the fact that the majority of the CCD network has served the community for manyyears as shown in Table III.1. Table III.1 Distribution of bridge age for the entire 615 CCD bridgesAge of bridge (years)Over 2020-1515-1010-5Under 5UnknownTotal Number of bridges3682886396331615 In addition, several known bridges that ar e due significant repair showupin the highest rated area(90%-100%). For example, 8thAvenue Viaduct bridge (D-03-V-150) is located on the west 8thAvenue over the railroad tracks between Mariposa St. on the east andVallejo St. on the west and is shown in FigureIII.1.Using the Pontis BHI, 2006 BHI results of this bri dge are 90.4% for the failure cost-based BHI

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23 and 97.8% for the repair cost-based BHI (shown in Tables II.4and II.5). Actually, this bridge has served 26years and repair is probably warranted due to the numerous cracks in the pier cap supports, buckles of the webs broken bearing guides, and the corrosionat the pot bearings. Figures III.2throughIII.4show the crack in the cap,buckles of the webs, broken bearing guides,and the corrosionatthe pot bearing, respectively. Figure III.1 Photograph of the 8thAvenue Viaduct brid ge (D-03-V-150). This bridge is located in metro Denver onthe west 8thAvenue over the railroad tracks betweenMariposa St. on the east and Vallejo St.on the west.

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24 Figure III.2 Flexure cracks at the pier cap. Several tension cracks extend in the cap section. Figure III.3 Buckle of the web. Visible buckle existed on the box girder exterior web.Figure III.4 Broken bear ing guides and corrosi onat the pot bearings PierCap Buckle Broken bearing guides Corrosion at the pot

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25 Another deficiency of the current overall BHI is its sensitivity. Even when a bridge suffers large amounts of element damage between inspections, the BHI only decreases an extremely minimal amount aswas shown in Tables II.4and II.5.For example, 8thAvenue Viaductbridge (D03-V-150) listed in Tables II.4and II.5had many new cracks in the pier cap supports which ha d been an ongoing distress since the year 2000. Cracks had been propagating in width and length at every biennialinspection as shown in Figure III.2. Inaddition buckled we bs (Figure III.3), and broken bearing guides and the corrosionat the pot bearings (Figure III.4) were identifiedin recent inspections. However, as shown in Table II.5, the trend of th e repair cost-based BHI of this bridge is 99.886% in 2000, 99.492% in 2002, then 98.451% in 2004, and finally97.762% in 2006. The overall BHI decrease is merely 2.124% between 2000 and 2006. Thisminimal decreasein numerical resultscan hardly tell bridge management engineers anyextentof element damagewhich hadhappened.It does not raise a flag that any local element deterioration issue ma y be developing. Finally, another feature of the BHI is that the r ationality of bridgedistributionin BHI is suspect.Figure II.1shows the bridge distribution plotted by BHI.Figure II.1 indicates that almost 90% of total bridge s ranked in the top 10% level (90%-100%). However, only 5.5%were in 80%--89%;2.0%in 70%--79%and less than 4%ranked in other interval levelsbelow 70%.The si gnificant disparity in bridge condition distribution between the top 10% leveland other levels misleadsbridge management engineersbetween the limits of good and poor conditions.In other words,is it credible

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26 that an overwhelmingly majorityof the br idgenetworkis still in outstanding condition even though many are in the 20-30 year old range? Problems in Computing Methodology Element Value in Pontis BHI Computation The key ideaof the current Pontis BHIis that elements are assigned weights according to the economic consequences of el ement failurewhen the element-level health indexes are convertedto the bridge-level h ealth index.The BHI presented inChapter II can alsobe presentedin an equivalentalternatematter. According toShepard and Johnson (2001), Pontis BHI is the ratioof the current element value to the initial element value of all elements on the bridge. Equations III.1 through III.3 are equivalent but more intuitive forms compared to Equations II.1 throughII.3.Although both Equations III.1 throughIII.3 and Equations II.1 through II.3determine thesame BHI results, element value defined in the former helps analyze the problems in the current Pontis BHI methodology. Therefore, presentingPontis BHI computation (Equations III.1 through III.3) utilizing element value is necessary. % 100 / TEV CEV HIEquation III.1 Where HI is the Bridge Health Index, CEV is the current element value, TEV is thetotal element value.FC TEQ TEV Equation III.2

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27 Where TEQ is the total element quantity, FC is thefailure cost of element.FC k QCS CEVi i Equation III.3 Where QCSiis thequantity in the condition state i, kiis thehealth index coefficient for the condition state i. TablesIII.2 through III.4present an application of Equations III.1 through III.3 to the8thAvenue viaduct bridge (D -03-V-150).Using Equation III.1 and Tables III.3 and III.4,the final BHI calculation is: % 424 90 % 100 8368242 $ / 7566901 $ % 100 / TEV CEV HITable III.2Element distributionfor 8thAvenue Viaduct br idge (D-03-V-150)ElementElement inspection data No.keydescriptionUnitTEQQCS1QCS2QCS3QCS4QCS5EHI (%) e(1)14P Conc Deck/AC Ovlyinch es8895.288895.280.000.000.000.00100 e(2)101Unpnt Stl Box Girderinches1444.761300.28144.480.000.0097 e(3)106Unpnt Stl Opn Girderinches176.48176.480.000.000.00100 e(4)210R/Conc Pier WallFeet164.59164.590.000.000.00100 e(5)215R/Conc AbutmentFeet27.4327.430.000.000.00100 e(6)234R/Conc Capinches175.260.00175.260.000.0067 e(7)305Elastomeric Flex Jtinches27.4327.430.000.00100 e(8)314Pot BearingEach86.0027.004.0055.0034 e(9)326Bridge WingwallsEach4.004.000.000.00100 e(10)331Conc Bridge RailingEach874.78874.780.000.000.00100 e(11)333Other Bridge RailingEach569.98569.980.000.00100 e(12)334Metal Rail CoatedEach722.38633.380.0089.000.000.0094 e(13)338Conc Curbs/SWEach722.38722.380.000.000.00100 QCS1through QCS5are element quantities in CS1 through CS5.

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28 Table III.3Total element value(TEV) for 8thAvenue Viaduct bridge (D-03-V150) ElementCalculation No.keydescription Unit failure cost (FC) TEQ* FC Resulting TEV TEV (%) e(1)14P Conc Deck/AC Ovly$788895.28*78$6938328.29 e(2)101Unpnt Stl Box Girder$7401444.76*740$106912212.78 e(3)106Unpnt Stl Opn Girder$2344176.48*2344$4136694.94 e(4)210R/Conc Pier Wall$14573164.59*14573$239857028.66 e(5)215R/Conc Abutment$3157327.43*31573$86604710.35 e(6)234R/Conc Cap$8740175.26*8740$153177218.30 e(7)305Elastomeric Flex Jt$231927.43*2319$636100.76 e(8)314Pot Bearing$434986*4349$3740144.47 e(9)326Bridge Wingwalls$12054*1205$48200.06 e(10)331Conc Bridge Railing$456874.78*456$3989004.77 e(11)333Other Bridge Railing$442569.98*442$2519313.01 e(12)334Metal Rail Coated$285722.38*285$2058782.46 e(13)338Conc Curbs/SW$133722.38*133$960771.15 Total( $8368242100

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29 Table III.4Current element value(CEV) for 8thAvenue Viaduct bridge (D-03-V150)ElementCalculation No.keydescription i*ki) *FCResulting CEV e(1) 14P Conc Deck/AC Ovly8895.28*1.0*78$693832 e(2)101Unpnt Stl Box Girder[1300.28*1.0+144.48*0.67]*740$1033840 e(3)106Unpnt Stl Opn Girder176.48*1.0*2344$413669 e(4)210R/Conc Pier Wall164.59*1.0*14573$2398570 e(5)215R/Conc Abutment27.43*1.0*31573$866047 e(6)234R/Conc Cap175.26*0.67*8740$1026288 e(7)305Elastomeric Flex Jt27.43*1.0*2319$63610 e(8)314Pot Bearing[(27*1.0)+(4*0.5)+(55*0)]*4349$126121 e(9)326Bridge Wingwalls4*1.0*1205$4820 e(10)331Conc Bridge Railing874.78*1.0*456$398900 e(11)333Other Bridge Railing569.98*1.0*442$251931 e(12)334Metal Rail Coated[(633.38*1.0)+(89*0.5)]*285$193196 e(13)338Conc Curbs/SW722.38*1.0*133$96077 Total( $7566901 Effect of Element Value on Pontis BHI The product of theweight taken as an ec onomic cost and the element quantity is defined as an element value. The problem with the current Pontis BHI methodology is ascribed to an element value.The potential ef fects of an element value on the BHIare as follows: (1) The EHImultiplied by a higher element value willplaya decisive role in determining the BHI.Oppositely,the EHImultiplied by a lower element value will playan extremely minimalrole even though the EHIis zero due to the element failure.This was dem onstrated in Table III.3. (2) The element valueonly indicates its percentagein the total element value ( TEV )of a bridge in terms of economic cost but not the effect of element damage

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30 onthe bridge health and function.This e ffect should be applied as a weightto convertthe element-level health indexes to the bridge-level health index.The actual meaning of the element value multiplied by the EHIistheresidual element value (same as CEV in EquationIII.3). It is thereforeobjective to say the Pontis BHIis the percentageforresidual element value CEV out oftotal element value TEV of a bridge in terms of economic cost.This results in misleadingresults as weredemonstrated in Tables II .4and II.5and Figures II.1 and II.4. Conclusion The study presented in the first 2 sections of this chapterhelps concludethatthe current BHImeasure will not help bridge agencies gain a reasonable BHIfrom the results and will not support decision-making.The re liability of the BHIis a serious problem whichconcerns CCD bridge management engi neers.Therefore, a modification for the BHIcalculation wasnecessaryand is presented in the following chapters.

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31CHAPTER IV.ANALYSISIn the Pontis BMS,the EHIare combined intoto the BHIaccording to the product of element quantity and the weighting factorassigned to each element based on the elementÂ’simportanceas was demonstrated in Equation II.3.If the weighting factor represents importance for each element,this product reflects importance for each EHI. The weighting point, anewlyconstructed conc ept,is used to describe the relative importance of the EHI.As a result, asimplified formula for computingthe BHIis obtained(Jiang and Rens, 2010). Weighting Point and Simplified BHI It iscomplicated to simultaneously analyze all the factorsthat affectthe BHIin the final computationalformula.Therefore, a final simplified weighting point was developed for the bridge management engineerasan analysistool toobserve the integrated effect of all original factors in the computationalformulawith respect to the BHI.After determining the EHI, the weighting points for each element, WPe,which representthe relative importance ofthe EHI,will be used to combine the EHI toa final overall BHI.Equation IV.1isasimplified formulausing the weighting pointidea to compute the BHI.This simplifiedformula yields the exact same result as current Pontis BHI as presented in Equation II.3and Equation III.1.e eWP H HEquation IV.1 Where H isBridge Health Index,

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32 Heis element health index, WPeiselement weighting point,% 100 eWP, e is the index of an element. Using Weighting PointMetho dology in the Pontis BMS There are two optionsforutilizing the weighting pointin the current Pontis BMS. Followingare the equations for the two cost-b ased weighting points.Equation IV.2 is the failure cost-based weighting pointand is designated as Option 1. Equation IV.3 is the repair cost-based weighting pointas is termed Option 2. % 100 e e e e e eFC Q FC Q WPEquationIV.2 % 100 e e e e e e e ew RC Q w RC Q WPEquation IV.3 A question to be answered includes“ How will the element weighting point affect the BHI? ”Another question to be answered is “ Willoptions1and2be themostreliable methodologiesto obtain reasonable resultsof the BHI? ”In order to answer these questions, an experimental calculationof the BHI for 8thAvenue Viaduct bridge (D-03V-150)using Equations IV.2 and IV.3 was comp letedas shown in Table IV.1.Note that Equations IV.2 and IV.3 produc ethe same results as Equations II.4 and II.5.

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33 Table IV.1 Failure cost-based and repair cost-based element weighting point for 8thAvenue Viaduct br idge (D-03-V-150)ElementWeighting point (%) No.keydescriptionOption 1Option2EHI (%) e(1)14 P Conc Deck/AC Ovly 8.29 63.27 100 e(2)101 Unpnt Stl Box Girder 12.78 20.94 97 e(3)106 Unpnt Stl Opn Girder 4.94 2.53 100 e(4)210 R/Conc Pier Wall 28.66 2.94 100 e(5)215 R/Conc Abutment 10.35 0.39 100 e(6)234 R/Conc Cap 18.30 3.13 67 e(7)305 Elastomeric Flex Jt 0.76 0.22 100 e(8)314 Pot Bearing 4.47 0.61 34 e(9)326 Bridge Wingwalls 0.06 0.02 100 e(10)331 Conc Bridge Railing 4.77 2.07 100 e(11)333 Other Bridge Railing 3.01 1.34 100 e(12)334 Metal Rail Coated 2.46 1.67 94 e(13)338 Conc Curbs/SW 1.15 0.86 100 BHI: !eHe90.4 97.8 The weighting point wascomputed for e ach element based on Equation IV.2for option1 and Equation IV.3for option2.For a comparison,observethat: (1) The ratio of 28.66% to 0.06%, whichreflectsthe difference between the maximum weighting point and the minimum weighting point,is478 for option1. The ratio of 63.27% to 0.02% for option2 is 3164.Inother words, these ratios are on the order of 100 to 1000. (2)Using option2,the summation of weighting points fore(1)and e(2)is 84.21%,while the summationof weighting points from e(3) throughe(13) is 15.79%.In other words, 2 of the elements end up accounting for 84% of the final BHI. In contrast, 10 of the remaining el ements account for less than 16% of the final BHI. (3) The weighting point is proportional to the element quantity and theelement

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34 cost. For example, a repair with a large cost will assign too much importance to the weighting point, while it should be focused on safety. (4)Using either option1 or option2,the weighting pointis a numerical value which is alwaysaconstant factor in an y BHI calculation.What should happen is that when an element conditionbecomes distressed and hence poorly functioning, the weighting point should increase. In other words, its relative importance and overall contribution to the BH I should be more pronounced. (5) In this experimental calculationfor Table IV.1,the element health indexes werecalculated based on real 2006inspec tion data.This calculation shows that the EHI of e(6): R/Conc Cap is 67% andt hat of e(8):Pot Bearing is 34%. But the BHIscalculatedbythe simplified formula ofEquation IV.1are90.4%using option1 and 97.8%using option2.In other words, the overall high BHI number hides the fact that low elements conditions exist. Theresults of the BHI indicate that the 8thAvenue Viaduct bridge(D-03-V-150) is in good healthwith ratings of 90.4% a nd 97.8%. The cause of the high rating is entirelyattributed to points (1) through (5) above.The potential effect of element value on the BHI was already introduced inthe se cond section of Chapter III-Problems in Computing Methodology.The failure cost of option1 and repair cost of option2 multiplied by element quantity are both defined as element value Therefore,using Equations IV.2andIV.3, the element weighting point isthe percentageforits element valueout of the totalelement value TEV of a bridge. It does not indicate therelative importance ofthe EHIaccording to the effect of element damage on bridge health and function.

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35 Anotheroption is to use aweight coefficient as the weighting factor and to remove element quantityandhence remove co st from the computing formula of the weighting point. The weight coefficient, te rmed option 3, representsthe importance of elementsto bridge health and function.To develop the weight coefficients, many meetings were held between University of Colorado Denver experts and CCD experts. Each element was discussed based on all pa rticipantsÂ’opinions. Eventually, the final values of weight coefficientswere determined as shown in Appendix A. Equation IV.4 is termed the weight coefficient-based weighting point. % 100 e e e ew w WPEquation IV.4 Where WPeis element weighting point,% 100 eWP, weis elementweight coefficient, Equation IV.4 (Option 3) was applied to the 8thAvenue Viaduct bridge (D-03-V150) which is shown in TableIV.2.

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36 TableIV.2Weight coefficient-based elementweighting point for 8thAvenue Viaduct bridge (D-03-V-150) ElementWeighting point (%) No.keydescription weight coefficient Option 3EHI(%) e(1)14P Conc Deck/AC Ovly66.25100 e(2)101Unpnt Stl Box Girder1212.5097 e(3)106Unpnt Stl Opn Girder1212.50100 e(4)210R/Conc Pier Wall1515.63100 e(5)215R/Conc Abutment1212.50100 e(6)234R/Conc Cap1515.6367 e(7)305Elastomeric Flex Jt77.29100 e(8)314Pot Bearing66.2534 e(9)326Bridge Wingwalls44.17100 e(10)331Conc Bridge Railing22.08100 e(11)333Other Bridge Railing22.08100 e(12)334Metal Rail Coated22.0894 e(13)338Conc Curbs/SW11.04100 BHI (%): !eHe90 Using this method, the element weightin gpointwascalculatedusing the element weight coefficient and was thereforeindepende nt of cost. But if the EHIin Table IV.2 are assigned to elements,theBHI resultis still at 90%. This is misleading because the individual EHIof reinforced concrete cap is 67% andthat ofpot bearing is 34%which is severe impact to bridge hea lth and function.The cost is removed but the overall BHI is still arguably too high. Conclusion This section explored currently used P ontis calculationmethods using the element weightingpointto calculate the BHI.The result of this analysis finds 3 main points: 1.In the Pontis BMS,the element weighting pointsusing Equations IV.2 and IV.3 are based on element values (repair cost and failure cost). 2.The element weighting pointusing Equation IV.4 is based on the elementÂ’s

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37 importanceto bridge health and function.Although an improvement, the concept still misleads with a high BHI and low rated elements. 3.A further revised method mu st be developed using the theory that theelement weighting point should stressthe effect of element damage on bridge health and function. Thefurther revised method in this study is called the DenverBridge Health Index (DBHI) which will be introduced in ChapterV.

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38CHAPTER V.DENVER BRIDGE HEALTH INDEXAs analyzed in Chapter IV, the Pontis element weighting point has deficiencies and should stress the effect of element dama ge on bridge health and function.It was demonstrated that a modification of the BH I wasnecessary.The key idea included in the modification, from heretofore called theDBHI were emphasizing the effect of element: (1) damage on EHI; (2)health indexon BHI.Three important modifications are proposed to the original Pontis formula(Jiang and Rens, 2010): (1) In reference to option 3 discussed in chapter IV(EquationIV.4), element quantityand cost were removed from the formula. (2)The introduction of the nonlin ear health index coefficientks N. (3) The introduction of the weight coefficient adjustment method. In order to present the rationality of modifications, and to determine the adjustment factor value, Condition Index (CI) zones are introduced below. Condition Index(CI) Zones ACondition index (CI)is a numerical measure of the current state of a structure from a low of 0% to a high of 100%. It uni formly and consistently describes and ranks the condition of structure or structurecompone nts. The condition indexis meant to focus management attention on those structures most likely to warrant immediate repair or further evaluation (Greimann et al., 1991).The condition index scale has been adopted since 1989 and is listedin Table V.1.

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39 Table V.1 Condition index (CI) scaleValueCondition description 85-100Excellent-No noticeable defects, some aging or wear visible 70-84Very good-Only minor deterioration or defects evident 55-69Good-Some deterioration or defects evident, function not impaired 40-54Fair-Moderate deterioration, function not seriouslyimpaired. 25-39 Poor-Serious deterioration in at least some portion of structure, function seriouslyimpaired 10-24Very poor-Extensive deterioration, barely functional 0-9Failed-General failure or failure of a major component no longer functional In addition, for managementpurposes, the CI scale is calibratedin order to group structures into 3 basic zones as listed in Table V.2. Table V.2Condition index (CI) zonesZoneCI Range (%)Action 171-100Immediate action not required 241-70 Economic analysis of repair alternatives recommendedto determine appropriate maintenance action 30-40 Detailed evaluation required to determine the need for Repair, rehabilitation or recons truction, safety evaluation recommended. As the CI zones in Table V.2indicated, the purpose of the CI zones is to draw attention to a particular problem that may re quire further investigation.Therefore, in the study of following sections, CI zones ar e used to group element conditions, based on element health index. They are also us ed to group overall brid ge conditions, based on DBHI.

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40 Nonlinear Health Index Coefficient ks NAccording to Pontistheory (AASHTO, 2003), theEHIis the ratio ofthe sum of thecurrent quantities in each CSmultiplied by corresponding coefficients, over the initial total quantity ofthe element. It was presented in Chapter IIby Equation II.2in which ksis the healthindex coefficient corresponding to the sthCS.The health indexcoefficients for the CSsksarefractional valuescalculatedby Equation V.1: 1 n s n ksEquation V.1 Where ksis the health index coefficient forthesthcondition state, n is the number of applicable condition states ( n =3, 4, and 5), s is the index of the condition state( s =1, 2,..., n ). TableV.3 gives the health indexcoeffici ents which are according toEquationV.1. Table V.3Linear health index coefficients ksNumber ofCSs CS1CS2CS3CS4CS5 31.000.500.00 41.000.670.330.00 51.000.750.500.250.00 Table V.3 can be illustrated with Figure V.1.

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41 Figure V.1 Trend of health index coefficient of CSs. Shown is the linear health index coefficient for n=5, n=4, and n=3 (n represents the number of CSs).As shown in Figure V.1,the health inde x coefficients are linear values when n is equal to 3, 4, or 5 dependingupon the type of the CoRe element. Based on linear ks,the EHIfrom 2000 to 2006 werecomputed for the 8thAvenue viaductbridge (D-03-V-150)as shown in Table V.4. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 12345Sth condition StateElem Health Index Coefficien t n=5 n=4 n=3

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42 TableV.4 EHIbased on linear ksfor 8thAvenue Viaductbridge(D-03-V-150)EHI (%) No.Element keyElement description2000 200220042006 e(1)14P Conc Deck/AC Ovly100100100100 e(2)101Unpnt Stl Box Girder10010010097 e(3)106Unpnt Stl Opn Girder100100100100 e(4)210R/Conc Pier Wall100100100100 e(5)215R/Conc Abutment100100100100 e(6)234R/Conc Cap1001006767 e(7)305Elastomeric Flex Jt95100100100 e(8)314Pot Bearing100343434 e(9)326Bridge Wingwalls100100100100 e(10)331Conc Bridge Railing100100100100 e(11)333Other Bridge Railing100100100100 e(12)334Metal Rail Coated94949494 e(13)338Conc Curbs/SW100100100100 As shown in Table V.4,the EHIof e(6) : R/Conc Cap changes from 100% in 2000 to 67% in 2006 based on linear ks.Acorrding toTable V.2, 67%is ranked in CI zone 2. Element condition in CI zone 2 should accords with the action” Economic analysis of repair alternatives recommendedto de termine appropriate maintenance action ”. However, the actual element damage of e(6) :R/Conc Cap was severe As described in ChapterIII,8thAvenue Viaductbridgehas served 26years and repair is probably warranteddue to the numerous cracks in the pier cap supports as shown in Figure III.2. Therefore, the elementconditionof e(6):R /ConcCapshould be ranked in CI zone 3 corresponding to the action ” Detailed evaluation required ”. As analyzed above, the EHIbased on linear ksis not rational. In other words, in order to make the EHImore conservative, nonlinear health index coefficient (ks N)lines are presented in Figure V.2.

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43 Figure V.2 Comparison of trends of linear and nonlinear health index coefficient of CSs.“—“represents linear ks; “---“represents nonlinear ks N.(n represents number of CSs). FigureV.2 shows the comparison between 3 linear kslines and 3 nonlinear ks Nlines. In either linear coefficient lines or nonlinear coefficientlines, the trends of the health index coefficients descend with the increasing CSs. These descending trends result in gradually reducing of the EHI, and finally result in gradually reducing of BHI with the increasing element deteriorati on. However, compared to the linear kslines, the nonlinear ks Nlines are located at the lower positions. In other words, except for the first point and the last points in each line, each point in the nonlinear ks Nlines is lower than its corresponding point in the linear kslines. For example (n=5), k2,k3, and k4are 0.75, 0.5, and 0.25respectively, by contrast, k2 N,k3 N, and k4 Nare 0.6, 0.3, and0.1respectively. Compared to the linear descending trend, th ese nonlinear descending trends result in more greatly reducing of EHIand finally result in more greatly reducing of BHI with the 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 12345Sth condition StateElem Health Index Coefficien t n=5 n=5 n=4 n=4 n=3 n=3

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44 increasing element deterioration. In another words, using nonlinear ks N, the BHI calculation result will be more conservative. Table V.5 gives the nonlinear ks Nvalue corresponding to the Figure V.2. Table V.5 Nonlinear health index coefficients ks NNumber ofCSs CS1CS2CS3CS4CS5 31.000.200.00 41.000.400.100.00 51.000.600.300.100.00 Based onnonlinear ks N,the EHIfrom 2000 to 2006 werecomputed for the 8thAvenue viaductbridge (D-03-V-150)as shown in Table V.6. Table V.6 EHI based on nonlinear ks Nfor 8thAvenue Viaductbridge(D-03-V-150)EHI (%) No.Element keyElement description2000200220042006 e(1)14P Conc Deck/AC Ovly100100100100 e(2)101Unpnt Stl Box Girder10010010094 e(3)106Unpnt Stl OpnGirder100100100100 e(4)210R/Conc Pier Wall100100100100 e(5)215R/Conc Abutment100100100100 e(6)234R/Conc Cap1001004040 e(7)305Elastomeric Flex Jt92100100100 e(8)314Pot Bearing100323232 e(9)326Bridge Wingwalls100100100100 e(10)331Conc Bridge Railing100100100100 e(11)333Other Bridge Railing100100100100 e(12)334Metal Rail Coated91919191 e(13)338Conc Curbs/SW100100100100 Compared to Table V.4,Table V.6 shows the EHI arereducedwhenthe nonlinear ks Nis used. The EHIof e(6): R/Conc Ca p changes from 100% in 2000 to 67% in 2006 based on linear ks.However, based on nonlinear ks N, this decrease trend changes from 100% in 2000 to 40% in 2006.As analysis for Table V.4,the actual element damage of e(6):R/Conc Cap was severe, therefore this elemen t conditionshould be ranked inCI

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45 zone 3. The EHI40% based on nonlinearks Nis right ranked in CI zone 3 according to CI zones category. The e(6):R/Conc Cap accords with the action “ Detailed evaluation required to determine the n eed for Repair, rehabilitation or reconstruction, safety evaluationRecommended ”.Therefore, the EHI based onnonlinear ks Nis much more conservativethan that based on linearks. Weight Coefficient Adjustment Method The weight coefficient wasintroduced tofurt her define the effect of the individual elementon the overall bridge structure. It also reflectedthe relative importance of various elements.For example, railings and curbs had less impact on the overall bridge condition when compared to the effect of prima ry structural elements such as beams. Therefore, the proposed weig ht coefficients assignmore value to more significant structure elements.Relative initial weight coefficients of the 8thAvenue Viaduct bridge elements (D-03-V-150) are listed in Table V.7.The table shows that the reinforced concrete pier wall and the cap are the most important, and that the concrete curbs and sidewalks arethe least important. The norma lized weight coefficients are defined by Equation V.2and are listed in Table V.7. % 100 (%) e e e ew w wEquation V.2 Where weis elementweight coefficient.

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46 TableV.7 Unadjusted weight coefficients for 8thAvenue Viaduct bridge (D-03-V150)No.Element keyElement descriptionEHI (%)wewe(%) e(1)14P Conc Deck/AC Ovly10066 e(2)101Unpnt Stl Box Girder941213 e(3)106Unpnt Stl Opn Girder1001213 e(4)210R/Conc Pier Wall1001516 e(5)215R/Conc Abutment1001213 e(6)234R/Conc Cap401516 e(7)305Elastomeric Flex Jt10077 e(8)314Pot Bearing3266 e(9)326Bridge Wingwalls10044 e(10)331Conc Bridge Railing10022 e(11)333Other Bridge Railing10022 e(12)334Metal Rail Coated9122 e(13)338Conc Curbs/SW10011 100 Deteriorationcorrelates with a decreaseinEHI, and finally results in a decrease in the overall BHI. Distresses of various elem ents ata specific level now take different effects on the overall BHI becauseof the individual element weight coefficients. Furthermore, distresses of a specific element atvarious levels also have different effects onthe overall BHI. Greimann et al.(1991) developed condition a ssessment procedures for various components making up lock a nd dam structuresas part of theRepair, Evaluation, Maintenance, and Rehabilitation Programfor theU nited States Army Corps of Engineers. As part of this work, it became clear that, as the distress of an individual element became more severe, its relativ e importanceto the overall condition of the structurebecame larger(Greimann et al ., 1991).To account for this, the weight coefficients were adjusted or amplified with variable adjustment factors. Similarly, this variable adjustment factor was introduced to the DBHI toincreasethe weight coefficient as its EHIdecreases.Figure V.3illustrates adjustment factorversus EHIrelationship.

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47 The adjustment factor has a maximum value of eight,that is if an element has an EHIless than 40%, its importance increases eight times. Adjusted elementweight coefficientsof the 8thAvenue Viaduct bridge (D-03-V-150)and it s normalized form arelisted in Table V.8.Table V.8shows that the weight coefficien ts of element e(6) and e(8) which have low EHI(40% and 32%) are increased eight times. This illustrates that as these individual elements conditions decrease, the relative importanceof element e(6) and e(8) and theireffect on the overall structure conditionare increased. Figure V.3 A linear step curve for calculating adjustment factor in weight coefficient adjustment method. 0 1 2 3 4 5 6 7 8 9 10 0102030405060708090100Element Health Index(%)Adjustment Factor

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48 Table V.8 Weight coefficien t adjustment method for 8thAvenue Viaduct bridge (D03-V-150)No.Element keyElement descriptionEHI (%)AFwewe a j we a j (%) e(1)14P Conc Deck/AC Ovly1001662 e(2)101Unpnt Stl Box Girder94112125 e(3)106Unpnt Stl Opn Girder100112125 e(4)210R/Conc Pier Wall100115156 e(5)215R/Conc Abutment100112125 e(6)234R/Conc Cap4081512049 e(7)305Elastomeric Flex Jt1001773 e(8)314Pot Bearing32864820 e(9)326Bridge Wingwalls1001442 e(10)331Conc Bridge Railing1001221 e(11)333Other Bridge Railing1001221 e(12)334Metal Rail Coated911221 e(13)338Conc Curbs/SW1001110 100 DenverBridge Health Index The DBHIis the accumulation of weighted individual EHIand is given by EquationsV.3 through V.5. % 100 s s s s N s eq q k HEquation V.3 Where Heis the element health index, s is the index of the condition state, qsis the quantity of the element in the sthcondition state, ks Nis the nonlinear health index co efficient corresponding to the sthcondition state.

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49e e aj eAF w w Equation V.4 Where we ajis theadjusted weight coefficient, weis theweight coefficient, AFeis the adjustment factor. % 100 e aj e e aj e ew w H DBHIEquation V.5 Where DBHI is the Denver Bridge Health Index. Tables V.9 through V.11presenttheapplication of the DBHIformulas tothe 8thAvenueViaduct bridge(D-03-V-150).Using Eq uation V.5, the final BHI calculation is: % 6 56 % 100 243 46 137 % 100 e aj e e aj e ew w H DBHICompared to 90.4% of the failure cost-b ased BHI and 97.8% of the repair costbased BHI, the DBHI 56.6% is much more in line with the known element defects of 8thAvenue Viaduct bridge(D-03-V-150).It is unreasonable that the Pontis BHI for this bridge is in the highest BHI level 90%-100%. That is because according to the CI zones category, 90.4% and 97.8% are ranked inCI zone 1 corresponding to the action” Immediate action not required ”. However,as described in Chapter III, this bridge has served 26years. Repair is probably warrante d due to the numerous cracks in the pier cap

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50 supports, buckles of the webs, broken bear ing guides, and the corrosionat the pot bearings aswere shown in FiguresIII.2 through III.4.Therefore, the condition state of this bridge should not be ranked in CI z one 1. Pontis BHIs, 90.4% and 97.8%, cannot give engineers any warning of individual elem ent defect. Oppositely, the DBHI for this bridge, 56.6%, is ranked in CIzone 2 corresponding to the action” Economic analysis of repair alternatives recommendedto de termine appropriate maintenance action ”. So, the DBHI of 56.6%causes a flag to be raised so that the engineer can look into the reason of low number. Table V.9 Element distribution for the 8thAvenue Viaduct br idge (D-03-V-150)ElementElement inspectiondata (%) No.keydescriptionunitquantityq1q2q3q4q5EHI (%) e(1)14P Conc Deck/AC OvlySF957491000000100 e(2)101Unpnt Stl Box GirderLF4739.9990100094 e(3)106Unpnt Stl Opn GirderLF579100000100 e(4)210R/Conc Pier WallLF540100000100 e(5)215R/Conc AbutmentLF90100000100 e(6)234R/Conc CapLF57501000040 e(7)305Elastomeric Flex JtLF9010000100 e(8)314Pot BearingEA863156432 e(9)326Bridge WingwallsEA410000100 e(10)331Conc Bridge RailingLF2870100000100 e(11)333Other Bridge RailingLF187010000100 e(12)334Metal Rail CoatedLF2370880120091 e(13)338Conc Curbs/SWLF2370100000100 q1through q5are element quantities in CS1 through CS5.

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51 TableV.10 Adjusted weight coefficients for 8thAvenue Viaduct br idge (D-03-V-150)No.Element keyElement descriptionEHI (%)AFwewe a j e(1)14P Conc Deck/AC Ovly100166 e(2)101Unpnt Stl Box Girder9411212 e(3)106Unpnt Stl Opn Girder10011212 e(4)210R/Conc Pier Wall10011515 e(5)215R/Conc Abutment10011212 e(6)234R/Conc Cap40815120 e(7)305Elastomeric Flex Jt100177 e(8)314Pot Bearing328648 e(9)326Bridge Wingwalls100144 e(10)331Conc Bridge Railing100122 e(11)333Other Bridge Railing100122 e(12)334Metal Rail Coated91122 e(13)338Conc Curbs/SW100111 Table V.11 Calculation of DBHI for the 8thAvenue Viaduct brid ge (D-03-V-150) No. Element key Element descriptionEHI (%)we ajCalculation He* we aj e(1)14P Conc Deck/AC Ovly10066 e(2)101Unpnt Stl Box Girder941211.28 e(3)106Unpnt StlOpn Girder1001212 e(4)210R/Conc Pier Wall1001515 e(5)215R/Conc Abutment1001212 e(6)234R/Conc Cap4012048 e(7)305Elastomeric Flex Jt10077 e(8)314Pot Bearing324815.36 e(9)326Bridge Wingwalls10044 e(10)331ConcBridge Railing10022 e(11)333Other Bridge Railing10022 e(12)334Metal Rail Coated9121.82 e(13)338Conc Curbs/SW10011 243137.46 Equation V.5was applied to162 major CCD bridgeswhich have complete inspection data from 2000 to 2006. The resu lts are shown in AppendixC. TableV.12 showstheDBHI for previous 10 major sample bridges from 2000 to 2006, using Equation V.5.

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52 Table V.12DBHI for 10 major sample bridgesDBHI (%) Bridge key2000200220042006 D-01-CC-01499.63571.74571.74565.528 D-01-CC-017100.000100.00052.02052.020 D-01-CC-019100.00098.40651.66151.658 D-03-V-04691.42990.10362.22161.982 D-03-V-09090.40084.71056.69756.589 D-03-V-15099.23776.33656.93056.636 D-04-BOST-15199.86499.17598.42862.632 D-31-PB-41084.26284.35265.81759.160 D-31-PB-65086.84986.70055.03555.035 D-31-PB-69095.00095.00078.94778.947 As introduced in Chapter II,10 bridgeswer eselected to be shown in Table II.4. This is because their BHIs from 2000 to 2006 are all between 90% and 100% and have extremelyminimal decrease with age despite the fact thatthey all hadsevere element damage. Therefore, the condition states of these 10 bridges should not be consistently ranked inCI zone 1. Compared to Table II.4, Table V.12 shows that the BHIs of these 10 bridges have obvious descending with increa sing age.In addition, the bridge conditions in 2006 are mostly ranked inCI zone 2. The 2006 data in Table V.12 can be ex panded to include the entire 615 CCD bridges network as illustrated in Figure V.4.

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53 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 10 20 30 40 50 60 70 80 90 100 3 1 23 33 44 45 47Percentage of total bridges (%)Bridge Health Index Bridge Distribution363 47 9Figure V.4 Distribution of the enti re 615 CCD bridges in 2006 based on DBHI As shown in Figure V.4, a new bridge network distribution is presented. Compared to the bridge distributions shown in Figure II.1,DBHI reasonably distributes 182 bridges previously ranged in the 90 %-100% interval to the levels under 90%. Similarly, comparedto Figure II.4,138 bridges were redistributed to the level under 90%. These redistributed bridges act ually have various extentsof element damage according to real element inspection data. Figures V.5 and V.6 show the distributionsfor 308 major CCD bridges and 307 minor CCD bridges in 2006 based on DBHI.

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54 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 10 20 30 40 50 60 70 80 90 100 0 1 3 35 31 13 30Percentage of total bridges (%)Bridge Health Index Bridge Distribution147 40 8Figure V.5 Distribution of 308 major CCD bridges in 2006 based on DBHI 90-10080-8970-7960-6950-5940-4930-3920-2910-190-9 0 2 4 6 8 60 70 80 90 100 20 15 1 2 6 9 15Percentage of total bridges (%)Bridge Health Index Bridge Distribution216 16 7Figure V.6 Distribution of 307 mino r CCD bridges in 2006 based on DBHI

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55 Comparison between Pontis BHI and DBHI To illustrate the advantagesof the DB HI, the comparison of the entire 615 CCD bridges distributions utilizing failure cost-based BHI, repair cost-based BHI, and DBHI is shown in Figure V.7. Figure V.7 Distribution of the entire 615 CCD brid ges in 2006 based on failure costbased BHI, repair cost-based BHI, and DBHI. Figure V.7 displays bridge distributions in each BHI intervallevel. In the BHI level 90%-100%, comparing545 bridges (88.6%) for the failure cost-based BHI to 363 bridges (59%) for DBHI, the difference is 182 bridges (29.6%). Similarly, comparing 501 bridges (81.4%) for the repair cost-based BHI to 363 bridges (59%) for DBHI, the difference is 138 bridges (22.4%). Obviously either 182 bridges or 138 bridges have various extents of elementdamage and are rang ed in theactual BHI levels below 90% by DBHI.

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56 Another comparison of the BHI trends rela ted to the age of bridge using failure cost-based BHI, repaircost-based BHI, and DBHI isshown in Figure V.8. Figure V.8 Trend of BHI related to the age of bridge. Three lines in this figure respectively represent the trends of BHI using three BHI methods. For each BHI method, each point in the line represents the average of BHIs for those bridges whose ages are ranged to relevant 10 years interval.Generally speaking, bridge health and function deteriorate with bridgeage. Referring to Figure V.8, three lines repres enting the BHI related to the bridge age correctly present this deterioration trend. However, compared to the upper two lines representing failure cost-based BHI and repair cost-based BHI, the line representing the DBHI is more steep. In other words, the dete rioration rate of the DBHI decreases faster than the failure cost-based BHI and the repa ir cost-based BHI. Hearn (1996) stated “ the bridge deterioration proce ss accelerates rapidly with increasing age, and in a short period of time the deteriorationgrowth correlate s with a rapid decrease in bridge health

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57 condition “. The steep slope of the DBHI greatly accords with this fact. The DBHI can alert engineers of potential bridge health a nd function problems at an earlier deterioration stage.

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58CHAPTER VI.ISSUEAn increasing number of agencies are utilizing theBHIbased on element level inspectiondata.The Pontis user group was surveyed to determine the current use of the BHI for bridge management decision making. The survey results revealed that the BHI or a modified BHI is being used topredict the condition of a specific structure and for prioritizing projects at the network leve l (Kang and Adams, 2010). The CCD has successfully developed the DBHI to trac kbridgehealth condition as presented in Chapters Ithrough V. As a result, it is be ing used as a basis for resource allocation and maintenance, repair, and replac ement (MR&R) decision support. Kang and Adams (2010)state that“ There are no standards or guidelines for decision making based on the BHI.Since the BH I is relatively new, and familiarity with it is still growing, there is limited experience among agencies for using it ”. Althoughthe DBHIis providing valuable analysis and time ly alertnoticesof bridge health condition for CCD engineers, it was still in the pro cess of improvementsin its MR&R decision support.CCD engineersare of the opinion th at DBHI may have apotential sensitivity issue due to uncertainties and variabilities inherent in element weight coefficients. However, this author is of the opinion that some deviations exist between theestimated nonlinear ksand the theoreticalactual health index co efficients and this consequently is causingthe sensitivity issue. The second stage of this study is introduced in ChaptersVIthrough VIIIin this dissertation.It is based on the 2010 CCD bridge network, which has 490majorbridges and 372 minorbridges. There are threegoals in the second stage of this study:

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59 (1) Presentthe sensitivity issue in the DBHI based-MR&R decision supportand analyzethe reasonof the sensitivity issueof ks N. (2) Developa methodology ofdetermining the improved ks. (3)Implement methodology a nd determine the improved ks, which is named ks J&R. Finally, the CCD bridge condition distributi on will be presented based on the DBHI and using the improvedks. Background Knowledge Element Classification When the DBHI of a bridge is low, this raises “a safety flag” for engineers, and warns them to pay attention to specific elemen ts of thebridgeneeding maintenance. As a result, it is necessary to first again review the bridge elements. As shown in TableVI.1, all 144CoRe el ements in Appendix A can bedivided into three element categories based on the number of CSs. All elements under each category are grouped into different parts of a bridge structure. The average weof all elements under each element categ ory is also listed in Table VI.1.In this dissertation, MR&R activities will be restricted within these defined categories which will be further explained later. Table VI.1 Classification of 144CoRe elementsElement category CS count Number of elements Elements grouped by parts Average we I540 Deck(23), superstructure(10), substructure(2), miscellaneous(2), smartflags(2), general remarks(1) 9.6 II468 Deck(4), superstructure(25), substructure(19), culverts(4), miscellaneous(13), smartflags(3) 10.68 III336 Miscellaneous(22), smartflags(8), channel/roadway alignment(6) 3.44

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60 An analysis of Table VI.1 reveals the following trends: 1. Fromastructural componentpoint of view, all deck, superstructure, and substructure elements are included in Cate gory I with 5 CSs and in Category II with 4 CSs. 2.Looking atthe element categories, all el ements included in Category III with 3 CSs are miscellaneous elements (such as wingwall, railing, etc.),smartflag elements (like scour, settlement, etc.), and channel/roadway alignment elements. 3. From the perspective of element importance, the average weof elements includedin category I with 5 CSs and categ ory II with 4 CSs are respectively9.6 and 10.68. The average weof elements included in category III with 3 CSsis 3.44. Therefore, one can conclude that elements with 4 or 5 CSs areassociated with structural related condition states, and c onsequently elements with 3 CSs are associated with nonstructural condition states. DynamicCalculationReport (DCR) In order to utilize the DBHI to help aid with element MR&R decision making, the DCR was developed as show n in Figure VI.1. TheDCR utilizesDBHI methodology. The user can modify or change the elemen t quantity distribution to simulate element MR&R activities, thus it is dynamic.Twooutput functions of the DCRincludethe current DBHIandthe DBHI under element MR&R activities (DBHIMR&R) forsingleor multiple elements. DCRimplementation includes the following steps: (1) The user can choosethe Bridge Keyand select the bridge. (2) All element information and element inspection dataof the chosen bridgeare

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61 displayed in the report. The EHIand DBHIare automatically calculated. (3) After completing steps 1and 2, the user can input the new element quantity distribution in the CSarea.The EHI under the element MR&R (EHIMR&R) activitiesand the resulting DBHIMR&Rare automatically calculated. Figure VI.1 shows the DCR for bridge D-02-PR-057 as an example. The information on the 10 elements associated with this bridge and the element inspection data are displayed in the report. Figure VI.1 Dynamic Calculation Report The 10 individual EHI and the DBHI of 81.8% are automatically calculated. The MR&R activityis chosen for e(3) (elementMR&R)which is 100% in CS2. The repair activity,

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62 shown in the grey area, is such that 100% of element quantity improves to CS1. This means that e(3) is assumed to be repa ired, and consequently the individual EHIMR&Rbecomes 100%. As a result, the DBHIMR&Rimproves to 99.4%. The DCR enables engineersto observe the consequence of elementMR&R activity and its improvement tothe DBHI. One can also observe which elementMR&R activity most effectively improves the DBHI.This numerical calculation result is one of the tools forelementMR&R decision making. Issue After the DCRwas applied to many i ndividual CCD bridges and to the CCD major and minor bridge networks, an issue arose. The DBHIMR&Rwas more sensitive to the elements with 3 CSs which arearguablyr elatively less important (ie: non-structural). This will be demonstrated in the next section where the DCR numerical calculation resultsfor the three sample bridges and majo r and minor bridge networksare presented and discussed to demonstratethe sensitivity. Sample Bridges Table VI.2 shows the element informa tion and inspection data ofbridgeD-20MB-785.In Table VI.3, the EHI are calculated based on 2010element inspection data. In this example, e(5) bridge railing, is the only element with MR&R issues. The EHI and EHIMR&Rof e(5) are 15.20% and 100%respectively, and consequently the DBHI is 76% and the DBHIMR&Ris 100%.The DBHI of 76%is entirelythe result from the single element damage of e(5): bridge railing.A concern is whether the EHI is too sensitive tonon-structural elements such as bridge railing.This will be explored with sample bridges with multiple MR&R scenariosnext.

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63 Table VI.2The 2008 elementinspection da ta for the sample bridgeD-20-MB-785 (Location: 56thAve. & W. Havana) Element inspection data (%) No. Element key Element description CS count Unit Total quantity q1q2q3q4q5 e(1)40P Conc Slab/AC Ovly5SF362.321000000 e(2)338Conc Curbs/SW4LF31.70100000 e(3)321R/Conc Approach Slab4EA2.00100000 e(4)215R/Conc Abutment4LF45.72100000 e(5)333Other Bridge Railing3LF31.7007624 e(6)326Bridge Wingwalls3EA4.0010000 e(7)300Strip Seal Exp Joint3LF45.7210000 q1through q5are element quantities in CS1 through CS5. Table VI.3 The numerical calculationresult for the sample bridgeD-20-MB-785EHIMR&R(%) No.Element keyElement descriptionCS countweEHI (%)Option 1 e(1)40P Conc Slab/AC Ovly514100.00100.00 e(2)338Conc Curbs/SW41100.00100.00 e(3)321R/Conc Approach Slab42100.00100.00 e(4)215R/Conc Abutment412100.00100.00 e(5)333Other Bridge Railing32 15.20100.00 e(6)326Bridge Wingwalls34100.00100.00 e(7)300Strip Seal Exp Joint37100.00100.00 DBHI / DBHIMR&R:76% 100% The first sample bridge (D-20-MB785) had only one element MR&R option because only one element was damaged.Acomparison is now made for a bridge with two possible element MR&R options as shown inTable VI.4 and VI.5.The element MR&R option scenarios are limited to categories in this dissertation in order to compare the sensitivity level of each element category to the DBHI. Element categories were previously defined and discussed in Table VI.1.The element information and inspection data of the second sample bridgeD-03-V -180is shown in Table VI.4.The EHI are calculated based on element inspection data. The resulting DBHI is 69%. Two element MR&R optionsare considered: option 1 is to re pair e(1): concrete deck and e(2): steel open girder, which have 5 CSs with an average weof 9.5;Option 2 is torepaire(9):

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64 bridge wingwalls which has3 CSs and an average weof 4. The DBHIMR&Rof option 1 and option 2 are 74% and 86%, respectively.The results indicate that the DBHI is more sensitive againto theelementMR&Rwith 3 CSswhich are notasimportant from the structural perspective. Table VI.4The 2008 element inspection datafor the sample bridgeD-03-V-180 (Location: Evans over Santa Fe) Element inspection data (%) No. Element key Element description CS count Unit Total quantity q1q2q3q4q5 e(1)13Unp Conc Deck/AC Ovl5SF5622.590100000 e(2)107Paint Stl Opn Girder5LF2812.698211520 e(3)338Conc Curbs/SW4LF592.53100000 e(4)321R/Conc Approach Slab4EA2.00100000 e(5)215R/Conc Abutment4LF48.7793700 e(6)234R/Conc Cap4LF195.0794420 e(7)205R/ConcColumn4EA40.0097300 e(8)333Other Bridge Railing3LF385.279901 e(9)326Bridge Wingwalls3EA4.0001000 e(10)310Elastomeric Bearing3EA144.0067330 e(11)300Strip Seal Exp Joint3LF97.549730 q1through q5are element quantities in CS1 through CS5. Table VI.5 The numerical calculation result for the sample bridge D-03-V-180weEHI (%)EHIMR&R(%) No. Element key Element description CS count xi ixyi iyOption 1Option 2 e(1)13Unp Conc Deck/AC Ovl5 7 9.5 60.00 73.80 100.00 60.00 e(2)107Paint Stl Opn Girder5 1287.60100.00 87.60 e(3)338Conc Curbs/SW41100.00100.00100.00 e(4)321R/Conc Approach Slab42100.00100.00100.00 e(5)215R/Conc Abutment41295.8795.8795.87 e(6)234R/Conc Cap41596.0296.0296.02 e(7)205R/Conc Column41698.5098.5098.50 e(8)333Other Bridge Railing3299.4599.4599.45 e(9)326Bridge Wingwalls3 4420.0020.00 20.00 100.00 e(10)310Elastomeric Bearing3673.3373.3373.33 e(11)300Strip Seal Exp Joint3797.1997.1997.19 DBHI / DBHIMR&R:69%74% 86%

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65 For the second example, engineers maye rroneouslyconclude that the damage condition of elementsMR&Rwith 5 CSs of option 1 isless serious than that of the elementMR&Rwith 3 CSs of option 2. The third sample bridge (F-16-DW) provi des three element MR&R scenarios, of which the elementsMR&Rare all under the condition of serious damage. Again, the element MR&R option scenarios are limited to categories in order to compare the sensitivity level of each element category to the DBHI. Table VI.6 presents the element information and inspection data of sample bridgeF-16-DW. The EHI are calculated in Table VI.7 based on element inspection data. The DBHI is 38%. Consider three element MR&R options: option 1 is to repaire(4): st eel cap and e(5): steel pin/hanger,whicheach has5 CSs and an average weof 17.5;Option 2 is to repaire( 9): reinforced abutment and e(10): reinforced column, which ea ch have 4 CSs and an average weof 14;Option 3 is to repairthe two elements with 3 CSs and an average weof 6.5, which are e(12): bearing and e(13): expansion joint. The DBHIMR&Rof option 1, option 2, and option 3 are 42%, 42%, and 43%, respectively. In other words, the sensitivity level of DBHIMR&Rfor the non structural elementsMR&Rwith 3 CSs of option3 is appr oximately equivalentto that for the structuralelementsMR&Rwith 4CSs of option2 and 5CSs of option1. Again, this impliesthat the non-structural elements have equalor greater effe ct toMR&Ractivities.

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66 Table VI.6The 2006 element inspection datafor the sample bridgeF-16-DW (Location: I25 ML SBND)Element inspection data (%) No. Element key Element description CS count Unit Total quantity q1q2q3q4q5 e(1)334Metal Rail Coated5LF147.52000982 e(2)13Unp Conc Deck/AC Ovl5SF1324.870100000 e(3)107Paint Stl Opn Girder5LF429.770663310 e(4)231Paint Stl Cap5LF69.4901167220 e(5)161Paint Stl Pin/Hanger5EA35.00097030 e(6)338Conc Curbs/SW4LF147.52100000 e(7)331Conc Bridge Railing4LF147.52100000 e(8)321R/Conc Approach Slab4EA2.00100000 e(9)215R/Conc Abutment4LF41.45060400 e(10)205R/Conc Column4EA3.00670033 e(11)326Bridge Wingwalls3EA4.0075250 e(12)311Moveable Bearing3EA14.0001000 e(13)304Open Expansion Joint3LF39.62315019 e(14)308Constr Non Exp Jt3LF56.6901000 q1through q5are element quantities in CS1 through CS5. Table VI.7 The numerical calculationresult for the sample bridgeF-16-DWweEHI (%)EHIMR&R(%) No. Element key Element description CS count xi ixyi iyOption 1Option 2Option 3 e(1)334Metal Rail Coated529.799.799.799.79 e(2)13Unp Conc Deck/AC Ovl5760.0060.0060.0060.00 e(3)107Paint Stl Opn Girder51249.4649.4649.4649.46 e(4)231Paint Stl Cap5 15 17.5 28.92 43.74 100.00 28.9228.92 e(5)161Paint Stl Pin/Hanger5 2058.55100.00 58.5558.55 e(6)338Conc Curbs/SW41100.00100.00100.00100.00 e(7)331Conc Bridge Railing42100.00100.00100.00100.00 e(8)321R/Conc Approach Slab42100.00100.00100.00100.00 e(9)215R/Conc Abutment4 12 14 27.88 47.28 27.88 100.00 27.88 e(10)205R/Conc Column4 1666.67 66.67 100.00 66.67 e(11)326Bridge Wingwalls3480.0080.0080.0080.00 e(12)311Moveable Bearing3 6 6.5 20.00 20.00 20.0020.00 100.00 e(13)308Constr Non Exp Jt3 720.00 20.0020.00 100.00 e(14)304Open Expansion Joint3740.7740.7740.7740.77 DBHI / DBHIMR&R:38%42%42% 43% A statistical analysis can be used to support the statementthat elements with 3 CSs are more sensitive to the DBHI compared to elements with 4 and 5 CSs. Twenty bridges were randomly selected from the 862 CCD bridge database. For each individual

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67 bridge, element MR&R were completed for elements with 3 CSs and elements with 4 or 5 CSs and the respective DBHIMR&Rwere calculated. Paired-sample T test was completed to compare the DBHIMR&Rassociated with two MR&R strategies. A confidence interval of 95% with a signific ance level of 0.05 was u tilized. The parameter is interpreted as the average difference between DBHIMR&Rfor 3 CSs elementsMR&Rand DBHIMR&Rfor 4 or 5 CSs elementsMR&R.The null hypothesis is H0:=0 and the alternative hypothesis is HA: 0.The general results are shown in Table VI.8.The statistical analysis reveals that it is not plausible that =0, and positive value of indicates that DBHIMR&Rfor 3 CSs elementsMR&Rtend to be larger than DBHIMR&Rfor 4 or 5 CSs elementsMR&R.Therefore, the null hypothesis (H0:=0) is rejected, and the alternative hypothesis (HA: 0) is accepted.The statistical resultsconclude that we are 95% confident that the DBHIMR&Rfor elements with 3 CSs is 0.33% to 13.4% higher than DBHIMR&Rfor elements with 4 or 5 CSs(p=0.02) .The statementthat elements with 3 CSs are more sensitive to the DBHIis confirmed. Table VI.8Paired-samplesT test resultfrom SPSS program. Pair 1 Paired Differences t df Sig. (2tailed) Mean () Std. Deviation Std. Error Mean 95% ConfidenceInterval of the Difference Lower Upper DBHIMR&Rfor 3 CSs elementsMR&R – DBHIMR&Rfor 4 or 5 CSs elementsMR&R .0686400 .1395664 .0312080 .0033209 .1339591 2.199 19 .040

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68 CCDMajor and Minor Bridge Networks The totalCCD bridge networkconsists of 862 bridge structures,474 major bridges and 336 minor bridges, for which the DBHI can be computed. Table VI.9 separately introduces the element condition fo r the major bridge network and the minor bridge network. For each category of elements with 3, 4, and 5 CSs, Table VI.9 illustrates a countofthe number of total and damaged elemen tsand presents the average weand average EHI of damaged elements. The Denver Bridge Network Health Index (DBNHI) is the average of all DBHI. The DBNHI is equal to 77%forthemajor bridge network and 86%for the minor bridge network.Three element MR&R options for the major and minor bridge networks are s hown inTablesVI.10and VI.11, respectively. Similarly, the element MR&R option scenarios are limited to categoriesin order to compare the sensitivity level of each element category to the DBHI. Table VI.9 Element condition for bridge networks Major bridge networkMinor bridge network Element categories Damaged(1) / total(2) Average weof (1) Average EHI of (1) (%) Damaged(1) / total(2) Average weof (1) Average EHI of (t) (%) Elements with 5 CSs 376/8378.0162.13 107/3908.5063.85 Elements with 4 CSs 815/18798.7179.51125/8648.5354.79 Elements with 3 CSs 447/13285.5056.06 65/6153.8637.41 Total / DBNHI:1638/404477%297/186986%

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69 Table VI.10 The numerical calculationres ults for the major bridge networkAverage EHIMR&R(%) ElementMR&Rdescription Average weAverage EHI (%) Option 1 Option 2Option 3 350 damaged elements w/ 5 CSs 8.0559.35 100.00 59.3559.35 350 damaged elements w/ 4 CSs 7.2856.1056.10 100.00 56.10 350 damaged elements w/ 3 CSs 5.6944.9544.9544.95 100.00 DBNHI / DBNHIMR&R:77%82%81% 85% Table VI.11 The numerical calculationres ults for the minor bridge networkAverage EHIMR&R(%) ElementMR&Rdescription Average weAverage EHI (%) Option 1Option 2Option 3 50 damaged elements w/ 5 CSs 12.3449.18 100.00 49.1849.18 50 damaged elements w/ 4 CSs 11.4433.0633.06 100.00 33.06 50 damaged elements w/ 3 CSs 3.8223.3823.3823.38 100.00 DBNHI / DBNHIMR&R:86%88%89% 91% For the major bridge networkin Table VI .9,the number of damaged elements is 376, 815, and 447 for element with 5, 4, and 3 CSs respectively. The EHI of damaged elements included in each element category are calculated and ranked inasortedEHI list. The 350 lowest-EHI elements with 3 CSs, 4 CSs and 5 CSs were chosen from the EHI list ofeach element categoryand assignedMR&R option3, option2andoption1, respectively, as shown in Table VI.10.These elements are shown in Appendix D. The average weand EHI of the 350 damaged elementsMR&Rweredetermined for each option. The major DBNHI is 77%. The major DBNHIMR&Rof option 1, option 2, and option 3 are 82%, 81%, and 85%, respectively.The sensitivity level of major DBNHIMR&Rfor the

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70 non-structural elementsMR&Rwith 3 CSs of option3 is slightly greater than that for the structural elementsMR&Rwith 4CSs of option2 and 5CSs of option1. For the minor bridge networkin Table VI.9, similar results were obtained.The number of damaged elements is 107, 125, a nd 65 for element with 5, 4, and 3 CSs, respectively.The EHI of damaged elements included in each element category are calculated and ranked ina sortedEHI list. Th e 50 lowest-EHI elements with 3 CSs, 4 CSs and 5 CSs were chosen from the EHI list ofeach element categoryand assigned MR&R option3, option2andoption1, respectively, as shown in Table VI.11.These elements are shown in Appendix E.The average weand EHI of the 50 damaged elementsMR&Rwerecalculated for each option. The minor DBNHI is 86%. The minor DBNHIMR&Rof option 1, option 2, and option 3 are 88%, 89%, and 91%. The sensitivity level of minor DBNHIMR&Rfor the non-structural elementsMR&Rwith 3 CSs of option3 is approximately equiva lentto that for the structuralelementsMR&Rwith 4CSs of option2 and 5CSs of option1. This is similar with the previous result for the major bridge network, the three individual sample bridges, and the statistical analysis. Summary In summary, whenfocusing on the consequence of repair of #$DBHI) between DBHI and DBHIMR&Ror the DBNHIbetween DBNHI and DBNHIMR&R,the benefit of element MR&R activ ities for non-structural elementsMR&Rwith 3 CSs is always approximately equal or slightlygreater than that forstructural elementsMR&Rwith 4 and 5 CSs. Since the DBNHI is the average of all DBHI, a trend existing in the above sample bridges and bridge networks is that the DBHI is more sensitive to the

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71 elementsMR&Rwith 3 CSs, even though they are weighted smaller.The sensitivity should be with the elements with 4 and 5 CSs which are weighted higher. Analysis The DBHI was observed to bemore sensitive to the elementsMR&Rwith 3 CSs. The goal is to analyze the reason forthi strendthrough a mathematic derivation, and, consequentlytofindan analytical reason forthe sensitivity issue. Mathematic Derivation As mentioned previously, the benefitor c onsequence of repair is the difference DBHI) betweenthe repaired and unrepairedstates %&DBNHIis the average of all DBHI. Therefore, the purpose of this section is to provide an 'DBHI, and to $()###DBHIand the variation trends that result in the DBHI. In other words, the analysis can help engineers to understand which elementsMR&R*+DBHI). The mathematicalderivation is as follows:(1) DBHI: aj n aj j aj i aj aj aj n n aj j j aj i i aj aj n e aj e n e aj e ew w w w w w H w H w H w H w H w w H DBHI 2 1 2 2 1 1 1 1Where Heis the EHI of e(e), we ajis the adjusted weight coefficient of e(e).

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72(2) DBHIMR&R: aj j j aj i i aj n aj j aj i aj aj aj j j j aj i i i aj n n aj j j aj i i aj aj aj n j i aj aj aj n n j i aj aj aj n aj j aj i aj aj aj n n aj j R MR j aj i R MR i aj aj n e aj e n e aj e e R MR j j R MR j aj j R MR j R MR j i i R MR i aj i RMR i R MR iw w w w w w w w w w H w w H w w H w H w H w H w H w w w w w w H w w w H w H w w w w w w H w H w H w H w H w w H DBHI w w AF w AF H w w AF w AF HR MR R MR R MR R MR R MR R MR 2 1 2 2 1 1 2 1 2 2 1 1 2 1 & & 2 2 1 1 1 1 & & & & & & && & & & & &, 1 %, 100 , 1 %, 100Where Hi MR&Rthrough Hj MR&Rare the EHIMR&Rof e(i) through e(j), AFi MR&Rthrough AFj MR&Rare the adjustment factor under element MR&R activity of e(i) through e(j),R MRaj iw&through R MRaj jw&are the adjusted weight coefficient under element MR&R activity of e(i) through e(j), withrough wjare the weight coefficient of e(i) through e(j).

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73(3) Simplification: 0 , 0 0 , , ,& 2 1 2 2 1 1 b b a DBHI DBHI Get w w w w b w H w w H w a w w w w w w H w H w H w H w H LetR MR aj j j aj i i aj j j j aj i i i aj n aj j aj i aj aj aj n n aj j j aj i i aj aj (4) DBHI: b b a b b a b a DBHI DBHIR MR DBHI &(5) Properties of DBHI: 0 0 0 , 0 1 1 0 0 0 b b a b a ator then numer b a and b a then H w H w w w w w w H w w H w b a b r Denominato bDBHI j aj j i aj i aj j j aj i i aj j j j aj i i i

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74 DBHI: j i e e e e e e e j i e e e e j aj j i aj i aj j j aj i i aj j j j aj i i i DBHIH H H H H w H AF w H w H w w w w w w H w w H w b a b a utilizing trend variation simulate b b a % 70 1 % 70 % 40 1 % 30 % 70 7 % 40 8 1 1 1 1 ) ( Two observations are obtained from th e above mathema tic derivation. #DBHIare Hiand wi. 2. Increasing wi(wi,$$-i(Hi./( $# DBHIDBHI, In other words, if an elementMR&Rconsists ofhigher wiand lower Hi, then it is possible to receive more benefit. In the next section, this theory is applied to previous sample bridges and CCD major and mi nor network in order to analyze the reason of trend. Reason of Trend Table VI.12and Table VI.13 )DBHIand influencing factors of element MR&R scenarios for previous sample bridge s and CCD bridge network, respectively.

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75 Table VI DBHIbased on ks Nfor sample bridgesInfluencing factors ElementweEHI (%) Sample bridges MR&R options key xi ixyi iy0DBHI ( % ) D-03-V-180 Option 1 for element w/ 5 CSs 137 9.5 60.00 73.805 1071287.60 Option 2 for element w/ 3 CSs 326 4 4 20.00 20.0017 F-16-DW Option 1 for element w/ 5 CSs 23115 17.5 28.92 43.744 1612058.55 Option 2 for element w/ 4 CSs 21512 14 27.88 47.284 2051666.67 Option 3 for element w/ 3 CSs 3116 6.5 20.00 20.005 308720.00 Table VI DBHIbased on ks Nfor CCD major and minor bridge networksInfluencing factors Bridge network Element MR&R options Average weAverage EHI (%) 0DBHI ( % ) CCD major bridge network Option 1for 350 elements w/ 5 CSs 8.0559.355 Option 2 for 350 elements w/ 4 CSs 7.2856.104 Option 3 for 350 elements w/ 3 CSs 5.6944.958 CCD minor bridge network Option 1for 50 elements w/ 5 CSs 12.3449.182 Option 2 for 50 elements w/ 4 CSs 11.4433.063 Option 3 for50 elements w/ 3 CSs 3.8223.385 As shown in Table VI.12, for sample br idge (D-03-V-180), option 2for elements with 3 CSs corresponded toalower weof 4 anda lower EHI of 20%and produced a

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76 higher 0DBHIof17%.Similar resultswereobtained for bridge F-16-DW:alower weof 6.5 and a lower EHI of 20% corresponding to opt ion 3for elements with 3 CSsgenerated thehighest 0DBHIof 5%. In Table VI.13, for the major bridge network, option 3for elements with 3 CSs withalower weof 5.69 anda lower EHI of 44.95%resulted in the highest 0DBHIof8%. Similar resultswereobtained for the minor bridge network:alower weof 3.82and a lower EHI of 23.38% corresponding to option 3f or elements with 3 CSsgenerated the highest 0DBHIof 5%. In summary, the experimentalresults of the sample bridges, including the networks, do not support the theorythat higher wiand lower Hiresult in higher 0DBHI. The relatively lower Hiof elementsMR&Rwith 3 CSs is the reason of trend. To be consistentbetweenexperimental data from bridge network and the theory, further analysis to find the reason forsensitivity issue is necessary. Reason of Issue Based on the above analysis, it is n ecessary to review EHI calculating methodologyin order to determine why the EHI is more sensitive to 3 CSs elements as supposed to 4 and 5 CSs elements. The EHI is calculated by following Equation VI.1. % 100 s s s s N s eq q k HEquation VI.1 Where s is the index of the condition state. qsis the quantity of the element in sthcondition state.

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77 ks Nis the nonlinear health index co efficient corresponding to the sthcondition state. As shown in Equation VI.1, qsand ks Nare two influencing factors of He. Both qsand ks Nare discussed next to determine th e reason of sensitivity issue. Considerqsfirst. Table VI.14presents the average quantities in CS1(the CS without element damage)and all other CSs of total elements included in each element category. Comparing 82% ofelements with 3 CSs to 86% and 68% of elements with 4 and 5 CSs in CS1for major bridge network andsimilar comparison for minor bridge network, it is concluded that th e damage distribution of total elements with 3 CSs is not much broader than that of total elements w ith 4 or 5 CSs. Table VI.15shows the average quantities in CS1(the CSwithout element damage)and all othe r CSs of damaged elements included in each element category. Again, comparing 47% ofelements with 3 CSs to 69% and 28% of elements with 4 and 5 CSs in CS1for major bridge network and similar comparison for minor bridge network, it is concluded that the damage extent of damaged elements with 3 CSs is not much great er than that of damaged elements with 4 or 5 CSs. Therefore, qsdoesnot account for the reason of sensitivity issue. Table VI.14 Average quantities in CS1 and all other CSs of total elements included in each element category for major and minor bridge networksMajor bridge networkMinor bridge network Average quantity (%) inAverage quantity (%) in Element description CS1 (w/o damage) all other CSs (w/ damage) Total (%) CS1 (w/o damage) all other CSs (w/ damage) Total (%) Total elements w/ 5 CSs 68321008020100 Total elements w/ 4 CSs 86141009010100 Total elements w/ 3 CSs 82 18100 92 8100

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78 Table VI.15 Average quantities in CS1 a nd all other CSs of damaged elements included in each element category for major and minor bridge networksMajor bridge networkMinor bridge network Average quantity (%) inAverage quantity (%) in Element description CS1 (w/o damage) all other CSs (w/ damage) Total (%) CS1 (w/o damage) all other CSs (w/ damage) Total (%) Damaged elements w/ 5 CSs 28721002773100 Damaged elements w/ 4 CSs 69311003070100 Damaged elements w/ 3 CSs 47 53100 27 73100 Consider ks Nnext. Table VI.16presents the linear and nonlinear health index coefficients ksand ks N. According to Table VI.16, two similarities between ksand ks Nare observed: (1) The health index coefficient of the first CS is 1 for each element category of ksorks N. (2) The health index coefficient of the last CS is 0 for each element category of ksor ks N. AASHTO defines the first CS as a good cond ition, while the last CS is the failed condition. The intermediate CS s have different extents of element damages. According to TablesVI.17and VI.18which introduce inte rmediate CSs, two differences between ksand ks Nare as follows: (1) In Table VI.17, the average of intermediate ksis 0.5 for each element category, and the averages of intermediate ks Nhave a descending trend from elementcategory I to III. (2) The intermediate ksare not equal to the intermediate ks Nfor each element category in Table VI.17. The differences between intermediate ksand ks Nare

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79 shown in Table VI.18. Table VI.16 Linear and nonlinear health index coefficients ks(ks N)Elementks(ks N) categorydescriptionCS1CS2CS3CS4CS5 IElements with 5 CSs1.00 (1.00)0.75 (0.60)0.50 (0.30)0.25 (0.10)0.00 (0.00) IIElements with 4 CSs1.00 (1.00)0.67 (0.40)0.33 (0.10)0.00 (0.00) IIIElements with 3 CSs1.00 (1.00)0.50 (0.20)0.00 (0.00) Table VI.17 Averages of intermediate ksand ks NElementks(ks N) categorydescriptionCS2CS3CS4Average IElements with 5 CSs0.75 (0.60)0.50 (0.30)0.25 (0.10)0.50 (0.33) IIElements with 4 CSs0.67 (0.40)0.33 (0.10)0.50 (0.25) IIIElements with 3 CSs0.50 (0.20)0.50 ( 0.20 ) Table VI.18 Difference between intermediate ksand ks NElementks-ks N categorydescriptionCS2CS3CS4 IElements with 5 CSs0.150.200.15 IIElements with 4 CSs0.270.23 IIIElements with 3 CSs 0.30 Referring tothe above two differences between ksand ks N, two main conclusions are obtained: (1) In Table VI.17, the average of intermediate ks Nof category III with 3 CSs (0.2) is less than that of category II with 4 CSs (0.25) and category I with 5 CSs (0.33). (2) Based on Figure V.2, the ks Nis the reduced ks. As shown in Table VI.18, the

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80 decrease (ks-ks N) of category III with 3 CSs is 0.3, which is more than any value of both category II with 4 CSs and category I with 5 CSs. The above two points result in relatively lower ks Nof the element with 3 CSs and, finally, result in relatively lower EHI of elements with 3 CSs. In other words, the reason for the sensitivity issue is the relatively lower ks Nof the element with 3 CS. Conclusion The analysispresented in the last section helps conclude that the most essential cause of the sensitivity issue is ks N. The ks Nwas determined subjectively by engineering experience. Therefore, a non-subjective hea lth index coefficient was necessary and is presented in the following chapters.

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81CHAPTER VII.METHODOLOGY TO DETERMINE ACTUAL ksVALUEBackground Knowledge The AASHTO CoRe Element Manual defines each element and the associated 35 conditionstates. The health index coefficient is an indicator that reflects the deterioration level of the c ondition state. This indicator may change from 0 (worst condition) to 1 (best condition). Presently, there arenoanalytical or experimental methods or techniquest o determine health index coefficients. The Pontis BHI methodology has the fo llowing two characteristics: (1) both failure and repair BHIs are based on cost; (2) the health index coefficients were assigned a series of linear subjective ksvalues.The problems associated with Pontis BHI methodology and analysis results demonstrat ed that a modification of the BHI was necessary. The key idea included in the m odification, called the DBHI, was emphasizing the effect of element damage on EHI and EHIon BHI. One of the important modifications proposed to the original Pon tis formula is to change health index coefficientsfromthelinear ksvalues tothenonlinear ks Nvaluesbased on engineering experience. As a result, the DBHI can address all element damages and is more conservative. Also, the bridge distributi on based on DBHI for the entire CCD bridge network is more rational than that base d on Pontis BHI as presented inChapter V. However, some deviations still exist in between the nonlinear ks Nvaluesand the actual health index coefficients. Although nonlinear ks Nare better, they were still determined subjectively. This is evident when low wiand low Hiresult in the highest 0DBHIas demonstrated in the examples of Chap ter VI. There are two possible reasons for

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82 the deviations. One possibility could bethat theks Nof the element with 3 CSs are less than actual values, or another that theks Nof the element with 4 or 5 CSs are greater than actual values. Both possibilities result in over-sensitivity of DBHI to the elements with 3 CSs. Therefore, the deviationbetween the nonlinear ks Nvaluesand the actual empirical health index coefficients is the reason causing the sensitivity issue when utilizing the DBHI with element MR&R decision making. To minimize the deviations and to obtain the actual health index coefficientsis a solution to develop by which the sensitivity issue can be eliminated. In conclusion, anyimproved health index coefficients (improved ks)should:(1) reflect actual deterioration le vel of the condition state; (2) help provide an objective reference for the elementMR&R decision maki ng; and (3) be determined by a reliable non-subjective methodology. Element Inspection Data Thompson and Shepard (2000) state “ One of the most immediate applications of CoRe elements is the collection and analysis of performance data. The datacollected through the biennial bridge inspection pro cess would be stored in a database, with subsequent users, ge nerally in the office and sometimes many years later, unable to apply any sort of subjective interpretation to th e data. Although some degree of analysis or interpretation may be applied by an inspector or engineer at the time of inspection, it is essential that the raw, objective data be st ored so that the analysis may be updated or improved at a later time. ”Thompson and Shepard (2000)also go on to saythat after an increasing number of agencies developed brid ge management system databasesusing the CoRe elements, these same agencies started using element inspection data for many types

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83 of agency decisions. The CCD, like many ot her public works entities, has successfully used theraw and objective element inspection data as a basis for performance measurement, resource allocation, and ma nagement decision support. Since the improved ksis an actual health index coefficient determined from empirical data, any sort ofsubjective interpretationwould be avoided. Therefore, by a survey of the entire actual CCD Pontis BMSdata, the raw and objective element inspection dataisa source to improveks.Figure VII.1 is the 2010 CCD Pontis BMS databasesche matic diagram. Figure VII.1 2010 CCD Pontis BMS databaseschematic diagram There are 490 major bridges, 1627 major bridge inspections, and a total of 24275 element inspection datapoints contained in the 2010 CCD Pontis BMS. Similarly, the database contains372 minor bridges, 875 minor bri dge inspections, and 5981 total element

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84 inspection data points.All the element inspecti on data of every bridge inspection of each bridge are respectively listed in Table VII.1. Table VII.1 Element inspection data in the 2010 CCD Pontis BMS databaseElement Bridge Bridge inspection inspection data (%) No.keyyear builtNo.yearNo.keydescriptionq1q2q3q4q5 1D-01-CC-0101977 12004 131Timber deck100000 :::::::: 11600Genl remarks1000000 :: : : : : : : : : : : : : : : : : 32010 131Timber deck100000 :::::::: 11600Genl remarks1000000 ::: : : : : : : : : : : : : : : : : : : : : : : : : : : : : 696D-31-PB-902200712009 123Conc deck1000000 :::::::: 10338Conc curbs/SW100000 ::: : : : : : : : : : : : : : : : : : : : : : : : : : : : : 862F-17-QH200512005 160Railroad deck1000000 :::::::: 10341Substr conc coating10000 Total: 862Total: 2502Total: 30256 q1through q5are element quantities in CS1 through CS5.Table VII.1 lists 862 bridges, 2502 bridge insp ections, for a total of 30256 sets of element inspection recordsin the CCD Pontis BMS data base. Theseare the same total numbers as shown in Figure VII.1. As shown in Table VII.1, these inspection data records all refer to different bridges, or elements,o rinspection years, and therefore,theyhave distinct disarrayand no regularity.As a result, theycannot be useddirectly.It is necessary to present the correlationand regularity within the element inspection data by a tool, which is an element transition model.

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85 Element Transition Model After the CSwas defined, transitionbeganimmediately. When a bridge element deteriorates, a transition is made from one condition state to a poorer one. When an element MR&R action is subjected to a specific bridge element, a transition is causedto improve the condition state. In the Pontis BMS, the transition concept appears in the Pontis Bridge Management Release 4 Technical Manual (AASHTO, 2003). In chapter 4: modeling system, one can find the following quote “ Deterioration of bridges is a probabilistic phenomenon –it is not possible to predict with certainty how each elem ent of each bridge will deteriorate over time. Since bridges c onsist of different quantities of elements that deteriorate differently from ea ch other, there is no meaningf ul way to quantify or even to speak about, deterioration of entire bridges. ”Pontis BMS addresses this dilemma through the element transition probabilities model(Scherer and Glagola, 1994). The probabilities model is actually aMarkovian deci sion model. It predicts the probability that a given unit of an element will transfer from one CS to another in one year due to element deterioration or el ement MR&R action. The initial transition probability for each element is provided through expert opinions. All of these data are assembled into element transition probability matrices. Pontis updates the element transition probabilities model using expert advice and historical inspection data. However, in the Pontis Bridge Management Release 4 Tec hnical Manual (AASHTO, 2003), one can also find the following quote “ Most element transition probabilities cannot be updated from the historic data in the current version of the system (Pontis Release 4 and higher) due to the change of the format in which MR&R work history is stored in Pontis ”.

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86 The model utilized in this studyis called an element transition model. Its definition is the change of the element quantity in each CS related to the age when the element is inspected. One can infer by this definition that the element transition model reflects thecorrelationand regularity within the element inspection data. Table VI.1 classifies all elements into three element categories by the number of CSs-specifically elements with 3, 4, and 5 CSs. Thus, there must be three types of element transition models associated with elements with 3, 4, and 5 CSs. Every individual element under each category utilizes its own element transiti on model. The development of a transition model of a specific element relies entirely on the inspection data for the specific element. Methodology The source of element inspection data is the basis toimproveks. Nevertheless, these data are distinctlydisarrayed, with no re gularity, and thus cannot be useddirectlyas was illustrated in Table VII.1.These facts i ndicate that the methodology for determining the actual ksshould be explored from the correlatio nand regularity within the element inspection data, which can be presented in the element transition model. Therefore, development of the element transition model and study on its correlationand regularity are the primary tasks in the methodology. Development of the Elem ent Transition Model Due tothe limited inspectionnumbersof each bridgestored in the CCD Pontis BMS, one specific bridge only can provide up to 6 inspection data points for a specific elementsuch as the concrete deck. Conse quently, one needs to use more than one specific bridge in development of the element transitionmodel. As an example, since a large number of concrete deck elements ar e included in the entire bridge network,

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87 numerous inspection data of the concrete deck elementscan beutilized as their structural, service, and deterioration are similar. This implies that the element transition model based on the entire bridge network can reflect the overall general de terioration trend of the concrete deck element. To developtheelement transition model, two requirementsmust be satisfied:(1) continuous long age range of element inspec tion data for an individual element; (2) a largeamount of element inspection data in any range of age for individual elements. Each of the requirements are described below. 1. Continuous long age range of elem ent inspection data for individual element. Table VII.2 lists 10 examples of element inspection data for the bridge railing. LinesNo.1 toNo.5 are the element inspection data in 5 different inspection years for the bridge railing, which is for bridge D-05RRBR-096 built in 1994. When the bridge was inspected during the period from 2000 to 2008, it was 6-14 years old.Thus, D-05RRBR-096 provides 6-14 year old element in spection data for the bridge railing. Similarly, line No.6 is the element inspecti on data forbridge railing of D-04-BOST-151 built in 1992. When the bridge: D-04-BOST-151 was inspected in 2008, it provided 16 years of element bridge railing inspecti on data. LinesNo.7 toNo.10 are the element inspection data in 4 different inspection ye ars for the bridge railing ofbridge D-26-SWG034 built in 1985. When the bridge D-26-S WG-034 was inspected duringthe periods 2002 to 2008, it provided 17-23 years worth of el ement inspection data for the bridge railing. Table VII.2 demonstratesthat 3 bridges built in different years provide6 to 23

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88 yearsof element bridge railing inspection data. This is the basic concept inherent in the model. Table VII.2 Sample of element insp ection data for the bridge railing Element inspection data (%) No. Element key Element description Bridge key Year built Inspection year Ageq1q2q3 1333Bridge railingD-05-RRB R-096199420006100.000.000.00 2333Bridge railingD-05-RRB R-096199420028100.000.000.00 3333Bridge railingD-05-RRB R-0961994200410100.000.000.00 4333Bridge railingD-05-RRB R-096199420071389.4710.530.00 5333Bridge railingD-05-RRB R-096199420081489.4710.530.00 6333Bridge railingD-04-BO ST-151199220081684.9815.020.00 7333Bridge railingD-26-SW G-034198520021782.3717.630.00 8333Bridge railingD-26-SW G-034198520041982.3417.660.00 9333Bridge railingD-26-SW G-034198520062182.3417.660.00 10333Bridge railingD-26-SW G-034198520082382.3417.660.00 q1through q3are element quantities in CS1 through CS3. The age of CCD bridgesrange from 1887to 2010. The bridge inspections stored in the CCD Pontis BMS run through 2000 to 2012 Figure VII.2 is a schematic diagram that describes the relationship among actual bridge year built, biennial inspection year, age, and the age range of element inspection data. Every single “dot” corresponds to a specific actual year built and a specific inspection year. For example, the dot (year built: 2010 and inspection year: 2010) re presents all 2010 built bridges which were inspected in 2010. The function of Figure VII.2 is to return the corresponding age from the actual bridge year built and the biennial inspection year. As shown in Figure VII.2, one example is that the years built (2010 & 1907) and the inspection year (2010) return the corresponding ages (0 & 103). This can be fu rther explained when different bridges built in 2010 & 1907 were inspected in 2010, they provided 0 & 103 years old element inspection data. Another example from Figure VII.2 is that the year built (1907) and the inspection years (2000-2010) return the corres ponding ages (93-103). In other words,

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89 when those bridges built in 1907 were inspected during the period from 2000 to 2010, they provided 93-103 years old element inspec tion data. From a theoretical point of view, even though there are bridges missing for some built years, the consistent band on the age 0-103 implies that different year built bridges and different inspectionyears can provide continuous long age range of elem ent inspection data for each element. Figure VII.2 Age range of element inspection data 2. Large amount of element inspection da ta in any range of age for individual element. Figure VII.1 shows that there are 2502 to tal bridge inspections and 30256 records of element inspections (that consist of 30256 el ement inspection data). These inspections provide large amount of element inspection da ta for each element. Figure VII.3 is the distribution of inspections in the entire range of age. As displayed in Figure VII.3, three

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90 bar chartsrespectivelyrepresent all bridge in spections, all concrete abutment inspections, and all P/S conc rete open girder inspections. The c oncrete abutment is the most common and frequently inspected element in the CCD P ontis BMS. All inspections are distributed by range of age in Figure VII.3 and its characteristics are as follows: (1) For either the bridge inspection or the concrete abutment inspection, both distribution trends similarly descend with the increasing age. For example, in the bridge inspection distribution, there are mo re in age 0-19, fewer in age 20-49, and fewest in age above 50. The significant de scending trend in the bridge inspection distribution also exists in the 144 other el ement inspection distributions. This is because the number of element inspections is dependent on bridge inspectionsin any range of age. Thus, the significant descending trend likely leads to inadequate element inspections in some ol d ranges of age for each CoRe element. (2) The inspection distribution of concre te abutmentis shown in Figure VII.3. The concrete abutment is the most comm on and frequent element in CCD Pontis BMS. It is indicated by Figure VII.3 that the numbers of inspections of many other elements such as P/S concrete open girder are all much less than that of concrete abutment inany range of age. This fact probably causes inadequate element inspections in some ranges of age for each element.

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91 Figure VII.3 Distributions of historical bridge/element inspections in 2011 CCD Pontis BMS based on the age Both characteristics result in insufficient element inspection data in some old age ranges for each element. The insufficien t element inspection data eventually produce large deviations to the expected actual value, even bringing uncertainties to the development of an element transition model.It is impossible to solve the actual element transition model according to the current CCD Pontis BMS database. Therefore, the ideal element transition model needs to be created. Ideal Element Transition Model As mentioned earlier, the element inspecti on data is the only basis to improve ks. Determining the improved ksis an analytical process, during which the methodology is based on the correlation and regularity of data Thecorrelationand regularity within the element inspection data are presented by the element transition model. However, using the current CCD Pontis BMS database, the de velopment of the element transition model is impracticable. Actually, the me thodology of determining improved kscan be

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92 determined from the correlationand regularity presented by an ideal model. This ideal model is called an ideal element transition model. Compared to the previous element transition model, the ideal element transition model has the same definition and is also classified into three types: elements with 3, 4, and 5 CSs. Only the ideal transition model of element with 4 CSs plotted in Figure VII. 4 is introduced here and used as an example in the next section. The same theory is used for elements with 3 and 5 CSs and is presented in Appendixes F and G. Figure VII.4 The ideal tr ansition model of el ement with 4 CSs Figure VII.4 illustrates the change of el ement quantity (Q) in each condition state related to the age when the element is inspected. Q1 through Q4 are element quantities in CS1 through CS4. The correlationand regulari ty presented by the idealtransition model of element with 4 CSs and their engineerin g meanings are discussed in the following three observations: Observation 1. In Figure VII.4, Q1 through Q4 respectively start from (t1(0), 100%), (t2, 0%), (t3, 0%), and (t4,0%). The year when the bridge is built, the bridge

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93 element is new. The age of the element is 0 (t1), and 100% of the element is in CS1. After t2years, the bridge element starts to deteriorate from CS1 to CS2. The age of the element is t2, and Q2 starts from 0%. Subsequently, after t3years, the bridge element starts to deteriorate from CS2 to CS3. The age of the element is t3, and Q3 starts from 0%. Eventually, after t4years, the bridge element starts the deterioration from CS3 to CS4. The age of the element is t4, and Q4 starts from 0%. Observation 2. At any age point, the sum of corresponding Q1 through Q4 is equal to 100%. According to Thompson and Sh epard (2000), when a bridge is inspected, the total quantity (100%) of each element is allocated among the condition states based on the visual observations of the inspector. Fo r instance, if 20% of the total length of a bridge girder has minor cracking, the inspector would note 20% in CS2 and 80% in CS1. Therefore, the sum of quantities among the c ondition states for each element is always 100%. Observation 3. At any age point (t), the algebraic sum of corresponding slopes on Q1 through Q4 equals 0. This statemen t is proved by the following mathematic operations. Equation VII.1 is an expression of Observation 2 in formulas. Equations VII.2 and VII.3 are referenced from Figure VII.4. Equation VII.4 is obtained by subtracting Equation VII.2 from Equation VI I.3. Equation VII.5 is Equation VII.4 divided by 0 t, which proves the above statement. % 100 t t tQ QEquation VII.1t t t t tQ Q Q Q Q 4 3 2 1 Equation VII.2

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944 4 3 3 2 2 1 1 Q Q Q Q Q Q Q Q Qt t t t t t Equation VII.34 3 2 1 0 Q Q Q Q Equation VII.4 t Q t Q t Q t Q 4 3 2 1 0Equation VII.5 Where: t and t are any age point and followed any short period of age as shown in Figure VII.4, Qtand Qt+ are total element quantity at t and t+ Q1tthrough Q4tare element quantities in CS1 through CS4 at t Q1 through Q4 are the changes of Q1tthrough Q4tduring t to t+ In other words, at any age point, the desce nding trends of quantities in some CSs must accompany the ascending trends of quantities in other CSs. In addition, at any age point, the sum of slopes of descendi ng trends is equalin magnit ude to that of slopes of ascending trendsbut is of oppositealgebraic sig n. For example, after having served for t2years, the bridge element begins to deteriorate from CS1 to CS2. At age t2in Figure VII.4, Q1 and Q2 respectively and simultaneous lystart to decrease and increase at the same rate. In any period of time between t2and t3, Q1 decreases the same amount of quantity as Q2 increases. The previous three observations follow the mo st universal laws that are present in all types of element transition models. It becomes apparent that an element transition

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95 model is known after the specific age points (t1, t2, t3, t4, etc.) and the variation trends are determined. To seek the specific age points, is then of prime importance. The next section will begin with these specific ag e points to determine a methodology of determining improved ks. Methodology As introduced in the previous section, the specific age point s are the transition points of element condition. They are also the critical age points in developing the element transition model. Thus, a study on the specific age points is of great significance and value. In the ideal element transition m odel, the element conditions at these specific age points are describedquantitatively. Ne xt, the element conditions are qualitatively expressed at these specific age points. A CoRe element: prestressed (P/S) concre te open girder (element key: 109) is used as an example element in the following analysis. The AASHTO definition and description of CSs for the sample element are presented in Table VII.3. The CDOT supplements Table VII.3 with two types of deterioration and corresponding CS scales shown in Table VII.4. The bridge inspectors collect data for two ty pes of deterioration by using the CS scales.

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96 Table VII.3Condition states of P/S concrete open girder(element key: 109)Condition state Condition term 1The element showsno deterioration. There may bediscoloration, efflorescence,and/or superficial cracking but without effecton strength and/or serviceability. 2Minor cracks and spalls may be present and there may beexposed reinforcing with no evidence of corrosion. There is no exposure ofthe prestress system. 3Some delaminations and/or spalls may be present. There maybe minor exposure but no deterioration of the prestress system. Corrosion ofnon-prestressed reinforcement may be present but loss of section isincidental and does not significantly affect the strength and/or serviceability of either the element or the bridge. 4Delaminations, spalls and corrosion on nonprestressedreinforcement are prevalent. There may also be exposure and deterioration ofthe prestress system (manifested by loss of bond, broken strands or wire,failed anchorages, etc). There is sufficientconcern to warrant an analysisto ascertain the impact on the streng th and/orserviceability of either theelement or the bridge. According to Thompson and Shepard (2000), the CSs reflect the most common processes of deterioration and its eff ects on serviceability. In fact, element deterioration is a complicated process. It is possible that only one type of deterioration process occurs, or two types happen at different times, or ev en that more than two types take place irregularly on the element. Also, the severity and extent of each type of deterioration are both unpredictable. In order to easily expr ess element condition dur ing deterioration, an ideal process of the sample element is estab lished as shown in Figure VII.5a. Only one type of deterioration (cracking) occurs on th e sample element: P/S c oncrete open girder. The schematic diagrams a(i) through a(iiii)represent element conditions atfour specific age points. Figure VII.5b (same as Figure VI I.4) is the ideal transition model of element with 4 CSs. With the aid of a(i) through a(iiii), the following analysis helps to qualitativelyunderstand the element cond itions at the specific age points (t1, t2, t3, t4etc.) as plotted in Figure VII.5b.

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97 Table VII.4 CDOT suggested CS scales for cracks and percent loss of bearing area in P/S concrete girderElementElement keydescription CS No. Crack width (W) (inches) keydescription CS No. Loss of bearing area (A) (%) 109 P/S concrete open girder 1W 12 109 P/S concrete open girder 120.004 < W 13 2A 1 10 30.009 < W 1 310 < A 1 40.030 < W420 < A Figure VII.5 Element conditions at t1(0), t2, t3, and t4At the year when the bridge is built, the entire element is in CS1. The element condition is a(i) at 0 (t1). Due to the frequent effects of large concentrated loads, varying environment factors (tempera ture, unequal settlement, etc. ), and bridge vibration, cracking in CS2 is always the first to occur at a specific location. For this example, this specific location is at the bottom edge of mid-span section because of simply supported beam, maximum moment at mid-span, and tens ion below neutral axis. In the ideal deterioration process of the sample element, this location where cracking first appears is named the critical location. After having served for t2years, the element cracking

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98 increases from CS1 to CS2 at the critical location as shown in a(ii). The element condition is a(ii) at t2. During t2to t3, more deterioration goes into CS2 from CS1 as shown in a(iii). Subsequently, due to the a bove same three deterior ation effects and the damage in CS2 that first occu rs at the critical location, cr acking in CS2 at the critical location initially grows intoCS3at t3as shown in a(iii). The element condition is a(iii) at t3. During t3to t4, more deterioration goes into CS3 from CS2 as shown in a(iiii). Eventually, due to the above same three deteri oration effects and the damage in CS3 that initially happens at the critical location, cracki ng in CS3 at the critical location primarily grows into CS4 at t4as shown in a(iiii). The element condition is a(iiii) at t4. As introduced in Table II.1, the AASHTO CoReElementManual generally defines 5levels of deteriora tionthat are denoted CS1 through CS5 respectively. Each CoRe element has a set of 3-5CSs.Table VI.1classifies all CoRe elements into three element categories by the number of condition states. Each categoryof elements with 3, 4, and 5 CSs has a set of 3-5 ks. Because the ksof the last CS for each element category is defined to be 0 in the Pontis Bri dge Management Releas e 4 Technical Manual (AASHTO, 2003), the last CSfor each element category represents the 5thlevel of deterioration: failed, the element no longer se rves its intended function This indicates that the critical location as shown in a( iiii) enters the failure condition and loses serviceability as it begins to deteriorate from CS3 to CS4 at t4. After t4, more deterioration goes into CS 4 (failure condition) from CS3. Meanwhile, the critical location continues to get worse at a higher rate. An immediate element MR&R action is recommended by bridge inspectors at a certain age after t4, when the critical location is too poor to work properly or safely. This certain age after t4determined by bridge

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99 inspectorsÂ’ engineering experi ence is element ultimate serv ice age. The corresponding life is the element ultimate service life. Similarly, the t4and related life are the failure age and service life of the critical location, which are determined by objective visual bridge inspections.Therefore, the critical locati on plays an important role in realizing the concept of service life. As defined by the Pontis Bridge Ma nagement Release 4 Technical Manual (AASHTO, 2003), the 1stand 4thCSs of the sample element represent the 1stand 5thlevels of deterioration, and thus the ksof the 1stand 4thCSs are 1 and 0respectively. For theksof the intermediate (2ndand 3rd) CSs, the definition and de scription of 5 levels of deterioration (Table II.1), 4 CSs (Table VII .3), and their CS scales (Table VII.4) cannot summarize any linear or nonlinear variation trend. In fact, the improved ksis inferred from the service life of the critical location. It is named Ks J&Rand defined as a ratio of the residual service life of the critical location (RSLC) at tsto the service life of the critical lo cation (SLC). The definition of Ks J&Ris expressed in formulas as shown in general Equation VII.6. n s n s R J st t t SLC t at RSLC K &Equation VII.6 Where: Ks J&Ris the actual health index coefficien t for every element under each element category, tsis the specific age point ( s =1, 2,..., n ), n is the number of applicable condition states ( n =3, 4, and 5), s is the index of condition state ( s =1, 2,..., n ),

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100 RSLCat tsis the residual service life of the critical location at ts, SLC is the service life of the critical location. The general Equation VII.6 is applicable to every element under each element category. Below is an application of general Equation VII.6 to every element with 4 CSs. The RSLC and SLC are displayed in the ideal transition model of element with 4 CSs in Figure VII.6. The K1 J&Rthrough K4 J&Rare computed by corresponding Equations VII.7 through VII.10. The same theory is used for every element with 3 and 5 CSs and is demonstrated in Appendixes F and G. FigureVII.6 The RSLC and SLC in the ideal transition mode l of element with 4 CSs 1 04 4 4 1 4 1 & 1 t t t t t SLC t at RSLC KR JEquation VII.7 4 2 4 2 & 2t t t SLC t at RSLC KR J

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101 Equation VII.8 4 3 4 3 & 3t t t SLC t at RSLC KR J Equation VII.9 0 04 4 4 4 4 & 4 t t t t SLC t at RSLC KR JEquation VII.10 Where: K1 J&Rthrough K4 J&Rare the actual health index coefficients of CS1 through CS4 for every element with 4 CSs, t1through t4are the specific age points, RSLC at t1through t4are the residual service lives of the critical location at t1throught4. As shown above, the K1 J&Rand K4 J&Requal to 1 and 0, and the K2 J&Rand K3 J&Rare calculated by the specific age points (t2, t3, and t4). Conclusion Using Equation VII.6 and its application to every element with 4 CSs (Equations VII.7 through VII.10) and same theory for every element with 3 and 5 CSs, one can determine the following: 1. For every element under each element category, the K1 J&Rand Kn J&Rare always equalto 1 and 0, which are derived from Equation VII.6. Thus, the K1 J&Rand Kn J&Rbased onthis methodology are in accord with the k1andknin the Pontis BridgeManagementRelease 4 Tec hnical Manual (AASHTO, 2003). 2. For every element under each element category, the Ks J&Rin Equation VII.6 are

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102 dependent on the specific age points (t1(0), t2, ..., tn). In fact, the preceding analysistells us that the specific age points (t1(0), t2, ..., tn) are the ages at which deteriorations at level 1 th rough n first happen at the critical location. Therefore, these ages are also called initial ages. 3. The initial ages (t1(0), t2, ..., tn) are all collected from element inspection data directly, which are the only basis of theKs J&R. 4. For the sample element P/S concrete open girder, thecalculated Ks J&Rbased on one set of initial ages from a specific girder reflect the deteriorationlevels of the critical location of this specific girder. However, the computed Ks J&Rbased on the average of multiple sets of initial ag es from all girders in the entire bridge network reflect the overall deterioration levels of all girders. This is because the former counts only one critical location of a specific girder, while the latter considers multiple possible critical locations of all girders. 5. TheKs J&Ris for every element under each element category, while the ks J&Rof each element category is a weighted average number determined from the Ks J&Rofall elements under each element category. The methodology implementation and the final ks J&Rresults are introduced and presented in Chapter VIII.

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103CHAPTER VIII.METHODOLOGY IMPLEMENTATION AND ks J&RBackground Knowledge Equation VII.6 is a general equation that is applicable to every element under each element category. As an application of Equation VII.6, Equations VII.7 through VII.10 are equations that apply to every el ement with 4 CSs. As was concluded in Chapter VII, the Ks J&Rin Equation VII.6 are dependent on specific age points (t1(0), t2, ..., tn). These specific age points are actually the ages at which deteriorations at level 1 through n first happen at the critical locati on, and hence are called the initial ages. The initialages (t1(0), t2, ..., tn) are all collected directly from element inspection data. Therefore, in theory, the ks J&Rcan be developed for every element under each category, as long as there is sufficient element inspection data. The following three points present the current data environment and its potential effects on methodology implementation. (1) Due to the limited inspection numbers of each bridgestored in CCD Pontis BMS, onespecific bridge can only provide up to 6inspection data points for a specific element suchas the concrete deck. Obviously, it is not sufficient to base any analysis on one specificbridge in determination of the initialages. However, since multiple concrete deckelements are included in the entire bridge network, the inspection data of all concretedeck elements in the entire bridge network can be utilized.Furthermore, one factpreviously mentioned is that all concrete deck elements in the entire bridge ne twork havesimilarities in structural characteristics,service functions, and failure mechanisms. Thesefacts implythat

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104 the initial ages based on the entire bridge network reflect thedeterioration characteristic of entire concrete deck elementslocated in the city and countyof Denver.Consequently, the entire bridge network is to be utilizedto serve as the source of element inspection data in determination of the initial ages. (2)As demonstrated in Figure VII.3, the significant descending trend in both bridge andelement inspection distributi on,includingmany elements with small quantities,result ininsufficient element inspection data and hence bring uncertainties to the determination ofthe initial ages. In other words, thisfact causesmissing data of the unknown initial ages(t2, ..., tn), especially the tn. Therefore, the Ks J&Rtend to be unable to be solved for manyelements with small quantitiesunder each element category. (3)The Ks J&Rare solved for severalelement s with large quantitiesunder each elementcategory. The ks J&Rof each element category can be respectively computed bycalculating a weighted average number from the Ks J&Rof all elements under each elementcategory. The above three points about the data envi ronment are taken into consideration in methodology implementation. Methodology Implementation Figure VIII.1 illustrates the flow char t of themethodology. The methodology implementation is completedin eight steps. Steps (a) through (d) aredata preparation and involve removing the faulty data points and affected data. Steps (e) through (h) are data processingand invol ve determining the initia l age, calculating Ks J&Rfor every element under each element category, and finally, summarizing the ks J&Rfor each

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105 element category. The Steps (a) through (d ) and Steps (e) through (h) are separately introduced in detail below. Figure VIII.1 Flow chart of methodology implementation Data Preparation The methodology of determining Ks J&Ris exploredin Chapter 7. Because determining Ks J&Ris a mathematical process that is based on element inspection data, preparing element inspection data is the fi rst workin methodology implementation. The comprehensive list of element inspection da ta for the entire CCD bridge network is presented in Table VIII.1.

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106 Table VIII.1 Element inspection da ta in the 2010 CCD bridge networkElement inspection BridgeElementdata (%) No.keyyear builtNo.keydescriptionNo.yearq1q2q3q4q5 1D-01-CC-0101977 131Timber deck 12004100000 :::::: 32010100000 ::: : : : : : : : : : : : : : : 11600Genl remarks 120041000000 ::::::: 320101000000 ::: : : : : : : : : : : : : : : : : : : : : : : : : : : : 696D-31-PB-9022007 123Conc deck120091000000 :::::::::: 10338Conc curbs/SW12009100000 ::: : : : : : : : : : : : : : : : : : : : : : : : : : : : 862F-17-QH2005 160Railroad deck120051000000 :::::::::: 10341Substr conc coating1200510000 Total: 862Total: 8999Total: 30256 q1through q5are element quantities in CS1 through CS5.Table VIII.1 lists 862 bridges, 8,999 bridge elements, for a total of 30,256 sets of element inspection data contained in the CCD bridge network, whichis Step (a) shown in Figure VIII.1(a).Theseare the same total num bers shown in Figure VII.1. In fact, a portionof entire element inspection data are inapplicable due to data inaccuracies and element repairs which are discussed in the following paragraphs. Table VIII.2 presents four examples re presentingfour different cases of data inaccuracies. All the raw data inaccuracies are boldedand summarized below for each case. In case 1, two sets of element inspec tion data exist on the same inspection date, which is due to a replicate data entryerror. Case 2 shows that two sets of element inspection data were recorded three months ap art and in the same year. Again, this is probably caused by replicate dataentryerror. As shown in case 3, two sets of different

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107 element inspection data present on the same inspection date, one of which istheresult of replicatedata entry and is identified as incorrect data. Case 4 illustrates that there are two sets of different element inspection data with different element quantities on the same inspection date, one of which is the result ofreplicatedata entry and is identified as incorrect data. All the data inaccuracies are identified and removedor corrected,which is Step (b) in Figure VIII.1(b). Table VIII.2 Data inaccuracies for sample bridgesElement inspection Elementdata (%) Case No. Bridge key keydescriptionunitquantitydateq1q2q3q4q5 1D-02-PR-140109 P/S Conc Open Girder Feet 9966/11/2003100000 996 6/14/2006 100000 996 6/14/2006 100000 2D-24-RM-020241 Concrete Culvert Feet 669/17/200294600 665/26/200594600 66 8/28/2008 94600 66 11/7/2008 94600 3D-27-MP-07031 Timber Deck SF 6778/3/2000100000 67710/17/2002001000 677 5/7/2003001000 677 5/7/2003100000 67710/6/2004100000 67711/16/2006100000 4D-01-CC-05013 Unp Conc Deck/ AC Ovl SF 632411/12/20020010000 2075211/12/20021000000 63247/29/20040010000 632410/16/20060010000 632411/11/20080001000 q1through q5are element quantities in CS1 through CS5.The Ks J&Ris the actual health index coefficient. It is computed using initial ages. If an element was repaired(rehabilitated), then the ages at which deteriorations at level 1 through n first happened prior to element repa ir are the actual initial ages. Conversely, the ages when deteriorations at level 1 thro ugh n first happened after element repair are not the actual first or initial ages. After Step (b) shown in Figure VIII.1(b), the remaining

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108 element inspection data are from elements re paired before 2000 and after 2000, including elements that have not experienced an y maintenance or repair activity. The CCD has been involved in the deve lopment and implementation of Pontis BMSsince the year 2000.The last available inspection data set wa s completed in 2010. Therefore, only 2000-2010 element inspection data exist for the purpose of this study. For elementsrepaired prior to2000, the initial ages collected from the(2000-2010) element inspection data are not the actual first ages. Thus, it is necessary to identifyall elements repairedor rehab ilitatedprior to2000 a nd remove their (2000-2010) element inspection data. It is completed by reviewing the historical drawings in Step (c) shown in Figure VIII.1(c). After Step (c) is completed, the remaining element inspection data consist ofelements repaired after 2000 and el ements which have not experienced MR&R activities. As previously noted, the CCD utilized the Pontis BMSafter the year 2000.For elementsrepaired after 2000, the initialages co llected from its (2000-repair year) element inspection data are the actual initial ages. Conversely, the initialages collected from its (repair year-2010) element insp ection dataare not the actual initial ages. Therefore, it is necessary to identifyall elements repaired af ter 2000 and remove their (repair year-2010) element inspection data. Tracking the simple EHI variation trends is a more effective and convenient way to identify repair and rehab ilitation compared to the inspection ofa large quantity of complex historical drawings. The schematic diagrams in Figure VIII.2 provide anexplanation.

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109 Figure VIII.2 Determination of elemen ts repaired after 2000 by utilizing EHI variation trends As shown in Figure VIII.2(a), the constant EH I trend of e(1) and the overall descending EHI trend of e(2) demonstrate that e(1) a nd e(2) were probably never repaired from 2000 to 2010.Conversely, it is illustrated that e( 3) and e(4) wereprobablyrepaired by the ascending EHI trends of e(3) from 2002 to 2004 and e(4) from 2006 to 2008 shown in Figure VIII.2(b). Therefore, the element insp ection data of e(3) af ter 2002 and e(4) after 2006 should be removed. This task is identifie d as Step (d) shown in Figure VIII.2(d). Data Processing Now that the data have been properly pr epared, the remaining data areregularly and systematically organized as shown in Figu re VIII.3 by the following three rules: (1) All elements in the entire bridge network are classified according to the number ofCS. Figure VIII.3 shows three categories of elements with 3, 4, and 5 CSs. (2)All the inspection data of every element under each element category are grouped bythe bridge key. For instan ce, the bridge railin g (element key: 333) under category I has182 groups of element inspection data, which means 182

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110 bridges in the entire bridgenetwork contai n the bridge railing (element key: 333). (3) All the element inspection data of each group are sorted by the element inspectionyear. For example, there are 6 individual items of element inspection data from 2000 to2010 in group No.1 of the br idge railing (element key: 333). As a result, a large and rigorous element inspection data system is established in Step (e) as shown in Figure VIII.1(e). The detailed sp ecifics contained in Step (e) are represented in Figure VIII.3.

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111Figure VIII.3 Element inspection data system

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112 Steps (f) through (h) in data processingare introduced in the following three sections. Determination of the Average Initial Ages (Ts) for Every Element under Each Element Category A brief descriptionas to how to collect the unknown initial ages (t2, ..., tn) from element inspection data is now presented.Th ere are four examples in Table VIII.3, which represent four different situationsof unknown initial ages. Each situation lists inspection data of an element of a brid ge for several inspection years and the corresponding age when the element was inspected. Table VIII.3 Determination of th e first ages for sample bridgesElement inspection data (%) Case No. Bridge key Element key Element description Age Inspection Year q1q2q3q4q5 1D-26-SWG-050156Timber floor beam 02004100000 32007891100 62010891100 2D-31-PB-310104 P/S Conc box girder 72000100000 92002100000 11200499100 3D-01-CC-270215 R/Conc abutment 43200094600 45200294420 47200494420 4D-05-RRBR-102202Paint Stl column 9020001000000 9220021000000 9420041000000 9720070010000 9820080010000 q1through q5are element quantities in CS1 through CS5.As shown in case 1, when the bridge el ement was first inspected in 2004, it was 0 years of age.The q2was 0% at 0 years age in 2004 and greater than 0% 3 years later in 2007. This means that a portion of the elemen t deteriorated from CS1 to CS2 during a 3 year period. Therefore,for this case,t2is equal to 3years.

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113 In case 2, the bridge element was inspected at age 7 inthe year2000. The q1did not decrease until 2004 at age 11, which means a portion of the element deteriorated from CS1 to CS2 at 11 years of age. Thus, t2equals 11years. Case 3 illustrates that the bridge element was inspected in 2000, when it was 43 years age. The q3was 0% until 2002 while the element was 45 years age, which means part of the element deteriorated from CS2 to CS3 at 45 years age. This indicates that t3is 45. It is shown in case 4 that when the bridge element was 90 years old, its first recorded inspection wasinthe year2000.Therefore, the q1is always equal to 100% until 94 years of age in 2004. The subsequent inspection in 2007 shows 0% for q1and q2and 100% for q3. This illustrates that theentire elem ent suddenly deteriorated into CS2 from CS1, and immediately entered into CS3 from CS2 at 97 years of age in 2007. Therefore, it is demonstrated that t2and t3are both equal to 97years. Figure VIII.4 illustrates a flow chart of the database program which determines the initial ages (ts). The goal of the database program is to return the initial ages for every element under each element categor y. First, the element inspection data in the group No.(j=1) of the element No.( i=1) are inputted into the database program. Next, the database program queriesevery item of elemen t inspection data in the group No.(j=1) starting from the item No.(r=1). Once tsis found or the loop of item No.(r) ends, the programautomaticallyreads the next group. Fi nally, the database pr ogram returns all tsfor the element No.(i=1) and the above process is restarted for the next element No.(i=2). Obviously, multiple first ages tsare obtained for the element No.(i=1). The average of multiple tsrepresents the average initial age (Ts) of the entire element No.(i=1).

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114 Determination of the average first ages (Ts) for every element under each element category is completed in the Step (f) shown in Figure VIII.1(f). Figure VIII.4 A flow chart of a database program of determinin g the initial ages (ts) Computation of the Ks J&Rfor Every Element under Each Element Category Based on the average initial ages (Ts) of each element determined in Step (f), the Ks J&Rfor every element can be computed utilizing Equation VII.6 as shown in Figure VIII.1(g). Table VIII.4 represents a porti on of Step (g), which is to compute the Ks J&Rfor every element with 4 CSs. The equations to compute K1 J&Rthrough K4 J&Rfor the

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115 element with 4 CSs are displayed in Table VIII.4 and were originally introduced in Equations VII.7 through VII.10.The same theory is used for every element with 3 and 5 CSs and is shown in Appendixes F and G. Table VIII.4 Computation of the Ks J&Rfor every element with 4 CSsNo. Element key T1 iT2 iT3 iT4 i (i)K1 J&R (i)K2 J&R (i)K3 J&R (i)K4 J&R 11e01 1 T1 2T1 3T1 4T 11 4 1 1 1 4 T T T 1 4 1 2 1 4T T T 1 4 1 3 1 4T T T 01 4 1 4 1 4 T T T ::::::::::qqe01qTqT2qT3qT4 14 1 4 q q qT T T q q qT T T4 2 4 q q qT T T4 3 4 04 4 4 q q qT T T The K1 J&Rthrough K4 J&Rare calculated for every element with 4 CSs in Table VIII.4. Similarly, in the entire Step (g), the Ks J&Rare computed for every element under each element categoryincluding 3 and 5 CSs elements. Computation of the ks J&Rfor Each Element Category The ks J&Rof each element category is computedfrom the Ks J&Rof all elements under each element cate gory. Table VIII.5 illustrates a simple example to comparetwo computing methods. Table VIII.5Computation of the ks J&RElement(i)KS J&RNiwiMethodFormulakS J&R e(A)0.2100121 R J s iK&0.5 e(B)0.81012 i i i i R J s iw N w N K&0.23 Niis the total number of the element; wiis the element weight coefficient. The elements e(A) and e(B) are in the same element category.The Ks J&R, Ni, and wiof e(A) and e(B) are shown in Table VIII.5. Two computingMethods 1 and 2 to

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116 calculate the ks J&Rare introduced in Table VIII.5. Method 1 is to calculate the average of all Ks J&R, while Method 2 is to compute a weighted average number from all Ks J&R. The ks J&Rof Method 1 and 2 are 0.5 and 0.23 respec tively. Compared to 0.23 of Method 2, 0.5 of Method 1 neglects the great importance (wi:12) of 100 e(A) out of a total of 110 elements. In other words, 0.23 of method 2 is more conservative than 0.5 of method 1. Therefore, in Step(h), method 2 is utilized to calculate the ks J&Rof each element category from the Ks J&Rof all elements undereach element category. Actual Health I ndex Coefficient ks J&RGeneration of ks J&RThe methodology in Chapter VIIwas imple mentedto the element inspection data system shown in Figure VIII.3.The data gene ration process isdisplayed in TablesVIII.6 through VIII.8.Finally, ks J&Rfor each element category areshown in Table VIII.9. Table VIII.6 Data processing for elements with 5 CSs Element k2 J&Rcalculation k3 J&Rcalculation k4 J&Rcalculation No. (i) key T2 iT3 iT4 iT5 i (i)K2 J&R (i)K3 J&R (i)K4 J&RNiwi (i) K2 J&R*Ni*wiNiwi (i) K3 J&R*Ni*wiNiwi (i) K4 J&R*Ni*wiNiwi 112182828280.350.000.0096723467206720672 2107151919200.250.050.0590122701080541080541080 33348811160.460.480.293982366796385796231796 4359132728500.740.470.442831209283133283126283 4 1079283157228314112831 k20.38k30.20k40.15

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117 Table VIII.7 Data processing for elements with 4 CSs Elementk2 J&Rcalculationk3 J&Rcalculation No.(i)keyT2 i T3 i T4 i (i) K2 J&R (i) K3 J&RNiwi (i) K2 J&R*Ni*wiNiwi (i) K3 J&R*Ni*wiNiwi 1311126280.580.0760724542028420 2323236390.180.0947528328 31091927300.370.12681229981695816 41104849520.070.0563125375640756 52151736480.650.25491123843589214735892 62341930350.460.1520215140130304573030 7240914200.540.3033610619859198 82411423330.590.32232682513924431392 93382532550.550.413521192352143352 103431119190.440.001071471070107 113581110140.250.3015813915848158 4 705613149279013149 k20.54k30.21 Table VIII.8 Data processing for elements with 3 CSs Elementk2 J&Rcalculation No.(i)keyT2 i T3 i (i) K2 J&RNiwi (i) K2 J&R*Ni*wiNiwi 130012320.6414376371001 230110350.711387690966 330215290.48827277574 43076250.76287149196 53117360.81986474588 631315380.61746269444 731410330.70476197282 83338360.781822283364 4 29754415 k20.67

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118 Table VIII.9 Improved health index coefficients ksCS count State1State2State3State4State5 51.000.380.200.150.00 41.000.540.210.00 31.000.670.00 Application of ks J&RThis non-subjective, theoretical ks J&Rwas applied toDBHI methodology introduced in Chapters Ithrough V.The DBHIMR&Rassociated withelement MR&R strategies were recalculated for sample bridges and CCD major and minor bridge networks utilizing ks J&R. TablesVIII.10 and VIII.11 show the results for the two sample bridges and bridge networks, respectively. Table VIII DBHIbased on ks J&Rfor sample bridgesInfluencing factors ElementweEHI (%) Sample bridges MR&R options key xi ixyi iy0DBHI ( % ) D-03-V-180 Option 1 for element w/ 5 CSs 137 9.5 38.00 61.9625.12 1071285.92 Option 2 for element w/ 3 CSs 326 4467.0067.001.06 F-16-DW Option 1 for element w/ 5 CSs 23115 17.5 20.89 29.1111.11 1612037.33 Option 2 for elementw/ 4 CSs 21512 14 40.67 53.671.14 2051666.67 Option 3 for element w/ 3 CSs 3116 6.5 67.00 67.000.26 308767.00

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119 According to Table VIII.10, for bridge D-03-V-180, option 1 corresponds to a relatively higher weof 9.5 and a relatively lower EHI of 61.96%. Based on ks J&R, Its 0DBHIrises to 25.12% which is relatively higher Similar resultswereobtained for Bridge F-16-DW, a relatively higher weof 17.5 and a relatively lower EHI of 29.11% corresponding to option 1 generated thehighest 0DBHIof 11.11%.Recall from Chapter VI, in contrast, Table VI.12shows that lower weand lower EHI produce the highest 0 DBHI for the two sample bridges, based on ks N. Table VIII.11 shows similar resultswhen MR&R activities are applied to the CCD major and minor bridgenetworks. Table VIII DBHIbased on ks J&Rfor CCD major and minor bridge networksInfluencing factors Bridge network Element MR&R options Average weAverage EHI (%) 0DBHI ( % ) CCD major bridge network Option 1for 350 elements w/ 5 CSs 8.0546.729.96 Option 2 for 350 elements w/ 4 CSs 7.2864.643.29 Option 3 for 350 elements w/ 3 CSs 5.6971.872.33 CCD minor bridge network Option 1for 50 elements w/ 5 CSs 12.3432..142.64 Option 2 for 50 elements w/ 4 CSs 11.4445.672.42 Option 3 for 50 elements w/ 3 CSs 3.8254.232.36 In summary, the calculation results support the statementthat higher wiand lower Hiresult in higher 0DBHI. Therefore, ks J&Ris an effectivetool forconsistencybetween data fromCCDbridge network and theoretical analysis.

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120 To illustrate the rationality of DBHI with ks J&R, the comparison of the entire CCD bridges distributions utilizing Pontis BHI, DBHI with ks Nand DBHI with ks J&Ris shown in Figure VIII.5. Figure VIII.5 Distribution of the entire 862 CCD bridges in 2010 based on Pontis BHI, DBHI with ks Nand DBHI with ks J&RFigure VIII.5 displays the bridge distributions in each BHI interval.Obviously, the bridge distribution based on DBHI with ks J&Rpresents similar trendsto DBHI with ks N.Hence, engineersare alerted of potential br idge health and function problems at an earlier deterioration stage.The DBHI with ks J&Riscomparable to the DBHI with ks N. However, the coefficients (ks J&R) are determined non-subjectively. Comparison of ks, ks N, andks J&RLinear health index coefficients ks, nonlinear health index coefficients ks N, and actual health index coefficients ks J&Rare shown in TablesVIII.12 through VIII.14, respectively. DBHI with ks J&R DBHI with ks N Pontis BHI

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121 Table VIII.12Linear health index coefficients ksNumber of condition states State1State2State3State4State5 51.000.750.500.250.00 41.000.670.330.00 31.000.500.00 Table VIII.13Nonlinear health index coefficients ks NNumber of condition states State1State2State3State4State5 51.000.600.300.100.00 41.000.400.100.00 31.000.200.00 Table VIII.14Actualhealth index coefficients ks J&RNumber of condition states State1State2State3State4State5 51.000.380.200.150.00 41.000.540.210.00 31.000.670.00 Comparisons among the trends of ks, ks N, and ks J&Rfor 5CSs, 4CSs, and 3CSs are displayed in FiguresVIII.6 through VIII.8, respectively.

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122 Figure VIII.6 Comparison of trends of ks N, ks, and ks J&Rfor n=5 Figure VIII.7Comparison of trends of ks N, ks, and ks J&Rfor n=4

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123 Figure VIII.8 Comparison of trends of ks N, ks, and ks J&Rfor n=3 In the scenarioof 5 condition states, the trend of ks J&Rbased on the element inspection data generally descends faster than the trends of ksand ks N. In the scenario of 4 condition states, the descendingtrend for ks J&Ris between the curves for ksand ks N. In the scenarioof 3 condition states, descending trend for ks J&R is slower than the other two curves.

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124CHAPTER IX.SUMMARY, CONCLUSION, AND RECOMMENDATIONS FOR FURTHER STUDIESSummary This dissertationprovidedan analysis of health indexesof bridgeslocatedin Denver, Coloradousing the bridge insp ection datafrom the CCD Public Works Department. The purpose of th is dissertationwas to determine the problems of the BHI currently usedbythe Pontis BMS and to obtai n an applicable and reasonable schemefor itsmodification.The first stage of this study introduced AF to amplify the effect of element damage on BHI. In the second stage, the actual empirical ks(called ks J&R) were determined to reflect actual deterioration le vels of CSs. The re search completedin two stagesfor this dissertationhas resulted in amore feasible diagnostic tool, the DBHI. The First Stage The main pointat the beginning of the first stage of this study wasto determine how the costaffectedthe BHI of the CCD netw ork.It was determined that thehistoric background ofcondition rating involving cost wasthat:(1)Cost was firstapplied in the method of failure cost-basedCa lifornia BHIwhich in turn introducedthe concept of the element value (2) The failure cost-based BH Iand repair cost-based BHIare two costbased options in the Pontis Bridge Management System. It was concludedthat the computationalresultsof each cost-based BHIrevealed the following problems: (1)The accuracy of BHIcalculationis not conservative.In other words, most

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125 bridgesrate in the highest BHIlevel,between 90% and 100% even after they haveserved manyyears and hadelement damageto various extents with age. (2) The tendency ofBHIdevelopmentis not sensitive to individualelement distress.Even when a bridge suffers large amounts of element damage between inspections,theBHIwill only decrease by an extremely minimal amount. (3) Rationality of bridge distributionbased on BHIs is suspect.The huge gap of bridgedistribution between the top 10% and other levels maymislead bridge managementengineersbetweenthe limitsof good conditionsand bad conditions. (4) Finally, the analysis of computing methodology proved that thePontis BHIis thepercentageof residual element value. It does not reflect bridge health condition. Inotherwords, element valu e has no correlative relationshipwith bridge healthcondition. As a starting point to the modification of the BHI, the concept of the element weighting point representingthe relative importance of element health index was developed.The nextstepwas to determinewhichfactorsthe element weighting point should be based on with regard to BHI.It was previouslybelieved that aweighting point analysis,based on element values,affectedthe BHI.However, the analytical study revealed thatthe cost-basedBHI result was notcalibrated well with actual element damage. Therefore, it was determined that the result couldnot satisfy bridge management engineerrequirements.It was then decided that the element weight coefficient representedthe importanceof an element tobridge health and function. Therefore, another attempt to calculatethe weighting point was completed based on the element weight coefficient. However, theoverall BHIresultstill did not represent an

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126 accurate condition based on actualelement da mage.Therefore, it was then demonstrated that any revised method should stress the effect of element damage on bridge health and function.In other words, it ha s to be based on safety. By taking the above into account, the DBHI wasproposed.The key pointsof the DBHI were that: (1) It removed the cost and element qua ntity and hence removed element value fromtheBHI calculation. (2) It introduced the nonlinear health index coefficient (ks N). Therefore, DBHI emphasizedthe effect of element damage on element health index. (3) It introduced weight coefficient adjustment method. Therefore, DBHI emphasizedthe effectof element health index onthe overallBHIas the individual element conditionsdecreased. A comparison of element health index wa s made between the calculating methods of linear ksand nonlinear ks N.The result showed that it wasfeasibleto emphasize element health index by the nonlinearhealth index coefficientks N.In fact, the nonlinear relationship results in a more conservative BHI whichwas the desiredeffect.But they were determined subjectively. Another comparisonwas madebetween the unadjusted weight coefficients and adjusted weight coefficients. The comparisonrevealedthat, when an individual element had a low element health index, this elementÂ’srelative importancewould beincreased by the adjustment factor. At the end of the first stage of this study, the DBHI was applied to theentireCCD bridge networkto trackbridgehealth conditionandused as a basis for resource allocation and MR&R decision support.

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127 The Second Stage Althoughthe DBHIprovidedvaluable analys is and timely alert of bridge health condition for CCD engineers,the DBHIwas still in the process of improvementin its MR&R decision support. At the beginning of the second stage, a sensitivity issue was observed in DBHIbased MR&R decision support for three sample bridges in a ddition to the CCD major and minor bridge networks. The statistical analys is results displayed a trend associated with the sensitivity issue in that the DBHI is more sensitive to the elementsMR&Rwith 3 CSs. Amathematicalderivationwas th en developed to provide an 'DBHI, andto $()###DBHIand the variation trends that result in increasingthe DBHI.The condition improvement resulting from MR&R is defined as DBHI. The theory showed that when the elementMR&Rconsists ofhigher wiand lower Hi, then itis possible to receive more benefit( DBHI).However, the actual results revealed just the opposite. Lower wiand lower Hireceived *DBHI.Thetheoreticalreason of the sensitivity issuewas due to the relatively lower ks Nof the elementswith 3 CS. Therefore, it was necessary to obtain a health index coefficient which was not determined subjectively. Theimproved ksshould:(1) reflect actual de teriorationlevel of the condition state; (2) help provide an objec tive reference for the elementMR&R decision making; and (3) be determined by a re liable methodology.In the end, the new kswere still nonlinear but were not subjectively determined. To developtheactualks,element inspection data wasdetermined as a source to improve ks. However, these element inspection data cannot be used directly due to their disarray and lack ofregularity. The element transition model reformats the raw element

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128 inspection data.The methodol ogyto determine actual ksshould be explored from the correlation and regularity within the element inspection data. However, due to the insufficient element inspection data in CCD Pontis BMS database, development of the element transition model is impracticable. Therefore, the ideal element transition model was introduced. The methodolog y to determine actual kswas based on the service life of the critical position of the element transition model. The actual ks(called Ks J&R) for each element is a ratio of RSLC at tsto SLC. The methodology implementation cons ists of the following8steps: (1) Gather historical inspection data. (2) Inspection of data for inaccuracies. (3) Inspection of historical drawin gs for element repair before 2000. (4) Inspection of data for element repair after 2000. (5) Establishment of element inspection data system. (6) Determination of the average initial ages (Ts) for every element under each element category. (7) Computation of the Ks J&Rfor every element under each element category. (8) Computation of the ks J&Rfor each element category. Finally, methodology was implemented to th e entire CCD bridge ne twork to determine the ks J&Rfor each element category. The final re sults show proper distribution and proper MR&R behavior. Furthermore, the ks J&Rare non-subjectively determined from empirical data.

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129 Conclusion The conclusion of this study resulted in the following 4 main conclusions/deliverables: 1. Compared to the Pontis BHI, the DBHI serves as a conservative indicator of bridgehealth condition. It provides timely alert of pot ential bridge health and functional problems to CCD engineers at an earlier deterioration stage. 2. The CCD bridge distribution based on DBHI with ks J&Rdisplays a similar pattern to the bridge distribution based on DBHI with ks N. Hence, DBHI with ks J&Ris comparable to the DBHI with ks Nin monitoring the health condition of CCD bridge network. 3. The ks J&R is a dynamicindicator that reflects th e actual deteriora tion level of the conditionstates, because, in the future, it will be determined by an increasing number ofinspection data. 4. This methodology can be applied to an y bridge network to non-subjectively determine ksvalues. Recommendationsfor Further Work As AASHTO (2011) states “ All of the elements, whethe r they are National Bridge Elements (NBE) or Bridge Management Elements (BME), have the same general requirements: (1)Standard num ber of condition states, (2)The standard number of condition states are comprised of good, fa ir, poor, and severe general descriptions. ” One of the significant changes in the AASHTO Guide Manual for Bridge Element Inspection will be 4 CSs for all elements which follow good, fair, poor, and severe convention(AASHTO, 2011).However, elem ents will still begrouped by structure

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130 portion or by material in the new AASHTO ma nual. Therefore, thisauthor recommends further research on developingthe actual ksfor thegrouped bridge elements in AASHTO Guide Manual for Bridge Element Inspection. Due to the increasing number of bridge inspections and element inspection data, this author recommends that actualksisupdated every 10 years to be consistentwith actual element deterioration conditions. This author recommends a futher stud y regardingthe application of the methodologyto any level of br idgemanagement agency,such as a large area networkof CDOT or the small communitieslike Fort Collions.

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131 Appendix A The 144 CoRe Elements in Pontis Bridge Inspection Coding Guide This appendix presents the Table A.1 c ontaining 144 CoRe elements in Pontis Bridge Inspection Coding Guide.The Pon tis Bridge Inspection Coding Guide was developed by the Colorado De partment of Transportation (CDOT) in 1997. It is intended to supplement the AASHTO CoRe Elem ent Manual with clarifying information and additional elements unique to Colora do bridges and structures (CDOT, 1998). Colorado adds 36 elements such as precast pa nel concrete deck, pres tress (P/S) concrete floor beam, and bridge wingwalls.

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132 Table A.1 The 144 CoRe elements in Po ntis Bridge Inspect ion Coding GuideElement keyCS countElement descriptionElement weight Deck 125Concrete Deck -Bare7 135Concrete Deck -Unprotected w/ AC Overlay7 145Concrete Deck -Protected w/ AC Overlay6 185Concrete Deck -Protected w/ Thin Overlay6 225Concrete Deck -Protected w/ Rigid Overlay6 23*5Concrete Deck -Bare Protected w/Coated Bars6 24*5Conc Deck w/ Thin (<1 inch) Overlay, Coated Bars5 25*5Concrete Deck-Rigid Overlay Protected w/Coated Ba5 265Concrete Deck -Protected w/ Coated Bars5 275Concrete Deck -Protected w/ Cathodic System6 285Steel Deck -Open Grid6 295Steel Deck -Concrete Filled Grid6 305Steel Deck -Corrugated/Orthotropic/Etc.7 314Timber Deck -Bare7 324Timber Deck -w/ AC Overlay7 35*5Precast Panel Concrete Deck -Bare6 36*5Precast Panel Concrete Deck w/ AC Overlay6 385Concrete Slab -Bare14 395Concrete Slab -Unprotected w/ AC Overlay14 405Concrete Slab -Protected w/ AC Overlay14 445Concrete Slab -Protected w/ Thin Overlay14 485Concrete Slab -Protected w/ Rigid Overlay14 525Concrete Slab -Protected w/ Coated Bars13 535Concrete Slab -Protect ed w/ Cathodic System14 544Timber Slab14 554Timber Slab -w/ AC Overlay14 605Railroad Deck6 Superstructure 1014Unpainted Steel Closed Web/Box Girder12 1025Painted Steel Clos ed Web/Box Girder12 1044P/SConc Closed Web/Box Girder12 1054Reinforced Concrete Closed Webs/Box Girder12 1064Unpainted Steel Open Girder/Beam12 1075Painted Steel Open Girder/Beam12 1094P/S Conc Open Girder/Beam12

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133 Table A.1 (Cont.)Element keyCS countElement descriptionElement weight 1104Reinforced Conc Open Girder/Beam12 1114Timber Open Girder/Beam12 1124Unpainted Steel Stringer12 1135Painted Steel Stringer11 1154P/S Conc Stringer11 1164Reinforced Conc Stringer11 1174Timber Stringer11 1204Unpainted Steel Bottom Chord Thru Truss18 1215Painted Steel Bottom Chord Thru Truss17 1254Unpainted Steel Thru Truss (excl. bottom chord)18 1265Painted Steel Thru Truss (excl. bottom chord)17 1304Unpainted Steel Deck Truss18 1315Painted Steel Deck Truss17 1354Timber Truss/Arch18 1404Unpainted Steel Arch18 1415Painted Steel Arch18 1434P/S Conc Arch18 1444Reinforced Conc Arch18 1454Other Arch18 1464Cable -Uncoated (not embedded in concrete)17 1475Cable -Coated (not embedded in concrete)17 1514Unpainted Steel Floor Beam14 1525Painted Steel Floor Beam14 154*4P/S Conc Floor Beam14 1554Reinforced Conc Floor Beam14 1564Timber Floor Beam14 1604Unpainted Steel Pin and/or Pinand Hanger Assembly20 1615Painted Steel Pin and/or Pin and Hanger Assembly20 Substructure 2014Unpainted Steel Column or Pile Extension16 2025Painted Steel Column or Pile Extension16 2044P/S Conc Column or Pile Extension16 2054Reinforced Conc Column or Pile Extension16 2064Timber Column or Pile Extension16 2104Reinforced Conc Pier Wall15 2114Other Material Pier Wall15

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134 Table A.1 (Cont.)Element keyCS countElement descriptionElement weight 2154Reinforced Conc Abutment12 2164Timber Abutment12 2174Other Material Abutment12 2204Reinforced Conc Submerged Pile Cap/Footing8 221*4Reinforced Conc Pile Cap/Footing8 2254Unpainted Steel Submerged Pile7 2264P/S Conc Submerged Pile7 2274Reinforced Conc Submerged Pile7 2284Timber Submerged Pile7 2304Unpainted Steel Cap15 2315Painted Steel Cap15 2334P/S Conc Cap15 2344Reinforced Conc Cap15 2354Timber Cap15 Culverts 2404Unpainted Steel Culvert6 2414Reinforced ConcreteCulvert6 2424Timber Culvert6 2434Other Culvert6 Miscellaneous 3003Strip Seal Expansion Joint7 3013Pourable Joint Seal7 3023Compression Joint Seal7 3043Open Expansion Joint7 305*3Elastomeric Flex-Type Joint7 306*3Asphaltic Plug Expansion Device7 307*3Modular Expansion Joint7 308*3Construction/Non-Expansion Joint7 309*3Elastomeric Bearing with Teflon6 3103Elastomeric Bearing6 3113Moveable Bearing (roller, sliding, etc.)6 3133Fixed Bearing6 3143Pot Bearing6 3153Disk Bearing6 3204P/S Concrete Approach Slab w/ or w-o/AC Ovly2 3214Reinforced Conc Approach Slab w/ or w/o AC Ovly2

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135 Table A.1 (Cont.)Element keyCS countElement descriptionElement weight 325*3Slope, Slope Protection, Berms0 326*3Bridge WIngwalls4 327*3Culvert Wingwalls3 3304Metal Bridge Railing -Uncoated2 3314Reinforced Conc Bridge Railing2 3323Timber Bridge Railing2 3333Other Bridge Railing2 3345Metal Bridge Railing -Coated2 335*3Culvert Headwalls3 336*5Metal Curbs/Sidewalks -Coated1 337*4Metal Curb/Sidewalk -Uncoated1 338*4Concrete Curbs/Sidewalks1 339*4Timber Curbs/Sidewalks1 340*3Concrete Coating (Superstructure)0 341*3Substructure Concrete Coating0 342*4Sign Attachment to Bridge1 343*4Pole Attachment to Bridge1 350*4T unnel (Formed Concrete Lined)10 351*4T unnel (Unlined/Unsu pported)12 352*4T unnel (Unlined/Su pported)10 353*4T unnel (Shotcrete Lined)12 Smartflags 355*3Steel Diaphragms Smart Flag1 3563Steel Fatigue1 3574Pack Rust1 3584Deck Cracking1 3595Soffit of Concrete Deck or Slab1 3603Settlement1 3613Scour1 3623Traffic Impact1 370*3Traffic Impact (Substructure) Smart Flag1 371*3Traffic Impact (Deck) Smart Flag1 372*3False Bent/Temporary Support Smart Flag1 373*4Substructure Pack Rust Smart Flag1 399*5Alkali-Silica Reactivity (ASR) Smart Flag1 Channel / Roadway Alignment / General Remarks

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136 Table A.1 (Cont.)Element keyCS countElement descriptionElement weight 501*3Channel Condition2 502*3Channel Prot. Material and Condition2 504*3Bank Condition1 505*3Debris Smart Flag1 510*3Waterway Adequacy3 520*3Approach Roadway Alignment1 600*5General Remarks0 The asterisk which follows the element key indicates it was created by CDOT.

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137 APPENDIXB Pontis BHI for162Major Bridges in CCD from2000 to 2006 This appendix presents the Table B.1 contai ning failure cost-based BHI and repair cost-based BHI for 162majorbridges in CCD which have complete inspection data from 2000 to 2006.

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138 TableB.1Failure cost-based BHIand repa ir cost-based BHIfor 162 major CCD bridges (2000-2006)Failure cost-based BHIR epair cost-based BHI Bridge key20002002200420062000200220042006 D-01-CC-014 100.099.099.098.199.997.997.996.8 D-01-CC-017100.0100.099.199.1100.0100.097.897.8 D-01-CC-019100.099.498.498.4100.099.897.497.4 D-01-CC-020100.0100.098.292.5100.0100.098.783.4 D-01-CC-030A100.099.499.899.7100.092.4100.099.9 D-01-CC-060100.099.999.999.9100.099.999.999.9 D-01-CC-08098.795.595.496.578.578.278.199.3 D-01-CC-10098.598.5100.0100.063.163.1100.0100.0 D-01-CC-110A100.098.5100.099.9100.073.2100.099.9 D-01-CC-14099.799.699.599.399.999.899.699.4 D-01-CC-150100.097.898.7100.0100.056.078.0100.0 D-01-CC-16099.999.999.999.999.999.999.999.9 D-01-CC-19094.689.089.089.088.075.075.075.0 D-01-CC-20098.898.498.598.497.097.097.097.0 D-01-CC-210A99.799.799.799.692.992.992.992.8 D-01-CC-220A100.0100.0100.099.599.999.999.987.0 D-01-CC-230A99.798.598.598.499.978.178.178.1 D-01-CC-240A100.0100.0100.0100.099.999.999.999.9 D-01-CC-25099.999.999.999.999.999.899.899.8 D-01-CC-260A100.0100.0100.0100.0100.0100.0100.0100.0 D-01-CC-27096.496.387.486.798.598.584.984.6 D-01-CC-28198.328.728.727.998.246.646.645.6 D-01-CC-282A98.297.397.398.763.351.051.099.6 D-01-CC-31069.469.367.467.484.083.983.683.6 D-01-CC-32089.889.889.685.596.296.295.783.6 D-02-PR-01099.799.799.799.799.899.899.899.9 D-02-PR-03599.099.399.399.098.299.499.499.1 D-02-PR-04098.996.898.498.199.374.799.098.5 D-02-PR-05099.593.198.898.899.787.799.299.1 D-02-PR-062100.099.399.398.2100.089.389.388.6 D-02-PR-070A94.592.192.092.499.487.987.888.0 D-02-PR-090A100.0100.099.789.2100.0100.099.697.2 D-02-PR-100A100.0100.099.489.1100.0100.099.597.1 D-02-PR-120A 91.8100.0100.0100.056.8100.0100.0100.0 D-02-PR-130A99.899.898.297.7100.0100.078.278.6 D-02-PR-15099.897.997.593.299.891.491.289.6 D-02-PR-22099.298.298.297.787.987.287.186.7 D-02-PR-25093.786.986.986.887.975.675.675.5 D-02-PR-260A93.693.693.593.587.287.287.287.1 D-03-V-01099.399.499.398.799.599.899.799.4 D-03-V-030(A)100.093.097.996.1100.071.990.279.6 D-03-V-032A99.999.398.798.7100.099.799.499.4 D-03-V-034A99.899.198.898.6100.099.699.499.3 D-03-V-036A99.997.396.896.8100.085.384.684.6 D-03-V-038A99.899.899.299.099.9100.099.699.5 D-03-V-04699.899.899.499.1100.0100.099.599.0 D-03-V-047A100.099.498.398.6100.099.779.798.6

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139 Table B.1(Cont.)Failure cost-based BHIR epair cost-based BHI Bridge key20002002200420062000200220042006 D-03-V-051100.0100.0100.093.4100.0100.0100.098.1 D-03-V-052100.0100.0100.099.0100.0100.0100.099.6 D-03-V-05499.997.797.797.6100.099.398.898.8 D-03-V-09099.899.598.398.3100.099.698.998.9 D-03-V-15099.896.990.890.499.999.598.597.8 D-03-V-16099.094.995.495.299.698.098.197.9 D-03-V-16195.194.887.787.196.697.468.968.4 D-03-V-17098.697.597.594.298.997.997.995.4 D-03-V-18094.794.489.289.487.387.183.583.9 D-04-BOST-052100.0100.0100. 099.7100.0100.099.999.8 D-04-BOST-05595.395.292. 087.587.476.975.372.9 D-04-BOST-05699.895.492. 387.689.487.675.773.1 D-04-BOST-065A83.9100.0100. 0100.072.6100.0100.0100.0 D-04-BOST-100100.0100.0100. 0100.0100.0100.0100.0100.0 D-04-BOST-110100.098.598. 398.3100.059.058.958.9 D-04-BOST-15099.998.598. 598.599.699.299.298.7 D-04-BOST-151100.099.499. 499.3100.099.899.898.8 D-04-BOST-16099.999.999. 999.999.999.999.999.9 D-04-BOST-161100.0100.0100. 0100.0100.0100.0100.0100.0 D-05-RRBR-105100.0100.0100.0 97.0100.0100.0100.079.1 D-05-RRBR-110100.0100.0100.0100.0100.0100.0100.0100.0 D-05-RRBR-120100.0100.0100.0 100.0100.099.9100.0100.0 D-05-RRBR-131A100.0100.0100.0 97.2100.0100.0100.078.6 D-05-RRBR-134A100.0100.0100.0 100.0100.0100.0100.0100.0 D-07-PO-180100.0100.099.9 99.9100.0100.099.899.8 D-07-PO-200100.099.899.8 99.8100.099.899.899.8 D-07-PO-350100.0100.099.9 99.9100.0100.0100.0100.0 D-07-PO-360100.0100.0100.0 100.0100.0100.0100.0100.0 D-10-HC-295100.099.999.999.9100.099.999.899.9 D-10-HC-326100.0100.0100.084.2100.0100.0100.087.2 D-10-HC-33097.297.299.299.292.892.899.699.5 D-11-GG-005100.0100.0100.0100.0100.0100.0100.099.8 D-11-GG-010A67.067.067.059.167.567.267.559.3 D-11-GG-04098.198.198.198.196.396.396.396.3 D-16-LG-070A100.0100.0100.099.8100.0100.0100.099.5 D-16-LG-12082.982.782.779.282.982.582.478.8 D-16-LG-13097.599.199.199.199.099.599.599.4 D-17-WG-02095.094.494.094.095.194.594.194.1 D-17-WG-03099.099.098.598.599.099.098.598.5 D-17-WG-04098.398.398.398.398.498.498.498.4 D-17-WG-05098.298.298.098.098.398.298.198.1 D-17-WG-07097.897.896.996.997.997.997.197.1 D-17-WG-08099.699.899.499.499.699.899.599.5 D-17-WG-09096.396.395.995.597.096.996.195.3 D-17-WG-100100.0100.090.793.8100.0100.077.585.0 D-17-WG-11099.299.298.697.699.399.398.897.9 D-17-WG-15091.791.791.489.991.791.791.489.9 D-17-WG-18096.997.390.890.596.997.689.489.1

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140 Table B.1(Cont.)Failure cost-based BHIR epair cost-based BHI Bridge key20002002200420062000200220042006 D-17-WG-20097.666.466.065.897.366.666.566.3 D-17-WG-21192.990.590.789.493.090.790.989.6 D-19-SC-01024.024.024.024.051.150.950.950.5 D-19-SC-050A97.492.392.390.499.297.997.997.2 D-20-MB-332100.0100.0100.0100.0100.0100.0100.0100.0 D-20-MB-43099.798.198.198.099.898.298.297.4 D-20-MB-640100.097.797.797.6100.097.797.897.4 D-20-MB-69296.795.695.693.596.995.795.693.7 D-20-MB-78599.899.699.599.599.799.599.499.4 D-20-MB-79099.498.998.970.599.699.199.174.0 D-25-FC-01042.642.642.541.783.683.683.476.6 D-25-FC-020A100.0100.0100.0 100.0100.0100.0100.0100.0 D-25-FC-025100.099.999.999.9100.099.799.799.7 D-25-FC-040100.0100.0100.099.6100.0100.0100.099.3 D-26-SWG-00597.197.397.096.296.997.096.796.0 D-26-SWG-01097.585.985.197.297.186.485.696.8 D-26-SWG-01594.295.594.993.294.395.695.093.3 D-26-SWG-03494.194.093.392.494.593.993.392.5 D-26-SWG-100A96.396.396.396.396.596.396.596.0 D-27-MP-01061.849.749.849.879.074.371.771.7 D-27-MP-030A99.299.295.195.398.698.691.691.8 D-27-MP-07068.769.269.668.765.747.663.962.9 D-27-MP-09072.872.867.667.665.965.942.742.7 D-27-MP-100100.0100.0100.0100.0100.0100.0100.099.9 D-31-PB-10099.099.099.099.099.199.199.199.1 D-31-PB-16099.399.298.497.8100.099.898.798.4 D-31-PB-20099.499.398.798.7100.099.898.898.8 D-31-PB-21099.499.398.898.6100.099.898.998.8 D-31-PB-22099.199.197.897.899.999.997.997.9 D-31-PB-23099.299.297.797.7100.0100.097.997.9 D-31-PB-31099.299.298.898.8100.0100.099.299.1 D-31-PB-32099.299.398.295.5100.099.999.598.7 D-31-PB-35099.499.596.996.9100.0100.084.484.4 D-31-PB-36099.497.098.998.9100.084.899.199.1 D-31-PB-41099.499.197.797.3100.099.899.198.9 D-31-PB-42099.699.699.699.6100.0100.0100.0100.0 D-31-PB-44098.898.398.396.898.898.398.396.8 D-31-PB-46098.899.198.197.798.999.999.699.4 D-31-PB-470100.099.199.198.3100.099.999.999.5 D-31-PB-51099.599.599.598.699.599.599.598.6 D-31-PB-53099.198.297.998.399.499.399.299.3 D-31-PB-54099.599.097.697.6100.099.999.099.0 D-31-PB-62099.599.498.299.399.599.298.099.2 D-31-PB-630100.0100.0100.099.4100.0100.0100.099.4 D-31-PB-65099.499.297.597.5100.099.898.098.0 D-31-PB-68099.599.795.896.999.599.585.498.6 D-31-PB-69099.999.999.899.899.899.899.599.5 D-05-PB-721100.0100.099.999.8100.0100.099.999.8

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141 Table B.1(Cont.)Failure cost-based BHIR epair cost-based BHI Bridge key20002002200420062000200220042006 D-31-PB-72299.999.999.799.6100.0100.0100.0100.0 D-31-PB-72399.799.796.799.4100.0100.076.399.5 D-31-PB-73199.699.599.097.499.999.999.098.0 D-31-PB-73299.699.699.497.8100.0100.099.798.7 D-31-PB-73399.699.698.697.599.999.998.898.7 D-31-PB-75099.599.599.599.5100.0100.0100.099.9 D-31-PB-78199.499.494.793.5100.0100.097.995.2 D-31-PB-78299.999.999.599.9100.0100.099.5100.0 D-31-PB-78399.699.599.598.9100.099.799.798.6 D-31-PB-82199.799.295.699.6100.099.198.899.7 D-31-PB-82399.799.799.499.0100.0100.099.599.3 D-31-PB-831100.099.899.398.6100.099.799.899.7 D-31-PB-83299.998.998.698.8100.099.799.499.4 D-31-PB-83399.499.496.496.4100.0100.097.797.7 D-31-PB-85099.599.598.197.9100.0100.089.588.9 D-31-PB-88199.699.698.496.1100.0100.090.587.9 D-31-PB-88299.699.699.397.1100.0100.099.989.6 D-31-PB-88398.999.692.991.099.8100.087.685.8 D-31-PB-900100.0100.0100.0 100.0100.0100.0100.0100.0

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142 APPENDIX C DBHI for 162 Major Bridges in CCD from 2000 to 2006 This appendix presents Table C.1 contai ning DBHI for 162 major bridges in CCD which have compete inspec tion data from 2000 to 2006.

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143 Table C.1DBHIfor 162 major CCD bridges (2000-2006)Bridge key2000200220042006 D-01-CC-01499.671.771.765.5 D-01-CC-017100.0100.052.052.0 D-01-CC-019100.098.451.751.7 D-01-CC-020100.0100.099.187.5 D-01-CC-030A100.089.999.899.8 D-01-CC-060100.098.998.998.9 D-01-CC-08079.377.677.596.9 D-01-CC-10054.054.0100.0100.0 D-01-CC-110A100.054.5100.099.9 D-01-CC-14097.196.896.295.4 D-01-CC-150100.049.176.899.9 D-01-CC-16099.499.499.499.4 D-01-CC-19060.759.059.059.0 D-01-CC-20097.397.197.197.1 D-01-CC-210A90.190.090.074.9 D-01-CC-220A99.699.699.684.0 D-01-CC-230A98.774.874.874.6 D-01-CC-240A99.799.799.799.7 D-01-CC-25099.699.599.699.7 D-01-CC-260A100.0100.0100.0100.0 D-01-CC-27094.393.183.882.9 D-01-CC-28158.026.525.222.9 D-01-CC-282A50.746.146.194.5 D-01-CC-31043.943.742.942.9 D-01-CC-32075.175.159.357.9 D-02-PR-01099.199.199.199.3 D-02-PR-03527.099.599.599.2 D-02-PR-04092.446.569.863.4 D-02-PR-05096.062.865.064.7 D-02-PR-062100.092.392.361.0 D-02-PR-070A94.880.480.381.6 D-02-PR-090A100.0100.068.164.5 D-02-PR-100A100.0100.068.064.5 D-02-PR-120A46.3100.0100.0100.0 D-02-PR-130A99.999.980.481.5 D-02-PR-15099.388.888.687.0 D-02-PR-22092.970.870.069.5 D-02-PR-25088.759.059.058.8 D-02-PR-260A87.187.087.086.9 D-03-V-01094.177.268.753.0 D-03-V-030(A)99.866.158.057.2 D-03-V-032A99.499.299.199.1 D-03-V-034A99.289.789.589.5 D-03-V-036A99.891.762.162.1 D-03-V-038A99.199.163.763.6 D-03-V-04691.490.162.262.0 D-03-V-047A100.098.280.058.6 D-03-V-05190.790.790.785.8 D-03-V-05290.790.790.790.2

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144 Table C.1(Cont.)Bridge key2000200220042006 D-03-V-05487.286.674.374.2 D-03-V-09090.484.756.756.6 D-03-V-15099.276.356.956.6 D-03-V-16090.244.751.651.5 D-03-V-16162.853.672.271.8 D-03-V-17099.181.681.680.1 D-03-V-18072.672.155.869.6 D-04-BOST-052100.0100.099.998.7 D-04-BOST-05580.269.053.552.9 D-04-BOST-05686.383.354.453.6 D-04-BOST-065A42.9100.0100.0100.0 D-04-BOST-10099.999.999.999.9 D-04-BOST-110100.046.346.346.2 D-04-BOST-15087.687.187.462.7 D-04-BOST-15199.999.298.462.6 D-04-BOST-16099.799.799.799.7 D-04-BOST-161100.0100.0100.0100.0 D-05-RRBR-105100.0100.0100.090.9 D-05-RRBR-110100.0100.0100.0100.0 D-05-RRBR-12099.199.0100.099.9 D-05-RRBR-131A100.0100.0100.086.2 D-05-RRBR-134A100.0100.0100.0100.0 D-07-PO-180100.0100.099.999.9 D-07-PO-200100.083.183.183.1 D-07-PO-350100.0100.099.499.4 D-07-PO-360100.0100.0100.0100.0 D-10-HC-29599.998.398.198.2 D-10-HC-326100.0100.0100.092.5 D-10-HC-33083.983.998.490.2 D-11-GG-005100.0100.0100.098.7 D-11-GG-010A43.543.543.545.8 D-11-GG-04085.585.585.585.5 D-16-LG-070A100.0100.0100.095.7 D-16-LG-12063.862.562.560.1 D-16-LG-13095.298.598.598.5 D-17-WG-02095.595.094.794.7 D-17-WG-03099.199.198.698.6 D-17-WG-04098.598.598.598.5 D-17-WG-05098.798.798.598.5 D-17-WG-07098.398.397.597.5 D-17-WG-08099.799.899.699.6 D-17-WG-09097.397.393.079.1 D-17-WG-100100.0100.050.355.9 D-17-WG-11099.499.499.098.3 D-17-WG-15090.190.189.988.9 D-17-WG-18096.098.349.349.2 D-17-WG-20077.845.845.545.3 D-17-WG-21194.793.593.693.1 D-19-SC-01054.053.553.545.5

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145 Table C.1(Cont.)Bridge key2000200220042006 D-19-SC-050A78.866.966.844.3 D-20-MB-332100.0100.0100.0100.0 D-20-MB-43099.798.298.281.7 D-20-MB-640100.098.298.295.3 D-20-MB-69297.478.678.677.5 D-20-MB-78594.377.176.676.6 D-20-MB-79095.581.281.242.7 D-25-FC-01015.115.115.115.8 D-25-FC-020A100.0100.0100.0100.0 D-25-FC-025100.090.890.890.8 D-25-FC-040100.0100.0100.099.5 D-26-SWG-00584.273.072.972.6 D-26-SWG-01077.071.170.776.8 D-26-SWG-01595.596.596.094.8 D-26-SWG-03495.193.393.092.5 D-26-SWG-100A97.192.897.194.2 D-27-MP-01031.323.823.623.6 D-27-MP-030A97.997.951.351.3 D-27-MP-07037.631.236.036.1 D-27-MP-09040.240.230.830.8 D-27-MP-100100.099.999.999.9 D-31-PB-10099.399.399.399.3 D-31-PB-16089.188.980.979.7 D-31-PB-20089.289.081.781.7 D-31-PB-21089.289.083.583.1 D-31-PB-22086.486.478.178.1 D-31-PB-23086.886.877.177.1 D-31-PB-31085.785.784.383.0 D-31-PB-32085.787.084.376.2 D-31-PB-35088.489.382.182.1 D-31-PB-36088.481.360.560.5 D-31-PB-41084.384.465.859.2 D-31-PB-42085.785.785.784.5 D-31-PB-44094.078.178.177.3 D-31-PB-46053.199.083.080.2 D-31-PB-470100.099.099.095.5 D-31-PB-51099.699.699.698.9 D-31-PB-53077.376.754.555.3 D-31-PB-54085.785.376.276.2 D-31-PB-62099.594.593.494.5 D-31-PB-630100.0100.0100.099.6 D-31-PB-65086.886.755.055.0 D-31-PB-68057.957.844.761.5 D-31-PB-69095.095.078.978.9 D-31-PB-721100.0100.099.499.0 D-31-PB-72291.991.987.885.1 D-31-PB-72391.392.371.386.6 D-31-PB-73190.190.182.079.5 D-31-PB-73290.190.189.486.3

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146 Table C.1(Cont.)Bridge key2000200220042006 D-31-PB-73390.190.181.586.4 D-31-PB-75089.789.789.689.6 D-31-PB-78190.790.762.450.1 D-31-PB-78291.292.384.792.3 D-31-PB-78391.391.591.581.3 D-31-PB-82191.381.877.486.1 D-31-PB-82391.392.387.186.6 D-31-PB-831100.099.298.890.7 D-31-PB-83291.268.163.659.6 D-31-PB-83390.790.779.579.5 D-31-PB-85090.190.161.057.5 D-31-PB-88190.190.183.758.8 D-31-PB-88290.190.189.781.4 D-31-PB-88386.890.178.664.0 D-31-PB-900100.0100.0100.0100.0

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147 APPENDIX D The 350 Lowest-EHI Elements under Each Element Category in CCD Major Bridge Network This appendix presents Table D.1 containi ng 350 lowest-EHI elements with 3 CSs in CCD major bridge network, Table D.2 cont aining 350 lowest-EHI elements with 4 CSs in CCD major bridge network, andTable D.3 containing 350 lowest-EHI elements with 5 CSsin CCD major bridge network.

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148 Table D.1 The 350 lowest-EHI elements wi th 3 CSs in CCD major bridge network Element key Element description Bridge key Inspection date EHI CS count 308 Constr Non Exp Jt F-16-DX 3/2/2006 0 3 333 Other Bridge Railing D-17-WG-100 1/13/2011 0 3 313 Fixed Bearing F-16-EJ 12/22/2005 0 3 300 Strip Seal Exp Joint D-19-SC-050A 1/11/2011 0 3 313 Fixed Bearing D-10-HC-170 1/12/2011 0 3 305 Elastomeric Flex Jt F-16-JX 1/5/2006 0 3 308 Constr Non Exp Jt F-16-DQ 2/22/2006 0 3 308 Constr Non Exp Jt F-16-DU 12/22/2005 0 3 308 Constr Non Exp Jt F-16-EN 1/29/2008 0 3 302 Compressn Joint Seal D-04-BOST-040 11/1/2010 0 3 302 Compressn Joint Seal D-04-BOST-030 11/1/2010 0 3 311 Moveable Bearing F-16-BM 6/13/2007 0 3 300 Strip Seal Exp Joint D-31-PB-150 12/29/2010 0.001 3 301 Pourable Joint Seal D-19-SC-010 10/21/2008 0.004 3 302 Compressn Joint Seal F-16-NZ 1/25/2006 3.5616 3 326 Bridge Wingwalls F-16-BM 6/13/2007 5 3 326 Bridge Wingwalls D-25-FC-010 11/15/2006 5 3 305 Elastomeric Flex Jt F-16-PM 9/6/2006 8 3 301 Pourable Joint Seal E-16-EM 6/29/2006 8.6528 3 300 Strip Seal Exp Joint D-31-PB-850 12/14/2010 10 3 302 Compressn Joint Seal E-16-EP 6/16/2006 10 3 302 Compressn Joint Seal D-02-PR-150 10/18/2010 10.204 3 311 Moveable Bearing D-10-HC-170 1/12/2011 13.3334 3 300 Strip Seal Exp Joint F-16-OB 3/7/2006 14.1176 3 302 Compressn Joint Seal D-31-PB-833 12/13/2010 14.598 3 333 Other Bridge Railing D-20-MB-785 1/11/2011 15.192 3 333 Other Bridge Railing E-17-AH 1/9/2007 15.2554 3 314 Pot Bearing D-02-PR-040 10/7/2010 16 3 307 Modular Expansion Jt D-03-V-045(A) 10/20/2010 16.1738 3 300 Strip Seal Exp Joint D-04-BOST-151 11/4/2010 16.25 3 301 Pourable Joint Seal D-31-PB-220 12/16/2010 16.3636 3 304 Open Expansion Joint F-16-DX 3/2/2006 17.9 3 301 Pourable Joint Seal D-31-PB-200 12/16/2010 18.373 3 304 Open Expansion Joint F-16-BM 6/13/2007 18.767218 3 311 Moveable Bearing F-16-EF 1/25/2006 18.8406 3 300 Strip Seal Exp Joint D-02-PR-062 10/7/2010 19.0566 3 333 Other Bridge Railing D-19-SC-050A 1/11/2011 19.9002 3 300 Strip Seal Exp Joint D-01-CC-019 10/6/2010 19.9986 3 300 Strip Seal Exp Joint D-03-V-051 1/26/2011 19.9994 3 300 Strip Seal Exp Joint D-06-BORR-040 1/26/2011 19.9998 3 300 Strip Seal Exp Joint D-31-PB-200 12/16/2010 19.9998 3 300 Strip Seal Exp Joint D-01-CC-017 10/6/2010 19.9998 3 301 Pourable Joint Seal D-04-BOST-055 11/15/2010 19.9998 3 301 Pourable Joint Seal D-04-BOST-056 11/15/2010 19.9998 3 301 Pourable Joint Seal D-31-PB-320 12/15/2010 19.9998 3 311 Moveable Bearing D-01-CC-020 10/6/2010 20 3 300 Strip Seal Exp Joint D-31-PB-650 12/14/2010 20 3 301 Pourable Joint Seal D-31-PB-650 12/14/2010 20 3 308 Constr Non Exp Jt E-17-FX 11/26/2007 20 3 301 Pourable Joint Seal D-31-PB-540 12/16/2010 20 3 313 Fixed Bearing D-05-RRBR-070A 11/16/2010 20 3 311 Moveable Bearing D-02-PR-150 10/18/2010 20 3 313 Fixed Bearing D-04-BOST-041 11/1/2010 20 3 300 Strip Seal Exp Joint F-16-NZ 1/25/2006 20 3 313 Fixed Bearing D-02-PR-150 10/18/2010 20 3 300 Strip Seal Exp Joint D-31-PB-540 12/16/2010 20 3

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149 Table D.1(Cont.) Element key Element description Bridge key Inspectiondate EHI CS count 310 Elastomeric Bearing D-04-BOST-041 11/1/2010 20 3 311 Moveable Bearing D-04-BOST-041 11/1/2010 20 3 308 Constr Non Exp Jt E-16-EW 6/2/2006 20 3 300 Strip Seal Exp Joint D-31-PB-360 12/16/2010 20 3 301 Pourable Joint Seal D-31-PB-460 12/15/2010 20 3 304 Open Expansion Joint F-16-GG 12/21/2005 20 3 302 Compressn Joint Seal D-31-PB-530 12/15/2010 20 3 302 Compressn Joint Seal D-31-PB-420 12/16/2010 20 3 308 Constr Non Exp Jt F-17-FZ 6/26/2007 20 3 308 Constr Non Exp Jt F-16-FI 2/15/2006 20 3 301 Pourable Joint Seal D-31-PB-470 12/16/2010 20 3 302 Compressn Joint Seal D-31-PB-360 12/16/2010 20 3 333 Other Bridge Railing D-01-CC-230A 10/4/2010 20 3 314 Pot Bearing F-16-NO 7/23/2007 20 3 300 Strip Seal Exp Joint E-17-FX 11/26/2007 20 3 326 Bridge Wingwalls E-17-EQ 8/2/2006 20 3 333 Other Bridge Railing E-17-AI 8/10/2006 20 3 302 Compressn Joint Seal D-31-PB-832 12/13/2010 20 3 333 Other Bridge Railing D-20-MB-790 1/11/2011 20 3 308 Constr Non Exp Jt F-16-DW 2/9/2006 20 3 333 Other Bridge Railing D-05-RRBR-091 11/16/2010 20 3 333 Other Bridge Railing D-05-RRBR-092 11/16/2010 20 3 333 Other Bridge Railing D-03-V-054 10/21/2010 20 3 327 Culvert Wingwalls D-12-SG-090 1/12/2011 20 3 335 Culvert Headwalls D-11-GG-080 1/12/2011 20 3 327 Culvert Wingwalls D-25-FC-033 11/29/2010 20 3 333 Other Bridge Railing D-01-CC-014 10/6/2010 20 3 326 Bridge Wingwalls E-16-NW 6/14/2006 20 3 310 Elastomeric Bearing E-16-EP 6/16/2006 20 3 326 Bridge Wingwalls D-05-RRBR-097 11/16/2010 20 3 326 Bridge Wingwalls D-04-BOST-050 11/1/2010 20 3 326 Bridge Wingwalls D-01-CC-080 10/6/2010 20 3 326 Bridge Wingwalls D-05-RRBR-098 11/16/2010 20 3 326 Bridge Wingwalls D-03-V-161 1/26/2011 20 3 326 Bridge Wingwalls D-03-V-010 10/20/2010 20 3 326 Bridge Wingwalls D-03-V-180 11/4/2010 20 3 311 Moveable Bearing F-16-DW 2/9/2006 20 3 311 Moveable Bearing E-17-GE 9/27/2007 20 3 311 Moveable Bearing E-17-BY 9/27/2007 20 3 313 Fixed Bearing E-17-GE 9/27/2007 20 3 326 Bridge Wingwalls F-16-NK 3/14/2006 20 3 302 Compressn Joint Seal E-17-KR 9/26/2006 20 3 302 Compressn Joint Seal D-03-V-054 10/21/2010 20 3 300 Strip Seal Exp Joint D-02-PR-070A 10/6/2010 20 3 300 Strip Seal Exp Joint D-02-PR-090A 10/18/2010 20 3 308 Constr Non Exp Jt D-11-GG-200 12/2/2010 20 3 307 Modular Expansion Jt D-02-PR-090A 10/18/2010 20 3 307 Modular Expansion Jt D-02-PR-100A 10/18/2010 20 3 301 Pourable Joint Seal D-31-PB-230 12/16/2010 20 3 308 Constr Non Exp Jt D-01-CC-070 10/6/2010 20 3 308 Constr Non Exp Jt D-05-RRBR-020B 11/17/2010 20 3 300 Strip Seal Exp Joint D-28-MS-155 11/18/2010 20 3 300 Strip Seal Exp Joint D-03-V-036A 1/24/2011 20 3 300 Strip Seal Exp Joint D-03-V-052 1/27/2011 20 3 300 Strip Seal Exp Joint D-03-V-230 10/4/2006 20 3 300 Strip Seal Exp Joint D-04-BOST-056 11/15/2010 20 3

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150 Table D.1(Cont.) Element key Element description Bridge key Inspection date EHI CS count 302 Compressn Joint Seal D-01-CC-281 10/4/2010 20 3 300 Strip Seal Exp Joint D-01-CC-282A 10/4/2010 20 3 308 Constr NonExp Jt D-05-RRBR-020A 11/17/2010 20 3 300 Strip Seal Exp Joint D-28-MS-150 11/18/2010 20 3 300 Strip Seal Exp Joint D-20-MB-790 1/11/2011 20 3 300 Strip Seal Exp Joint D-02-PR-100A 10/18/2010 20 3 300 Strip Seal Exp Joint D-21-WYC-070 11/29/2010 20 3 300 Strip Seal Exp Joint D-31-PB-160 12/16/2010 20 3 300 Strip Seal Exp Joint D-01-CC-320 10/4/2010 20 3 300 Strip Seal Exp Joint D-04-BOST-055 11/15/2010 20 3 308 Constr Non Exp Jt F-17-CG 11/8/2005 20 3 300 Strip Seal Exp Joint D-01-CC-333 10/4/2010 20 3 300 Strip Seal Exp Joint D-31-PB-781 12/13/2010 20 3 308 Constr Non Exp Jt F-16-DS 6/1/2006 20 3 300 Strip Seal Exp Joint D-31-PB-750 12/14/2010 20 3 308 Constr Non Exp Jt F-16-DT 2/8/2006 20 3 300 Strip Seal Exp Joint D-31-PB-733 12/14/2010 20 3 300 Strip Seal Exp Joint D-31-PB-732 12/14/2010 20 3 300 Strip Seal Exp Joint D-01-CC-332 10/4/2010 20 3 300 Strip Seal Exp Joint D-31-PB-731 12/14/2010 20 3 302 Compressn Joint Seal D-31-PB-781 12/13/2010 20 3 308 Constr Non Exp Jt F-16-EG 3/23/2006 20 3 300 Strip Seal Exp Joint D-21-WYC-081 11/29/2010 20 3 300 Strip Seal Exp Joint D-31-PB-881 12/14/2010 20 3 307 Modular Expansion Jt D-03-V-030(A) 1/24/2011 20 3 300 Strip Seal Exp Joint D-03-V-046 10/21/2010 20 3 308 Constr Non Exp Jt D-16-LG-160 10/17/2003 20 3 301 Pourable Joint Seal D-03-V-054 10/21/2010 20 3 300 Strip Seal Exp Joint D-04-BOST-045 11/15/2010 20 3 300 Strip Seal Exp Joint D-02-PR-040 10/7/2010 20 3 300 Strip Seal Exp Joint D-03-V-047A 10/21/2010 20 3 301 Pourable Joint Seal D-03-V-046 10/21/2010 20 3 300 Strip Seal Exp Joint D-01-CC-080 10/6/2010 20 3 300 Strip Seal Exp Joint D-31-PB-210 12/16/2010 20 3 300 Strip Seal Exp Joint D-02-PR-050 10/7/2010 20 3 308 Constr Non Exp Jt F-16-FW 4/10/2007 20 3 301 Pourable Joint Seal D-31-PB-850 12/14/2010 20.0008 3 302 Compressn Joint Seal D-31-PB-320 12/15/2010 20.0008 3 308 Constr Non Exp Jt E-16-EP 6/16/2006 20.4376 3 308 Constr Non Exp Jt F-16-BM 6/13/2007 26.4463786 3 302 Compressn Joint Seal E-17-FX 11/26/2007 26.6878 3 333 Other Bridge Railing E-16-OO 2/28/2006 29.286 3 307 Modular Expansion Jt D-03-V-200 10/21/2010 29.6 3 300 Strip Seal Exp Joint D-31-PB-721 12/13/2010 29.7422 3 311 Moveable Bearing F-16-DT 2/8/2006 30.64 3 314 Pot Bearing D-03-V-150A 11/2/2010 32.33 3 305 Elastomeric Flex Jt D-28-MS-130 1/27/2011 32.5 3 313 Fixed Bearing E-17-BY 9/27/2007 32.56 3 306 Asphaltic Plg Exp Jt D-31-PB-410 12/15/2010 32.6312 3 311 Moveable Bearing F-16-EJ 12/22/2005 33 3 311 Moveable Bearing F-16-DX 3/2/2006 34.4 3 333 Other Bridge Railing D-03-V-051 1/26/2011 36.5288 3 300 Strip Seal Exp Joint D-31-PB-783 12/13/2010 37 3 308 Constr Non Exp Jt F-17-GL 6/21/2007 38.4616 3 306 Asphaltic Plg Exp Jt D-31-PB-460 12/15/2010 38.9088 3 306 Asphaltic Plg Exp Jt E-17-FX 11/26/2007 39.38 3

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151 Table D.1(Cont.) Element key Element description Bridge key Inspection date EHI CS count 327 Culvert Wingwalls D-13-HGE-240 11/30/2010 40 3 304 Open Expansion Joint E-17-AH 1/9/2007 40 3 326 Bridge Wingwalls D-02-PR-250 10/7/2010 40 3 326 Bridge Wingwalls D-01-CC-210A 10/5/2010 40 3 326 Bridge Wingwalls E-17-PC 12/3/2007 40 3 326 Bridge Wingwalls F-16-DP 7/25/2007 40 3 327 Culvert Wingwalls D-10-HC-280 12/2/2010 40 3 327 Culvert Wingwalls D-11-GG-130 12/2/2010 40 3 304 Open Expansion Joint F-16-DW 2/9/2006 40.769 3 333 Other Bridge Railing D-10-HC-240 12/2/2010 42.6096 3 333 Other Bridge Railing D-03-V-045(A) 10/20/2010 43.2936 3 302 Compressn Joint Seal D-31-PB-831 12/13/2010 44.764 3 301 Pourable Joint Seal E-17-JP 12/13/2007 44.8272 3 326 Bridge Wingwalls D-03-V-170 11/2/2010 46.664 3 311 Moveable Bearing E-17-AH 1/9/2007 49.6 3 302 Compressn Joint Seal D-01-CC-014 10/6/2010 50 3 335 Culvert Headwalls F-16-AP 6/1/2006 50 3 301 Pourable Joint Seal D-02-PR-062 10/7/2010 50 3 308 Constr Non Exp Jt D-19-SC-010 10/21/2008 50 3 300 Strip Seal Exp Joint D-07-PO-022 1/14/2011 50.002 3 306 Asphaltic Plg Exp Jt D-31-PB-470 12/16/2010 50.5456 3 301 Pourable Joint Seal D-04-BOST-052 11/16/2010 50.908 3 300 Strip SealExp Joint D-31-PB-230 12/16/2010 51.5152 3 300 Strip Seal Exp Joint D-31-PB-833 12/13/2010 52.5 3 313 Fixed Bearing F-16-IK 9/6/2006 55 3 333 Other Bridge Railing D-10-HC-261 12/2/2010 55.1622 3 307 Modular Expansion Jt D-03-V-210 10/21/2010 56.0792 3 333 Other Bridge Railing D-10-HC-190 12/2/2010 56.9574 3 300 Strip Seal Exp Joint D-31-PB-823 12/13/2010 57.8948 3 300 Strip Seal Exp Joint E-16-MR 7/10/2006 57.9656 3 313 Fixed Bearing F-16-DP 7/25/2007 59 3 308 Constr Non Exp Jt F-16-DA 6/16/2006 59.2304 3 302 Compressn Joint Seal D-07-PO-040 1/4/2007 59.560626 3 302 Compressn Joint Seal F-16-BI 6/1/2006 59.86476 3 327 Culvert Wingwalls D-17-WG-090 1/13/2011 60 3 326 Bridge Wingwalls E-17-BY 9/27/2007 60 3 326 Bridge Wingwalls E-17-NA 12/5/2007 60 3 326 Bridge Wingwalls E-17-AI 8/10/2006 60 3 326 Bridge Wingwalls F-16-JX 1/5/2006 60 3 326 Bridge Wingwalls F-17-AE 3/22/2007 60 3 326 Bridge Wingwalls E-17-PO 12/21/2005 60 3 326 Bridge Wingwalls F-17-CG 11/8/2005 60 3 327 Culvert Wingwalls D-26-SWG-005 12/1/2010 60 3 335 Culvert Headwalls D-16-LG-120 10/23/2008 60 3 335 Culvert Headwalls D-20-MB-692 12/29/2010 60 3 327 Culvert Wingwalls D-16-LG-120 10/23/2008 60 3 335 Culvert Headwalls D-11-GG-010A 1/12/2011 60 3 327 Culvert Wingwalls D-11-GG-080 1/12/2011 60 3 335 Culvert Headwalls D-25-FC-040 11/29/2010 60 3 327 Culvert Wingwalls D-31-PB-690 12/14/2010 60 3 327 Culvert Wingwalls D-31-PB-440 12/29/2010 60 3 326 Bridge Wingwalls D-05-RRBR-101 11/16/2010 60 3 327 Culvert Wingwalls D-17-WG-200 1/13/2011 60 3 308 Constr Non Exp Jt E-17-IQ 12/13/2007 60 3 304 Open Expansion Joint D-28-MS-130 1/27/2011 60 3 301 Pourable Joint Seal D-31-PB-150 12/29/2010 60 3

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152 Table D.1(Cont.) Element key Element description Bridge key Inspection date EHI CS count 300 Strip Seal Exp Joint D-04-BOST-038 11/1/2010 60 3 300 Strip Seal Exp Joint D-03-V-045(A) 10/20/2010 60 3 300 Strip Seal Exp Joint D-31-PB-723 12/13/2010 60 3 300 Strip Seal Exp Joint D-31-PB-883 12/14/2010 60 3 326 Bridge Wingwalls D-13-HGE-200 11/30/2010 60 3 308 Constr Non Exp Jt F-17-GQ 6/21/2007 60 3 311 Moveable Bearing D-05-RRBR-037 11/17/2010 60 3 311 Moveable Bearing F-16-IK 9/6/2006 60 3 311 Moveable Bearing E-17-HD 4/10/2006 60 3 326 Bridge Wingwalls D-10-HC-295 12/2/2010 60 3 326 Bridge Wingwalls D-27-MP-110 8/4/2000 60 3 326 Bridge Wingwalls D-01-CC-172 10/5/2010 60 3 326 Bridge Wingwalls D-05-RRBR-102 11/16/2010 60 3 326 Bridge Wingwalls D-13-HGE-270 12/1/2010 60 3 300 Strip Seal Exp Joint D-31-PB-822 12/13/2010 60 3 301 Pourable Joint Seal D-02-PR-070A 10/6/2010 60.0008 3 304 Open Expansion Joint D-02-PR-010 10/7/2010 60.0008 3 300 Strip Seal Exp Joint D-04-BOST-150 11/4/2010 61.053 3 301 Pourable Joint Seal D-04-BOST-045 11/15/2010 61.7976 3 311 Moveable Bearing E-17-FX 11/26/2007 61.8374 3 313 Fixed Bearing E-17-HD 4/10/2006 61.9996 3 301 Pourable Joint Seal D-31-PB-310 12/15/2010 62.7912 3 302 Compressn Joint Seal D-10-HC-170 1/12/2011 63.6602 3 307 Modular Expansion Jt E-16-MS 7/26/2007 66.265 3 301 Pourable Joint Seal E-16-NX 4/12/2006 66.64 3 333 Other Bridge Railing D-03-V-052 1/27/2011 66.9424 3 301 Pourable Joint Seal D-31-PB-420 12/16/2010 67.164 3 300 Strip Seal Exp Joint F-16-OK 7/23/2007 67.7 3 301 Pourable Joint Seal D-31-PB-410 12/15/2010 68.4208 3 302 Compressn Joint Seal D-31-PB-721 12/13/2010 69.2304 3 305 Elastomeric Flex Jt D-19-SC-010 10/21/2008 70 3 314 Pot Bearing E-16-PJ 3/27/2006 70 3 301 Pourable Joint Seal D-31-PB-530 12/15/2010 70.9088 3 308 Constr Non Exp Jt D-01-CC-230A 10/4/2010 72.0928 3 309 Elast. Bearng-Teflon E-17-UP 9/25/2007 72.2226 3 314 Pot Bearing E-16-OO 2/28/2006 73.3336 3 305 Elastomeric Flex Jt D-03-V-150A 11/2/2010 73.3336 3 310 Elastomeric Bearing D-03-V-180 11/4/2010 73.3336 3 311 Moveable Bearing F-16-IL 9/6/2006 73.36 3 311 Moveable Bearing F-16-EG 3/23/2006 73.36 3 313 Fixed Bearing F-16-IL 9/6/2006 73.36 3 301 Pourable Joint Seal D-31-PB-823 12/13/2010 74.0552 3 333 Other Bridge Railing D-04-BOST-150 11/4/2010 74.1784 3 301 PourableJoint Seal D-03-V-047A 10/21/2010 74.5432 3 300 Strip Seal Exp Joint D-31-PB-832 12/13/2010 75 3 308 Constr Non Exp Jt F-16-JX 1/5/2006 75.359 3 300 Strip Seal Exp Joint D-31-PB-220 12/16/2010 75.7576 3 300 Strip Seal Exp Joint F-16-PN 1/19/2005 76.092 3 313 Fixed Bearing E-17-AH 1/9/2007 76.32 3 308 Constr Non Exp Jt F-16-EC 3/2/2006 77.5 3 333 Other Bridge Railing D-03-V-046 10/21/2010 77.5668 3 307 Modular Expansion Jt D-03-V-034A 1/24/2011 77.5758 3 300 Strip Seal Exp Joint E-16-NJ 9/11/2007 77.68 3 305 Elastomeric Flex Jt D-03-V-161 1/26/2011 77.9528 3 301 Pourable Joint Seal D-31-PB-350 12/15/2010 79.0904 3 300 Strip Seal Exp Joint E-17-PB 12/21/2005 79.167 3

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153 Table D.1(Cont.) Element key Element description Bridge key Inspection date EHI CS count 301 Pourable Joint Seal D-07-PO-023 1/24/2011 79.488 3 300 Strip Seal Exp Joint E-17-NT 9/25/2007 79.52 3 326 Bridge Wingwalls D-31-PB-420 12/16/2010 80 3 326 Bridge Wingwalls E-17-AF 8/10/2006 80 3 326 Bridge Wingwalls F-16-BI 6/1/2006 80 3 326 Bridge Wingwalls F-17-FZ 6/26/2007 80 3 326 Bridge Wingwalls F-16-DW 2/9/2006 80 3 327 Culvert Wingwalls D-12-SG-130 1/12/2011 80 3 326 Bridge Wingwalls D-31-PB-310 12/15/2010 80 3 327 Culvert Wingwalls D-12-SG-160 1/12/2011 80 3 326 Bridge Wingwalls E-16-EP 6/16/2006 80 3 326 Bridge Wingwalls D-31-PB-460 12/15/2010 80 3 327 Culvert Wingwalls D-25-FC-045 11/29/2010 80 3 335 Culvert Headwalls D-17-WG-150 1/13/2011 80 3 327 Culvert Wingwalls D-25-FC-029 11/29/2010 80 3 327 Culvert Wingwalls D-31-PB-110 12/29/2010 80 3 327 Culvert Wingwalls D-11-GG-070 1/12/2011 80 3 326 Bridge Wingwalls F-16-RT 6/12/2007 80 3 326 Bridge Wingwalls E-16-FA 7/7/2006 80 3 326 Bridge Wingwalls D-05-RRBR-020B 11/17/2010 80 3 326 Bridge Wingwalls E-16-EM 6/29/2006 80 3 308 Constr Non Exp Jt F-16-DC 3/2/2006 80 3 308 Constr Non Exp Jt E-17-HY 9/20/2007 80 3 326 Bridge Wingwalls D-16-LG-160 10/17/2003 80 3 326 Bridge Wingwalls D-05-RRBR-091 11/16/2010 80 3 326 Bridge Wingwalls E-17-DF 10/9/2007 80 3 326 Bridge Wingwalls D-02-PR-050 10/7/2010 80 3 326 Bridge Wingwalls D-05-RRBR-020A 11/17/2010 80 3 326 Bridge Wingwalls D-10-HC-120 12/1/2010 80 3 326 Bridge Wingwalls D-05-RRBR-080 11/16/2010 80 3 326 Bridge Wingwalls E-17-NT 9/25/2007 80 3 326 Bridge Wingwalls D-27-MP-090 11/18/2010 80 3 326 Bridge Wingwalls D-01-CC-270 10/4/2010 80 3 326 Bridge Wingwalls D-13-HGE-070 11/30/2010 80 3 326 Bridge Wingwalls D-13-HGE-230 11/30/2010 80 3 326 Bridge Wingwalls D-10-HC-180 12/1/2010 80 3 326 Bridge Wingwalls D-27-MP-100 11/18/2010 80 3 326 Bridge Wingwalls E-17-EW 10/9/2007 80 3 333 Other Bridge Railing E-17-FX 11/26/2007 80.5874 3 301 Pourable Joint Seal E-17-UQ 9/25/2007 81.25 3 301 Pourable Joint Seal D-31-PB-160 12/16/2010 81.3952 3 307 ModularExpansion Jt F-16-RI 6/20/2007 81.68 3 301 Pourable Joint Seal E-17-US 7/19/2006 81.8184 3 305 Elastomeric Flex Jt F-16-PL 9/6/2006 82.313 3 301 Pourable Joint Seal D-31-PB-750 12/14/2010 83.5896 3 301 Pourable Joint Seal D-31-PB-783 12/13/2010 84 3 333 Other Bridge Railing D-04-BOST-151 11/4/2010 84.0376 3 333 Other Bridge Railing D-21-WYC-070 11/29/2010 84.3608 3 300 Strip Seal Exp Joint D-03-V-180 11/4/2010 85 3 310 Elastomeric Bearing D-31-PB-833 12/13/2010 85 3 301 Pourable Joint Seal E-16-NJ 9/11/2007 85.12 3 301 Pourable Joint Seal D-31-PB-360 12/16/2010 85.4544 3 301 Pourable Joint Seal E-17-PP 1/25/2008 85.7144 3 333 Other Bridge Railing D-10-HC-280 12/2/2010 85.7144 3 333 Other Bridge Railing D-26-SWG-034 12/1/2010 85.88 3 300 Strip Seal Exp Joint E-16-GC 9/20/2007 86.313 3

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154 Table D.1(Cont.) Element key Element description Bridge key Inspection date EHI CS count 314 Pot Bearing F-16-OK 7/23/2007 86.64 3 300 Strip Seal Exp Joint E-17-NU 9/25/2007 87 3 333 Other Bridge Railing F-16-RU 5/31/2007 87.2 3 300 Strip Seal Exp Joint D-08-PU-012 1/27/2011 87.7552 3 307 Modular Expansion Jt D-03-V-038A 1/24/2011 87.9168 3 300 Strip Seal Exp Joint D-06-BORR-050 1/26/2011 88.3604 3 309 Elast. Bearng-Teflon D-28-MS-130 1/27/2011 88.8004 3 302 Compressn Joint Seal D-31-PB-210 12/16/2010 88.8376 3 333 Other Bridge Railing D-01-CC-250 10/4/2010 88.872 3 309 Elast. Bearng-Teflon F-16-NZ 1/25/2006 89.36 3 333 Other Bridge Railing D-01-CC-150 10/5/2010 89.5 3 307 Modular Expansion Jt D-03-V-230 10/4/2006 89.519976 3 300 Strip Seal Exp Joint D-03-V-030(A) 1/24/2011 89.524 3 301 Pourable Joint Seal D-04-BOST-044 11/1/2010 89.5424 3

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155 Table D.2The 350 lowest-EHI elements wi th 4CSs in CCD major bridge network Element key Element description Bridge key Inspection date EHI CS count 32 Timber Deck/AC Ovly D-27-MP-090 11/18/2010 0 4 215 R/Conc Abutment D-01-CC-310 9/13/2006 8.461539 4 321 R/Conc Approach Slab D-06-BORR-050 1/26/2011 10 4 338 Conc Curbs/SW E-17-HX 9/20/2007 10 4 321 R/Conc Approach Slab D-31-PB-722 12/13/2010 10 4 240 Steel Culvert D-11-GG-010A 1/12/2011 10 4 32 Timber Deck/AC Ovly D-27-MP-010 11/18/2010 10 4 221 Conc Pile Cap/Ftg D-16-LG-160 10/17/2003 10 4 230 Unpnt Stl Cap D-25-FC-010 11/15/2006 10 4 205 R/Conc Column E-17-GA 9/27/2007 10 4 205 R/Conc Column E-16-EP 6/16/2006 10 4 321 R/Conc Approach Slab D-31-PB-832 12/13/2010 10 4 201 Unpnt Stl Column D-25-FC-010 11/15/2006 10 4 201 Unpnt Stl Column D-10-HC-160 12/1/2010 10 4 201 Unpnt Stl Column D-19-SC-030 9/19/2000 10 4 217 Other Mtl Abutment D-25-FC-010 11/15/2006 10 4 338 Conc Curbs/SW D-10-HC-160 12/1/2010 10.9211 4 234 R/Conc Cap E-17-GE 9/27/2007 13.51 4 338 Conc Curbs/SW D-01-CC-320 10/4/2010 16.099 4 215 R/Conc Abutment F-16-DT 2/8/2006 18.0769 4 234 R/Conc Cap E-17-BY 9/27/2007 18.3039 4 205 R/Conc Column E-17-GB 9/27/2007 19.99 4 205 R/Conc Column E-17-GE 9/27/2007 19.9999 4 205 R/Conc Column E-17-HT 1/18/2008 20.0032 4 240 Steel Culvert D-17-WG-180 1/13/2011 22.5677 4 240 Steel Culvert D-10-HC-326 12/2/2010 22.6088 4 106 Unpnt Stl Opn Girder D-05-RRBR-070 9/28/2000 23.717521 4 234 R/Conc Cap F-17-AE 3/22/2007 24.939718 4 144 R/ConcArch F-16-BM 6/13/2007 25 4 116 R/Conc Stringer F-16-BM 6/13/2007 25 4 221 Conc Pile Cap/Ftg D-27-MP-090 11/18/2010 25 4 240 Steel Culvert D-11-GG-190 12/2/2010 25 4 321 R/Conc Approach Slab F-17-GL 6/21/2007 25 4 321 R/Conc Approach Slab E-16-NX 4/12/2006 25 4 338 Conc Curbs/SW E-17-GD 10/9/2007 25 4 215 R/Conc Abutment F-16-DW 2/9/2006 27.88 4 338 Conc Curbs/SW E-17-AH 1/9/2007 29.416 4 205 R/Conc Column F-16-DT 2/8/2006 30 4 338 Conc Curbs/SW F-16-EJ 12/22/2005 30.3816 4 201 Unpnt Stl Column D-05-RRBR-020A 11/17/2010 32.5 4 205 R/Conc Column E-17-AH 1/9/2007 32.5 4 206 Timber Column D-10-HC-120 12/1/2010 33.076 4 338 Conc Curbs/SW F-17-AE 3/22/2007 35.920078 4 338 Conc Curbs/SW E-17-GC 10/9/2007 36.565 4 234 R/Conc Cap E-17-AI 8/10/2006 37.2731 4 205 R/Conc Column E-17-HU 11/8/2005 37.4698 4 338 Conc Curbs/SW E-17-KR 9/26/2006 37.72 4 215 R/Conc Abutment D-01-CC-320 10/4/2010 37.7434 4 221 Conc Pile Cap/Ftg D-05-RRBR-080 11/16/2010 38.125 4 210 R/Conc Pier Wall E-17-EQ 8/2/2006 38.4990161 4 241 Concrete Culvert D-17-WG-200 1/13/2011 38.8819 4 338 Conc Curbs/SW D-01-CC-282A 10/4/2010 39.1534 4 234 R/Conc Cap E-17-AH 1/9/2007 39.36 4 338 Conc Curbs/SW D-03-V-010 10/20/2010 39.5407 4 338 Conc Curbs/SW F-16-FH 1/14/2008 39.58 4 338 Conc Curbs/SW D-10-HC-120 12/1/2010 39.5947 4

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156 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 241 Concrete Culvert D-26-SWG-010 12/1/2010 39.9996 4 338 Conc Curbs/SW D-01-CC-230A 10/4/2010 39.9996 4 338 Conc Curbs/SW D-10-HC-330 12/2/2010 39.9996 4 321 R/Conc Approach Slab D-31-PB-230 12/16/2010 40 4 321 R/Conc Approach Slab D-31-PB-881 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-420 12/16/2010 40 4 321 R/Conc Approach Slab D-31-PB-410 12/15/2010 40 4 321 R/Conc Approach Slab D-31-PB-360 12/16/2010 40 4 321 R/Conc Approach Slab D-31-PB-310 12/15/2010 40 4 321 R/Conc Approach Slab D-31-PB-350 12/15/2010 40 4 321 R/Conc Approach Slab D-31-PB-530 12/15/2010 40 4 321 R/Conc Approach Slab D-31-PB-731 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-732 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-783 12/13/2010 40 4 321 R/Conc Approach Slab D-31-PB-320 12/15/2010 40 4 321 R/Conc Approach Slab D-31-PB-831 12/13/2010 40 4 321 R/Conc Approach Slab E-16-NJ 9/11/2007 40 4 321 R/Conc Approach Slab D-31-PB-781 12/13/2010 40 4 321 R/Conc Approach Slab E-16-QQ 4/12/2006 40 4 321 R/Conc Approach Slab E-17-UV 10/19/2006 40 4 331 Conc Bridge Railing E-17-IQ 12/13/2007 40 4 321 R/Conc Approach Slab E-16-PJ 3/27/2006 40 4 321 R/Conc Approach Slab D-31-PB-833 12/13/2010 40 4 321 R/Conc Approach Slab D-31-PB-850 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-540 12/16/2010 40 4 331 Conc Bridge Railing D-31-PB-850 12/14/2010 40 4 338 Conc Curbs/SW D-04-BOST-055 11/15/2010 40 4 321 R/Conc Approach Slab E-16-OR 4/12/2006 40 4 321 R/Conc Approach Slab D-04-BOST-056 11/15/2010 40 4 338 Conc Curbs/SW F-16-PM 9/6/2006 40 4 338 Conc Curbs/SW F-16-EL 1/29/2008 40 4 338 Conc Curbs/SW D-19-SC-050A 1/11/2011 40 4 339 Timber Curb/SW D-05-RRBR-070A 11/16/2010 40 4 338 Conc Curbs/SW D-01-CC-240A 10/4/2010 40 4 339 Timber Curb/SW D-05-RRBR-070 9/28/2000 40 4 339 Timber Curb/SW D-27-MP-010 11/18/2010 40 4 321 R/Conc Approach Slab E-17-NT 9/25/2007 40 4 338 Conc Curbs/SW D-26-SWG-010 12/1/2010 40 4 321 R/Conc Approach Slab E-17-US 7/19/2006 40 4 338 Conc Curbs/SW D-01-CC-190 10/5/2010 40 4 338 Conc Curbs/SW D-25-FC-025 11/29/2010 40 4 338 Conc Curbs/SW D-01-CC-060 10/6/2010 40 4 321 R/Conc ApproachSlab D-31-PB-733 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-723 12/13/2010 40 4 321 R/Conc Approach Slab E-16-NW 6/14/2006 40 4 321 R/Conc Approach Slab D-31-PB-882 12/14/2010 40 4 321 R/Conc Approach Slab F-17-FW 6/28/2007 40 4 321 R/Conc Approach Slab D-31-PB-883 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-460 12/15/2010 40 4 338 Conc Curbs/SW D-01-CC-160 10/5/2010 40 4 215 R/Conc Abutment F-17-CG 11/8/2005 40 4 321 R/Conc Approach Slab D-03-V-036A 1/24/2011 40 4 321 R/Conc Approach Slab D-31-PB-160 12/16/2010 40 4 321 R/Conc Approach Slab D-07-PO-023 1/24/2011 40 4 321 R/Conc Approach Slab D-31-PB-150 12/29/2010 40 4 321 R/Conc Approach Slab D-01-CC-180A 10/5/2010 40 4

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157 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 240 Steel Culvert D-25-FC-035 11/29/2010 40 4 240 Steel Culvert D-25-FC-040 11/29/2010 40 4 321 R/Conc Approach Slab D-31-PB-823 12/13/2010 40 4 221 Conc Pile Cap/Ftg F-16-BM 6/13/2007 40 4 321 R/Conc Approach Slab D-04-BOST-040 11/1/2010 40 4 215 R/Conc Abutment E-17-EQ 8/2/2006 40 4 31 Timber Deck D-27-MP-070 11/18/2010 40 4 215 R/Conc Abutment D-20-MB-790 1/11/2011 40 4 110 R/Conc Open Girder D-01-CC-190 10/5/2010 40 4 151 Unpnt Stl Floor Beam D-27-MP-110 8/4/2000 40 4 211 Other Mtl Pier Wall F-16-DT 2/8/2006 40 4 234 R/Conc Cap D-03-V-150A 11/2/2010 40 4 205 R/Conc Column E-17-AI 8/10/2006 40 4 205 R/Conc Column E-16-EW 6/2/2006 40 4 205 R/Conc Column D-31-PB-832 12/13/2010 40 4 201 Unpnt Stl Column D-05-RRBR-020B 11/17/2010 40 4 201 Unpnt Stl Column D-05-RRBR-070A 11/16/2010 40 4 321 R/Conc Approach Slab D-03-V-030(A) 1/24/2011 40 4 321 R/Conc Approach Slab D-31-PB-650 12/14/2010 40 4 321 R/Conc Approach Slab D-31-PB-822 12/13/2010 40 4 221 ConcPile Cap/Ftg D-05-RRBR-101 11/16/2010 40 4 321 R/Conc Approach Slab D-31-PB-750 12/14/2010 40 4 321 R/Conc Approach Slab D-03-V-032A 1/25/2011 40 4 321 R/Conc Approach Slab D-31-PB-821 12/13/2010 40 4 321 R/Conc Approach Slab D-31-PB-782 12/13/2010 40 4 321 R/Conc Approach Slab D-31-PB-220 12/16/2010 40 4 321 R/Conc Approach Slab D-03-V-046 10/21/2010 40 4 321 R/Conc Approach Slab D-16-LG-130 1/13/2011 40 4 321 R/Conc Approach Slab D-01-CC-320 10/4/2010 40 4 321 R/Conc Approach Slab D-03-V-045(A) 10/20/2010 40 4 321 R/Conc Approach Slab D-31-PB-200 12/16/2010 40 4 321 R/Conc Approach Slab D-31-PB-210 12/16/2010 40 4 321 R/Conc Approach Slab D-03-V-034A 1/24/2011 40 4 321 R/Conc Approach Slab D-03-V-051 1/26/2011 40 4 321 R/Conc Approach Slab D-04-BOST-055 11/15/2010 40 4 321 R/Conc Approach Slab D-06-BORR-040 1/26/2011 40 4 321 R/Conc Approach Slab D-03-V-090 10/18/2010 40 4 321 R/Conc Approach Slab D-02-PR-062 10/7/2010 40 4 321 R/Conc Approach Slab D-03-V-054 10/21/2010 40 4 321 R/Conc Approach Slab D-03-V-052 1/27/2011 40 4 321 R/Conc Approach Slab D-03-V-038A 1/24/2011 40 4 338 Conc Curbs/SW D-01-CC-080 10/6/2010 40.6873 4 338 Conc Curbs/SW E-16-EP 6/16/2006 40.7589 4 234 R/Conc Cap F-16-FI 2/15/2006 40.78 4 338 Conc Curbs/SW F-16-EG 3/23/2006 41.8372 4 338 Conc Curbs/SW E-17-GB 9/27/2007 42.748 4 338 Conc Curbs/SW D-01-CC-270 10/4/2010 45.5434 4 205 R/Conc Column E-17-BY 9/27/2007 45.97 4 234 R/Conc Cap F-16-EJ 12/22/2005 46.222 4 234 R/Conc Cap F-16-EF 1/25/2006 46.3815 4 338 Conc Curbs/SW E-17-HY 9/20/2007 47.0992 4 338 Conc Curbs/SW E-17-HW 9/20/2007 47.32 4 338 Conc Curbs/SW F-16-FI 2/15/2006 47.41 4 205 R/Conc Column D-04-BOST-040 11/1/2010 47.5 4 215 R/Conc Abutment F-16-BM 6/13/2007 48.630373 4 234 R/Conc Cap D-31-PB-883 12/14/2010 50.1746 4

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158 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 338 Conc Curbs/SW F-16-BM 6/13/2007 50.413489 4 338 Conc Curbs/SW D-04-BOST-041 11/1/2010 50.4819 4 205 R/Conc Column D-07-PO-050 9/10/2004 55 4 221 Conc Pile Cap/Ftg F-17-FS 11/8/2005 55 4 215 R/Conc Abutment D-27-MP-090 11/18/2010 55.0009 4 205 R/Conc Column E-16-FW 9/20/2007 56.9 4 215 R/Conc Abutment F-16-GG 12/21/2005 57.069886 4 205 R/Conc Column F-16-FW 4/10/2007 57.7532 4 110 R/Conc Open Girder F-16-EN 1/29/2008 58.0792 4 338 Conc Curbs/SW E-17-HZ 9/20/2007 58.7788 4 338 Conc Curbs/SW E-16-EY 9/11/2007 59.7106 4 221 Conc Pile Cap/Ftg F-16-DT 2/8/2006 59.98 4 205 R/Conc Column E-17-GD 10/9/2007 59.9803 4 215 R/Conc Abutment D-05-RRBR-101 11/16/2010 59.998 4 205 R/Conc Column E-17-IQ 12/13/2007 59.9998 4 215 R/Conc Abutment F-16-EJ 12/22/2005 60.15 4 205 R/Conc Column D-05-RRBR-070A 11/16/2010 62.5 4 241 Concrete Culvert D-16-LG-120 10/23/2008 62.8984 4 241 Concrete Culvert D-11-GG-170A 12/2/2010 63.2754 4 215 R/Conc Abutment F-16-FI 2/15/2006 63.37 4 215 R/Conc Abutment D-05-RRBR-102 11/16/2010 63.4786 4 205 R/Conc Column F-17-GL 6/21/2007 64 4 105 R/Conc Box Girder D-03-V-045(A) 10/20/2010 64.462 4 338 Conc Curbs/SW D-01-CC-030A 10/6/2010 64.8436 4 338 Conc Curbs/SW F-16-IL 9/6/2006 65.116 4 241 Concrete Culvert D-11-GG-100A 12/2/2010 65.6833 4 338 Conc Curbs/SW D-13-HGE-110 11/30/2010 65.9998 4 215 R/Conc Abutment D-02-PR-070A 10/6/2010 66.31 4 205 R/Conc Column F-16-DW 2/9/2006 66.667 4 338 Conc Curbs/SW E-17-GA 9/27/2007 66.83 4 110 R/Conc Open Girder F-16-BM 6/13/2007 66.873532 4 338 Conc Curbs/SW E-17-EW 10/9/2007 67.2532 4 338 Conc Curbs/SW D-04-BOST-056 11/15/2010 67.8574 4 338 Conc Curbs/SW D-10-HC-190 12/2/2010 68.047 4 215 R/Conc Abutment F-16-EG 3/23/2006 68.1 4 215 R/Conc Abutment D-05-RRBR-103 11/16/2010 68.332 4 234 R/Conc Cap E-17-JP 12/13/2007 69.1492 4 338 Conc Curbs/SW D-02-PR-040 10/7/2010 69.718 4 215 R/Conc Abutment D-10-HC-160 12/1/2010 69.844 4 331 Conc Bridge Railing F-16-JX 1/5/2006 69.9523 4 215 R/Conc Abutment D-07-PO-050 9/10/2004 69.979492 4 338 Conc Curbs/SW D-16-LG-070A 1/13/2011 69.9994 4 338 Conc Curbs/SW E-17-FX 11/26/2007 69.9995 4 321 R/Conc Approach Slab E-16-GC 9/20/2007 70 4 338 Conc Curbs/SW F-17-HB 3/27/2006 70 4 338 Conc Curbs/SW D-01-CC-310 9/13/2006 70 4 338 Conc Curbs/SW D-01-CC-040A 10/6/2010 70 4 338 Conc Curbs/SW D-01-CC-333 10/4/2010 70 4 321 R/Conc Approach Slab E-16-ON 7/7/2006 70 4 321 R/Conc Approach Slab E-16-OQ 4/12/2006 70 4 321 R/Conc Approach Slab D-31-PB-680 12/14/2010 70 4 321 R/Conc Approach Slab D-01-CC-019 10/6/2010 70 4 321 R/Conc Approach Slab E-16-EP 6/16/2006 70 4 338 Conc Curbs/SW F-16-DS 6/1/2006 70 4 205 R/Conc Column D-05-RRBR-070 9/28/2000 70 4 321 R/Conc Approach Slab E-17-PC 12/3/2007 70 4

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159 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 205 R/Conc Column D-02-PR-150 10/18/2010 70 4 210 R/Conc Pier Wall F-16-BM 6/13/2007 70 4 215 R/Conc Abutment D-05-RRBR-030 11/16/2010 70 4 215 R/Conc Abutment D-05-RRBR-070 9/28/2000 70 4 221 Conc Pile Cap/Ftg D-27-MP-070 11/18/2010 70 4 321 R/Conc Approach Slab D-02-PR-130A 1/26/2011 70 4 321 R/Conc Approach Slab D-03-V-161 1/26/2011 70 4 321 R/Conc Approach Slab E-17-NU 9/25/2007 70 4 321 R/Conc Approach Slab E-17-XX 1/18/2008 70 4 105 R/Conc Box Girder F-17-GW 6/21/2007 70.0186 4 331 Conc Bridge Railing D-19-SC-010 10/21/2008 70.5929 4 338 Conc Curbs/SW D-13-HGE-270 12/1/2010 70.7686 4 338 Conc Curbs/SW E-16-EO 6/2/2006 70.93 4 215 R/Conc Abutment F-16-NK 3/14/2006 70.9522 4 234 R/Conc Cap F-16-OE 7/23/2007 70.96 4 338 Conc Curbs/SW E-17-BY 9/27/2007 70.9818 4 234 R/Conc Cap F-16-FH 1/14/2008 71.5792 4 215 R/Conc Abutment D-28-MS-130 1/27/2011 71.7145 4 110 R/Conc Open Girder D-05-RRBR-091 11/16/2010 72.0001 4 234 R/Conc Cap E-17-HD 4/10/2006 72.41 4 205 R/Conc Column E-17-HX 9/20/2007 72.45 4 338 Conc Curbs/SW E-17-JP 12/13/2007 72.4768 4 210 R/Conc Pier Wall D-13-HGE-200 11/30/2010 72.8296 4 234 R/Conc Cap D-01-CC-270 10/4/2010 72.953 4 205 R/Conc Column F-16-EJ 12/22/2005 73.3402 4 338 Conc Curbs/SW E-17-HT 1/18/2008 73.4284 4 338 Conc Curbs/SW F-16-EN 1/29/2008 73.9876 4 215 R/Conc Abutment D-01-CC-281 10/4/2010 74.2858 4 338 Conc Curbs/SW D-10-HC-240 12/2/2010 74.7822 4 205 R/Conc Column E-17-HY 9/20/2007 74.9797 4 234 R/Conc Cap D-05-RRBR-070 9/28/2000 74.98906 4 338 Conc Curbs/SW D-01-CC-050 10/6/2010 75.0004 4 205 R/Conc Column E-17-HZ 9/20/2007 75.0005 4 338 Conc Curbs/SW D-04-BOST-030 11/1/2010 75.5131 4 338 Conc Curbs/SW D-04-BOST-040 11/1/2010 75.676 4 338 Conc Curbs/SW D-05-RRBR-080 11/16/2010 75.8926 4 117 Timber Stringer D-27-MP-010 11/18/2010 75.9023 4 233 P/S Conc Cap D-03-V-220 10/21/2010 76.6 4 338 Conc Curbs/SW F-17-CG 11/8/2005 76.7236125 4 234 R/Conc Cap D-31-PB-881 12/14/2010 76.8415 4 210 R/Conc Pier Wall E-16-EP 6/16/2006 76.9231 4 331 Conc Bridge Railing D-31-PB-732 12/14/2010 77.4196 4 205 R/Conc Column F-16-BM 6/13/2007 77.5 4 205 R/Conc Column E-17-GC 10/9/2007 77.5 4 338 Conc Curbs/SW D-02-PR-250 10/7/2010 77.914 4 338 Conc Curbs/SW D-01-CC-200 10/5/2010 77.9248 4 215 R/Conc Abutment E-16-EP 6/16/2006 77.9481 4 234 R/Conc Cap D-10-HC-120 12/1/2010 78.0361 4 338 Conc Curbs/SW D-10-HC-261 12/2/2010 78.2254 4 338 Conc Curbs/SW F-16-EK 4/13/2006 78.4503467 4 241 Concrete Culvert D-11-GG-080 1/12/2011 78.7165 4 210 R/Conc Pier Wall F-17-AE 3/22/2007 78.8195497 4 234 R/Conc Cap D-31-PB-731 12/14/2010 79.474 4 215 R/Conc Abutment F-17-GL 6/21/2007 79.5451 4 234 R/Conc Cap D-31-PB-882 12/14/2010 79.9996 4 234 R/Conc Cap D-31-PB-732 12/14/2010 79.9996 4

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160 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 215 R/Conc Abutment D-02-PR-250 10/7/2010 79.9999 4 155 R/Conc Floor Beam F-16-BM 6/13/2007 80.000004 4 205 R/Conc Column D-31-PB-722 12/13/2010 80.0002 4 205 R/Conc Column E-16-DN 9/11/2007 80.0002 4 144 R/Conc Arch D-27-MP-110A 11/18/2010 80.0002 4 221 Conc Pile Cap/Ftg D-05-RRBR-103 11/16/2010 80.002 4 215 R/Conc Abutment D-27-MP-070 11/18/2010 80.002 4 109 P/S Conc Open Girder D-01-CC-260A 10/4/2010 80.227 4 241 Concrete Culvert D-26-SWG-015 12/1/2010 80.68 4 215 R/Conc Abutment D-13-HGE-200 11/30/2010 80.7556 4 104 P/S Conc Box Girder D-03-V-045(A) 10/20/2010 81.3169 4 338 Conc Curbs/SW D-07-PO-160 1/24/2011 81.4876 4 338 Conc Curbs/SW D-02-PR-070A 10/6/2010 81.6583 4 105 R/Conc Box Girder D-04-BOST-030 11/1/2010 82.255 4 241 Concrete Culvert D-11-GG-120A 12/2/2010 82.5138 4 109 P/S Conc Open Girder D-07-PO-040 1/4/2007 82.8126158 4 241 Concrete Culvert D-17-WG-020 1/12/2011 82.8127 4 221 Conc Pile Cap/Ftg D-01-CC-070 10/6/2010 82.858 4 215 R/Conc Abutment D-19-SC-050A 1/11/2011 83.074 4 215 R/Conc Abutment F-16-EF 1/25/2006 83.2852 4 205 R/Conc Column D-28-MS-130 1/27/2011 83.3328 4 338 Conc Curbs/SW D-10-HC-180 12/1/2010 83.3695 4 110 R/Conc Open Girder D-05-RRBR-092 11/16/2010 83.9998 4 241 Concrete Culvert D-17-WG-150 1/13/2011 84.0373 4 241 Concrete Culvert D-17-WG-211 1/13/2011 84.4 4 205 R/Conc Column E-17-FX 11/26/2007 84.4261 4 338 Conc Curbs/SW D-01-CC-220A 10/4/2010 84.868 4 205 R/Conc Column E-17-HW 9/20/2007 84.97 4 205 R/Conc Column F-16-EN 1/29/2008 85 4 205 R/Conc Column E-16-EO 6/2/2006 85 4 205 R/Conc Column F-16-DA 6/16/2006 85 4 338 Conc Curbs/SW D-04-BOST-045 11/15/2010 85 4 205 R/Conc Column F-16-BI 6/1/2006 85.000007 4 338 Conc Curbs/SW D-01-CC-140 10/5/2010 85.156 4 338 Conc Curbs/SW D-02-PR-150 10/18/2010 85.4942 4 241 Concrete Culvert D-20-MB-692 12/29/2010 85.6816 4 338 Conc Curbs/SW E-17-GE 9/27/2007 85.76 4 215 R/Conc Abutment D-31-PB-883 12/14/2010 85.7896 4 110 R/Conc Open Girder E-16-EO 6/2/2006 85.8822 4 110 R/Conc Open Girder D-01-CC-200 10/5/2010 86.668 4 215 R/Conc Abutment D-04-BOST-050 11/1/2010 86.9092 4 338 Conc Curbs/SW F-16-BI 6/1/2006 87.060238 4 241 Concrete Culvert D-26-SWG-034 12/1/2010 87.3757 4 338 Conc Curbs/SW E-17-HU 11/8/2005 87.5002 4 241 Concrete Culvert D-20-MB-335 12/29/2010 87.52 4 210 R/Conc Pier Wall D-02-PR-250 10/7/2010 87.586 4 210 R/Conc Pier Wall F-16-DA 6/16/2006 87.67 4 338 Conc Curbs/SW F-16-DT 2/8/2006 87.7597 4 205 R/Conc Column D-02-PR-220 10/7/2010 88 4 205 R/Conc Column D-01-CC-140 10/5/2010 88 4 215 R/Conc Abutment D-05-RRBR-070A 11/16/2010 88.261 4 241 Concrete Culvert D-10-HC-380 1/11/2011 88.333 4 110 R/Conc Open Girder F-16-DS 6/1/2006 88.4797 4 215 R/Conc Abutment F-16-RI 6/20/2007 88.66 4 338 Conc Curbs/SW D-13-HGE-210 11/30/2010 88.6798 4 215 R/Conc Abutment F-16-EL 1/29/2008 88.6876782 4

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161 Table D.2(Cont.) Element key Element description Bridge key Inspection date EHI CS count 338 Conc Curbs/SW F-16-EF 1/25/2006 88.69 4 215 R/Conc Abutment F-17-H 2/1/2006 88.7131 4 205 R/Conc Column E-16-FJ 2/1/2007 88.75 4 234 R/Conc Cap D-31-PB-733 12/14/2010 88.9474 4 205 R/Conc Column D-05-RRBR-091 11/16/2010 89.0032 4 234 R/Conc Cap E-17-FX 11/26/2007 89.1271 4 241 Concrete Culvert D-11-GG-050A 1/12/2011 89.1832 4 105 R/Conc Box Girder E-17-KR 9/26/2006 89.6272 4 215 R/Conc Abutment E-17-PC 12/3/2007 89.7142 4 205 R/Conc Column E-17-CJ 8/10/2006 89.9998 4 338 Conc Curbs/SW D-13-HGE-160 11/30/2010 90.0004 4 241 Concrete Culvert D-31-PB-440 12/29/2010 90.0517 4 241 Concrete Culvert D-25-FC-033 11/29/2010 90.184 4 215 R/Conc Abutment E-16-EM 6/29/2006 90.34 4

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162 Table D.3The 350 lowest-EHI elements wi th 5CSs in CCD major bridge network Element key Element description Bridge key Inspection date EHI CS count 12 Bare Concrete Deck D-19-SC-010 10/21/2008 0 5 13 Unp Conc Deck/AC Ovl D-01-CC-050 10/6/2010 0 5 13 Unp Conc Deck/AC Ovl F-16-GG 12/21/2005 0 5 26 Conc Deck/Coatd Bars D-04-BOST-055 11/15/2010 0 5 26 Conc Deck/Coatd Bars D-04-BOST-056 11/15/2010 0 5 26 Conc Deck/Coatd Bars D-06-BORR-040 1/26/2011 0 5 12 Bare Concrete Deck D-01-CC-281 10/4/2010 0 5 334 Metal Rail Coated F-16-DW 2/9/2006 9.79 5 60 RR Dk D-05-RRBR-080 11/16/2010 10 5 14 P Conc Deck/AC Ovly D-01-CC-168 10/5/2010 10 5 14 P Conc Deck/AC Ovly D-02-PR-220 10/7/2010 10 5 26 Conc Deck/Coatd Bars D-06-BORR-050 1/26/2011 10 5 25 Conc Deck/Rig OL+Brs D-03-V-045(A) 10/20/2010 10 5 13 Unp Conc Deck/AC Ovl E-17-HT 1/18/2008 10 5 334 Metal Rail Coated F-16-AP 6/1/2006 10 5 107 Paint Stl Opn Girder D-27-MP-030A 11/18/2010 10 5 334 Metal Rail Coated F-16-DT 2/8/2006 10 5 13 Unp Conc Deck/AC Ovl D-02-PR-250 10/7/2010 10 5 13 Unp Conc Deck/AC Ovl D-09-CLC-010 1/13/2011 10 5 107 Paint Stl Opn Girder D-27-MP-010 11/18/2010 10 5 152 Paint Stl Floor Beam D-27-MP-010 11/18/2010 10 5 40 P Conc Slab/AC Ovly D-01-CC-230A 10/4/2010 10 5 202 Paint Stl Column D-05-RRBR-103 11/16/2010 10 5 202 Paint Stl Column D-05-RRBR-101 11/16/2010 10 5 202 Paint Stl Column D-05-RRBR-080 11/16/2010 10 5 13 Unp Conc Deck/AC Ovl D-02-PR-260A 10/7/2010 10 5 231 Paint Stl Cap D-05-RRBR-080 11/16/2010 17.9431 5 334 Metal Rail Coated F-16-FW 4/10/2007 20.244 5 107 Paint Stl Opn Girder D-05-RRBR-103 11/16/2010 25.8848 5 152 Paint Stl Floor Beam D-05-RRBR-103 11/16/2010 27.3214 5 231 Paint Stl Cap D-10-HC-160 12/1/2010 27.916 5 107 Paint Stl Opn Girder D-05-RRBR-080 11/16/2010 28 5 334 Metal Rail Coated F-17-CG 11/8/2005 28.6208840110758 5 231 Paint Stl Cap F-16-DW 2/9/2006 28.92 5 334 Metal Rail Coated D-27-MP-070 11/18/2010 29.9979 5 13 Unp Conc Deck/AC Ovl F-16-EK 4/13/2006 30 5 23 Bare Conc Dk w/Brs D-03-V-030(A) 1/24/2011 30 5 14 P Conc Deck/AC Ovly D-01-CC-210A 10/5/2010 30 5 35 Pcst Pnl Cnc Dk Bare D-07-PO-050 9/10/2004 30 5 13 Unp Conc Deck/AC Ovl E-17-HU 11/8/2005 30 5 14 P Conc Deck/AC Ovly D-01-CC-180A 10/5/2010 30 5 13 Unp Conc Deck/AC Ovl E-16-DY 9/11/2007 30 5 14 P Conc Deck/AC Ovly F-17-GL 6/21/2007 30 5 13 Unp Conc Deck/AC Ovl E-17-HX 9/20/2007 30 5 13 Unp Conc Deck/AC Ovl F-16-EL 1/29/2008 30 5 23 Bare Conc Dk w/Brs D-03-V-054 10/21/2010 30 5 39 Unp Conc Slab/AC Ovl D-10-HC-160 12/1/2010 30 5 14 P Conc Deck/AC Ovly D-02-PR-070A 10/6/2010 30 5 14 P Conc Deck/AC Ovly E-17-JP 12/13/2007 30 5 14 P Conc Deck/AC Ovly F-17-GQ 6/21/2007 30 5 26 Conc Deck/Coatd Bars F-16-PM 9/6/2006 30 5 26 Conc Deck/Coatd Bars F-16-OE 7/23/2007 30 5 334 Metal Rail Coated D-16-LG-160 10/17/2003 30 5 334 Metal Rail Coated D-19-SC-050A 1/11/2011 30 5 13 Unp Conc Deck/AC Ovl E-17-GE 9/27/2007 30 5 14 P Conc Deck/AC Ovly D-01-CC-020 10/6/2010 30 5

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163 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 40 P Conc Slab/AC Ovly D-01-CC-160 10/5/2010 30 5 126 P/Stl Thru Truss/Top D-27-MP-110 8/4/2000 30 5 121 P/Stl Thru Truss/Bot D-27-MP-110 8/4/2000 30 5 202 Paint Stl Column D-05-RRBR-102 11/16/2010 30 5 231 Paint StlCap D-05-RRBR-102 11/16/2010 30 5 52 Conc Slab/Coatd Bars D-03-V-047A 10/21/2010 30 5 38 Bare Concrete Slab D-03-V-030(A) 1/24/2011 30 5 13 Unp Conc Deck/AC Ovl F-16-DA 6/16/2006 30 5 38 Bare Concrete Slab E-17-CJ 8/10/2006 30 5 107 Paint Stl Opn Girder D-27-MP-090 11/18/2010 30 5 12 Bare Concrete Deck D-16-LG-160 10/17/2003 30 5 38 Bare Concrete Slab D-07-PO-160 1/24/2011 30 5 12 Bare Concrete Deck D-07-PO-160 1/24/2011 30 5 107 Paint Stl Opn Girder D-01-CC-020 10/6/2010 30 5 13 Unp Conc Deck/ACOvl D-03-V-180 11/4/2010 30 5 13 Unp Conc Deck/AC Ovl D-10-HC-180 12/1/2010 30 5 13 Unp Conc Deck/AC Ovl D-01-CC-320 10/4/2010 30 5 13 Unp Conc Deck/AC Ovl D-01-CC-270 10/4/2010 30 5 13 Unp Conc Deck/AC Ovl D-04-BOST-050 11/1/2010 30 5 113 Paint Stl Stringer D-05-RRBR-103 11/16/2010 30 5 113 Paint Stl Stringer D-02-PR-150 10/18/2010 30 5 334 Metal Rail Coated E-17-HU 11/8/2005 33.3334 5 334 Metal Rail Coated F-16-DA 6/16/2006 39.2118 5 334 Metal Rail Coated E-16-EP 6/16/2006 44.0045 5 202 Paint Stl Column F-16-DP 7/25/2007 45.6782 5 231 Paint Stl Cap F-16-DT 2/8/2006 45.78 5 334 Metal Rail Coated D-01-CC-019 10/6/2010 46.3635 5 334 Metal Rail Coated F-16-DS 6/1/2006 48.1756 5 107 Paint Stl Opn Girder D-02-PR-150 10/18/2010 48.6627 5 107 PaintStl Opn Girder F-16-DW 2/9/2006 49.46 5 334 Metal Rail Coated F-16-FI 2/15/2006 50.8412 5 334 Metal Rail Coated D-11-GG-040 1/12/2011 50.9085 5 141 Paint Stl Arch F-16-DP 7/25/2007 51.9975 5 107 Paint Stl Opn Girder D-05-RRBR-037 11/17/2010 52.293 5 334 Metal Rail Coated F-16-BM 6/13/2007 52.300241 5 334 Metal Rail Coated D-05-RRBR-096 11/15/2010 53.6766 5 152 Paint Stl Floor Beam F-16-DP 7/25/2007 53.7911 5 334 Metal Rail Coated D-01-CC-017 10/6/2010 53.9043 5 334 Metal Rail Coated F-16-EK 4/13/2006 54.3055599 5 334 Metal Rail Coated D-10-HC-380 1/11/2011 54.5454 5 334 Metal Rail Coated D-16-LG-120 10/23/2008 55.383 5 107 Paint Stl Opn Girder F-16-GG 12/21/2005 56.639949 5 161 Paint Stl Pin/Hanger F-16-DW 2/9/2006 58.55 5 152 Paint Stl Floor Beam D-02-PR-150 10/18/2010 58.5812 5 107 Paint Stl Opn Girder D-05-RRBR-020B 11/17/2010 59.5239 5 334 Metal Rail Coated F-16-EJ 12/22/2005 59.78 5 334 Metal Rail Coated D-04-BOST-056 11/15/2010 59.851 5 141 Paint Stl Arch D-04-BOST-055 11/15/2010 59.9958 5 231 Paint Stl Cap D-07-PO-022 1/14/2011 59.997 5 334 Metal Rail Coated D-16-LG-060A 1/13/2011 59.9994 5 334 Metal Rail Coated D-02-PR-220 10/7/2010 59.9994 5 60 RR Dk D-05-RRBR-091 11/16/2010 60 5 13 Unp Conc Deck/AC Ovl E-17-AF 8/10/2006 60 5 13 Unp Conc Deck/AC Ovl E-16-FW 9/20/2007 60 5 13 Unp Conc Deck/AC Ovl F-16-FW 4/10/2007 60 5 13 Unp Conc Deck/AC Ovl F-16-DW 2/9/2006 60 5

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164 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 13 Unp Conc Deck/AC Ovl E-16-FJ 2/1/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-GD 10/9/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-DF 10/9/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-BY 9/27/2007 60 5 13 Unp Conc Deck/AC Ovl F-16-BM 6/13/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-HZ 9/20/2007 60 5 13 Unp Conc Deck/AC Ovl F-16-DP 7/25/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-HY 9/20/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-EW 10/9/2007 60 5 13 Unp Conc Deck/AC Ovl E-16-EY 9/11/2007 60 5 13 Unp Conc Deck/AC Ovl E-17-GA 9/27/2007 60 5 60 RR Dk D-05-RRBR-102 11/16/2010 60 5 35 Pcst Pnl Cnc Dk Bare D-07-PO-030 1/14/2011 60 5 23 Bare Conc Dk w/Brs D-31-PB-220 12/16/2010 60 5 60 RR Dk D-05-RRBR-092 11/16/2010 60 5 60 RR Dk D-05-RRBR-070 9/28/2000 60 5 14 P Conc Deck/AC Ovly D-03-V-010 10/20/2010 60 5 60 RR Dk D-05-RRBR-020B 11/17/2010 60 5 60 RR Dk D-05-RRBR-020A 11/17/2010 60 5 107 Paint Stl Opn Girder D-05-RRBR-020A 11/17/2010 60 5 13 Unp Conc Deck/AC Ovl F-16-DU 12/22/2005 60 5 13 Unp Conc Deck/AC Ovl E-17-AH 1/9/2007 60 5 23 Bare Conc Dk w/Brs D-31-PB-230 12/16/2010 60 5 14 P Conc Deck/AC Ovly D-28-MS-155 11/18/2010 60 5 60 RR Dk D-05-RRBR-098 11/16/2010 60 5 14 P Conc Deck/AC Ovly D-01-CC-282A 10/4/2010 60 5 60 RR Dk D-05-RRBR-101 11/16/2010 60 5 13 Unp Conc Deck/AC Ovl E-16-EP 6/16/2006 60 5 60 RR Dk D-05-RRBR-070A 11/16/2010 60 5 141 Paint Stl Arch D-07-PO-340 1/24/2011 60 5 13 Unp Conc Deck/AC Ovl D-01-CC-310 9/13/2006 60 5 231 Paint Stl Cap D-07-PO-330 1/24/2011 60 5 202 Paint Stl Column D-07-PO-330 1/24/2011 60 5 202 Paint Stl Column D-07-PO-200 12/13/2006 60 5 202 Paint Stl Column D-07-PO-022 1/14/2011 60 5 121 P/Stl Thru Truss/Bot D-07-PO-022 1/14/2011 60 5 40 P Conc Slab/AC Ovly D-20-MB-790 1/11/2011 60 5 141 Paint Stl Arch D-04-BOST-056 11/15/2010 60 5 40 P Conc Slab/AC Ovly D-02-PR-130A 1/26/2011 60 5 141 Paint Stl Arch D-02-PR-090A 10/18/2010 60 5 141 Paint Stl Arch D-07-PO-330 1/24/2011 60 5 141 Paint Stl Arch D-02-PR-100A 10/18/2010 60 5 161 Paint Stl Pin/Hanger D-07-PO-330 1/24/2011 60 5 161 Paint Stl Pin/Hanger D-07-PO-340 1/24/2011 60 5 60 RR Dk D-05-RRBR-131A 11/17/2010 60 5 126 P/Stl Thru Truss/Top D-07-PO-022 1/14/2011 60 5 102 Paint Stl Box Girder D-06-BORR-050 1/26/2011 60 5 13 Unp Conc Deck/AC Ovl E-17-GB 9/27/2007 60 5 13 Unp Conc Deck/AC Ovl D-01-CC-190 10/5/2010 60 5 12 Bare Concrete Deck D-07-PO-142 1/14/2011 60 5 13 Unp Conc Deck/AC Ovl D-25-FC-010 11/15/2006 60 5 13 Unp Conc Deck/AC Ovl D-10-HC-120 12/1/2010 60 5 13 Unp Conc Deck/AC Ovl D-02-PR-150 10/18/2010 60 5 40 P Conc Slab/AC Ovly D-04-BOST-100 11/3/2010 60 5 107 Paint Stl Opn Girder F-17-FS 11/8/2005 60 5 13 Unp Conc Deck/AC Ovl E-17-HW 9/20/2007 60 5

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165 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 107 Paint Stl Opn Girder D-07-PO-022 1/14/2011 60 5 52 Conc Slab/Coatd Bars D-10-HC-295 12/2/2010 60 5 40 P Conc Slab/AC Ovly D-13-HGE-140 11/30/2010 60 5 40 P Conc Slab/AC Ovly D-13-HGE-270 12/1/2010 60 5 40 P Conc Slab/AC Ovly D-13-HGE-050 11/30/2010 60 5 39 Unp Conc Slab/AC Ovl D-01-CC-080 10/6/2010 60 5 12 Bare Concrete Deck D-07-PO-260 1/14/2011 60 5 26 Conc Deck/Coatd Bars F-16-NZ 1/25/2006 60 5 26 Conc Deck/Coatd Bars F-16-NO 7/23/2007 60 5 334 Metal Rail Coated D-07-PO-340 1/24/2011 60 5 334 Metal Rail Coated D-05-RRBR-102 11/16/2010 60 5 334 Metal Rail Coated D-16-LG-070A 1/13/2011 60 5 334 Metal Rail Coated D-05-RRBR-101 11/16/2010 60 5 334 Metal Rail Coated D-31-PB-200 12/16/2010 60 5 334 Metal Rail Coated D-07-PO-330 1/24/2011 60 5 26 Conc Deck/Coatd Bars F-16-OG 2/14/2006 60 5 334 Metal Rail Coated D-25-FC-010 11/15/2006 60 5 14 P Conc Deck/AC Ovly D-28-MS-130 1/27/2011 60 5 26 Conc Deck/Coatd Bars F-16-MX 2/22/2006 60 5 26 Conc Deck/Coatd Bars F-16-OK 7/23/2007 60 5 26 Conc Deck/Coatd Bars F-16-OH 7/23/2007 60 5 26 Conc Deck/Coatd Bars F-16-RI 6/20/2007 60 5 26 Conc Deck/Coatd Bars F-16-PO 1/19/2005 60 5 26 Conc Deck/Coatd Bars F-16-JX 1/5/2006 60 5 334 Metal Rail Coated D-27-MP-090 11/18/2010 60 5 334 Metal Rail Coated D-20-MB-337 12/29/2010 60 5 336 Coated Metal Curb/SW D-05-RRBR-037 11/17/2010 60 5 334 Metal Rail Coated D-31-PB-230 12/16/2010 60 5 334 Metal Rail Coated D-31-PB-310 12/15/2010 60 5 334 Metal Rail Coated D-02-PR-090A 10/18/2010 60 5 334 Metal Rail Coated D-20-MB-430 12/29/2010 60 5 334 Metal Rail Coated D-31-PB-220 12/16/2010 60 5 334 Metal Rail Coated D-07-PO-022 1/14/2011 60 5 334 Metal Rail Coated D-02-PR-100A 10/18/2010 60 5 26 Conc Deck/Coatd Bars F-16-EG 3/23/2006 60 5 334 Metal Rail Coated D-05-RRBR-103 11/16/2010 60 5 334 Metal Rail Coated D-05-RRBR-091 11/16/2010 60 5 334 Metal Rail Coated D-05-RRBR-092 11/16/2010 60 5 334 Metal Rail Coated D-31-PB-210 12/16/2010 60 5 334 Metal Rail Coated D-05-RRBR-080 11/16/2010 60 5 334 Metal Rail Coated D-11-GG-080 1/12/2011 60 5 334 Metal Rail Coated D-31-PB-150 12/29/2010 60 5 334 Metal Rail Coated D-04-BOST-055 11/15/2010 60 5 23 Bare Conc Dk w/Brs E-16-OP 2/22/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-733 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-320 12/15/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-723 12/13/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-850 12/14/2010 60 5 26 Conc Deck/Coatd Bars E-16-EM 6/29/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-882 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-881 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-731 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-832 12/13/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-732 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-01-CC-019 10/6/2010 60 5 60 RR Dk D-05-RRBR-105 11/16/2010 60 5

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166 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 23 Bare Conc Dk w/Brs D-03-V-036A 1/24/2011 60 5 14 P Conc Deck/AC Ovly D-01-CC-220A 10/4/2010 60 5 35 Pcst Pnl Cnc Dk Bare D-07-PO-040 1/4/2007 60 5 60 RR Dk D-05-RRBR-097 11/16/2010 60 5 26 Conc Deck/Coatd Bars F-16-DC 3/2/2006 60 5 26 Conc Deck/Coatd Bars F-16-PL 9/6/2006 60 5 14 P Conc Deck/AC Ovly F-16-FI 2/15/2006 60 5 26 Conc Deck/Coatd Bars F-16-EC 3/2/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-750 12/14/2010 60 5 26 Conc Deck/Coatd Bars D-03-V-230 10/4/2006 60 5 25 Conc Deck/Rig OL+Brs D-27-MP-110A 11/18/2010 60 5 26 Conc Deck/Coatd Bars D-04-BOST-040 11/1/2010 60 5 26 Conc Deck/Coatd Bars D-02-PR-062 10/7/2010 60 5 60 RR Dk E-17-AI 8/10/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-680 12/14/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-360 12/16/2010 60 5 26 Conc Deck/Coatd Bars F-16-PN 1/19/2005 60 5 14 P Conc Deck/AC Ovly F-16-IL 9/6/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-310 12/15/2010 60 5 23 Bare Conc Dk w/Brs D-31-PB-350 12/15/2010 60 5 14 P Conc Deck/AC Ovly F-16-DQ 2/22/2006 60 5 23 Bare Conc Dk w/Brs D-31-PB-883 12/14/2010 60 5 23 Bare Conc Dk w/Brs E-17-PC 12/3/2007 60 5 334 Metal Rail Coated D-03-V-045(A) 10/20/2010 62.212 5 334 Metal Rail Coated F-16-BI 6/1/2006 62.780329 5 334 Metal Rail Coated D-07-PO-142 1/14/2011 65 5 334 Metal Rail Coated D-03-V-034A 1/24/2011 66.6387 5 334 Metal Rail Coated E-17-HT 1/18/2008 67.1724 5 102 Paint Stl Box Girder E-16-OO 2/28/2006 70.0272 5 334 Metal Rail Coated F-16-EL 1/29/2008 70.2500727 5 334 Metal Rail Coated F-16-FH 1/14/2008 71.4561 5 107 Paint Stl Opn Girder E-17-AH 1/9/2007 71.5004 5 334 Metal Rail Coated D-11-GG-070 1/12/2011 72.1216 5 334 Metal Rail Coated D-11-GG-100A 12/2/2010 73.7032 5 161 Paint Stl Pin/Hanger F-16-DX 3/2/2006 73.76 5 107 Paint Stl Opn Girder F-16-DX 3/2/2006 74.1727 5 107 Paint Stl Opn Girder E-16-EP 6/16/2006 75.8 5 334 Metal Rail Coated D-11-GG-130 12/2/2010 77.5 5 334 Metal Rail Coated F-16-EF 1/25/2006 77.7985 5 334 Metal Rail Coated D-11-GG-005 1/12/2011 79.2316 5 161 Paint Stl Pin/Hanger D-04-BOST-056 11/15/2010 80 5 161 Paint Stl Pin/Hanger D-03-V-051 1/26/2011 80 5 161 Paint Stl Pin/Hanger D-04-BOST-055 11/15/2010 80 5 334 Metal Rail Coated D-03-V-030(A) 1/24/2011 80 5 334 Metal Rail Coated D-26-SWG-100A 12/1/2010 80 5 334 Metal Rail Coated D-31-PB-350 12/15/2010 80 5 334 Metal Rail Coated D-31-PB-320 12/15/2010 80 5 334 Metal Rail Coated D-31-PB-360 12/16/2010 80 5 107 Paint Stl Opn Girder D-07-PO-260 1/14/2011 80.25 5 107 Paint Stl Opn Girder E-17-JP 12/13/2007 80.4572 5 107 Paint Stl Opn Girder F-16-DT 2/8/2006 81.2052 5 107 Paint Stl Opn Girder E-17-FX 11/26/2007 81.7541 5 202 Paint Stl Column D-03-V-161 1/26/2011 82.6081 5 107 Paint Stl Opn Girder D-03-V-161 1/26/2011 83.7188 5 107 Paint Stl Opn Girder D-10-HC-170 1/12/2011 84.2374 5 107 Paint Stl Opn Girder D-05-RRBR-092 11/16/2010 85.8064 5

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167 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 107 Paint Stl Opn Girder D-01-CC-281 10/4/2010 85.8571 5 107 Paint Stl Opn Girder E-17-AI 8/10/2006 85.96 5 334 Metal Rail Coated D-07-PO-160 1/24/2011 86.4512 5 107 Paint Stl Opn Girder D-03-V-180 11/4/2010 87.5971 5 334 Metal Rail Coated D-04-BOST-052 11/16/2010 88.0316 5 231 Paint Stl Cap D-07-PO-340 1/24/2011 88.5708 5 107 Paint Stl Opn Girder D-07-PO-026 1/24/2011 88.5716 5 102 Paint Stl Box Girder E-16-MS 7/26/2007 88.9778 5 107 Paint Stl Opn Girder D-05-RRBR-091 11/16/2010 89 5 107 Paint Stl Opn Girder D-01-CC-320 10/4/2010 89.5266 5 107 Paint Stl Opn Girder F-16-EG 3/23/2006 90.0572 5 107 Paint Stl Opn Girder F-16-IL 9/6/2006 90.9136 5 334 Metal Rail Coated E-17-HX 9/20/2007 90.9161 5 107 Paint Stl Opn Girder D-04-BOST-050 11/1/2010 91.1112 5 334 Metal Rail Coated D-03-V-150A 11/2/2010 91.376 5 152 Paint Stl Floor Beam D-07-PO-340 1/24/2011 91.4284 5 102 Paint Stl Box Girder D-03-V-051 1/26/2011 91.453 5 334 Metal Rail Coated D-11-GG-090 1/12/2011 91.724 5 334 Metal Rail Coated D-07-PO-026 1/24/2011 91.836 5 102 Paint Stl Box Girder E-16-MR 7/10/2006 91.8932 5 113 Paint Stl Stringer F-16-DP 7/25/2007 91.976 5 152 Paint Stl Floor Beam F-16-BI 6/1/2006 92.080124 5 334 Metal Rail Coated D-26-SWG-005 12/1/2010 92.3812 5 102 Paint Stl Box Girder E-16-PJ 3/27/2006 92.61 5 334 Metal Rail Coated D-27-MP-030A 11/18/2010 93.2982 5 107 Paint Stl Opn Girder F-16-EJ 12/22/2005 93.41 5 102 Paint Stl Box Girder D-06-BORR-040 1/26/2011 93.924 5 334 Metal Rail Coated D-05-RRBR-030 11/16/2010 93.9392 5 107 Paint Stl Opn Girder F-16-EF 1/25/2006 94.212 5 113 Paint Stl Stringer F-16-BI 6/1/2006 94.320068 5 231 Paint Stl Cap D-05-RRBR-101 11/16/2010 94.344 5 107 Paint Stl Opn Girder F-16-DP 7/25/2007 94.5638 5 102 Paint Stl Box Girder E-16-ND 1/8/2008 94.6428 5 107 Paint Stl Opn Girder F-16-BI 6/1/2006 94.9351557 5 107 Paint Stl Opn Girder D-27-MP-070 11/18/2010 95.082 5 102 Paint Stl Box Girder E-17-PP 1/25/2008 95.1048 5 107 Paint Stl Opn Girder D-05-RRBR-102 11/16/2010 95.1903 5 107 Paint Stl Opn Girder F-17-H 2/1/2006 95.2632 5 102 Paint Stl Box Girder E-17-PD 12/21/2005 95.4866 5 107 Paint Stl Opn Girder D-01-CC-333 10/4/2010 95.5194 5 334 Metal Rail Coated D-13-HGE-100 11/30/2010 95.862 5 102 Paint Stl Box Girder D-03-V-054 10/21/2010 95.94 5 102 Paint Stl Box Girder D-04-BOST-151 11/4/2010 96.0092 5 334 Metal Rail Coated E-17-BY 9/27/2007 96.0121 5 102 Paint Stl Box Girder D-04-BOST-150 11/4/2010 96.338 5 107 Paint Stl Opn Girder D-03-V-032A 1/25/2011 96.493 5 152 Paint Stl Floor Beam D-07-PO-022 1/14/2011 96.52 5 107 Paint Stl Opn Girder D-01-CC-282A 10/4/2010 96.7792 5 334 Metal Rail Coated D-20-MB-692 12/29/2010 96.8 5 334 Metal Rail Coated D-03-V-010 10/20/2010 97.0007 5 107 Paint Stl Opn Girder F-16-IK 9/6/2006 97.2302 5 334 Metal Rail Coated E-17-FX 11/26/2007 97.4871 5 152 Paint Stl Floor Beam D-04-BOST-055 11/15/2010 97.576 5 152 Paint Stl Floor Beam D-04-BOST-056 11/15/2010 97.576 5 334 Metal Rail Coated F-17-BJ 3/22/2007 97.64 5 102 PaintStl Box Girder E-17-PO 12/21/2005 97.76 5

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168 Table D.3(Cont.) Element key Element description Bridge key Inspection date EHI CS count 231 Paint Stl Cap D-03-V-161 1/26/2011 97.8584 5 107 Paint Stl Opn Girder E-16-NJ 9/11/2007 97.91 5 102 PaintStl Box Girder D-03-V-052 1/27/2011 97.9852 5 107 Paint Stl Opn Girder D-02-PR-040 10/7/2010 98 5 334 Metal Rail Coated F-16-IK 9/6/2006 98.0771 5 107 Paint Stl Opn Girder D-03-V-034A 1/24/2011 98.165 5 102 Paint Stl Box Girder E-16-MO 7/18/2006 98.28 5 107 Paint Stl Opn Girder D-03-V-038A 1/24/2011 98.4629 5 107 Paint Stl Opn Girder D-03-V-036A 1/24/2011 98.54 5 152 Paint Stl Floor Beam E-16-QR 6/2/2006 98.60601783 5 107 Paint Stl Opn Girder D-03-V-030(A) 1/24/2011 98.6168 5 102 Paint Stl Box Girder D-01-CC-180A 10/5/2010 98.66 5 102 Paint Stl Box Girder E-17-PB 12/21/2005 98.6668 5 334 Metal Rail Coated D-10-HC-160 12/1/2010 98.6861 5

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169 APPENDIX E The 50 Lowest-EHI Elements under Each Element Category in CCD Minor Bridge Network This appendix presents Table E.1 containi ng 50 lowest-EHI elements with 3 CSs in CCD minor bridge network, Table E.2 cont aining 50 lowest-EHI elements with 4 CSs in CCD minor bridge network, andTable E.3 containing 50 lowest-EHI elements with 5 CSsin CCD minor bridge network.

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170 Table E.1 The 50 lowest-EHI elements with 3 CSs in CCD minor bridge network Element key Element description Bridge key Inspection date EHI CS count 333 Other Bridge Railing D-24-RM-070 8/28/2008 0 3 302 Compressn Joint Seal D-02-PR-074 6/24/2009 0 3 332 Timb Bridge Railing D-24-RM-400 9/2/2008 0 3 332 Timb Bridge Railing D-27-MP-170 8/28/2008 0 3 332 TimbBridge Railing D-24-RM-430 9/2/2008 0 3 327 Culvert Wingwalls D-26-SWG-043 3/26/2010 0 3 327 Culvert Wingwalls D-27-MP-170 8/28/2008 0 3 327 Culvert Wingwalls D-26-SWG-037 3/26/2010 0 3 327 Culvert Wingwalls D-26-SWG-045 3/26/2010 0 3 301 Pourable Joint Seal D-02-PR-030A 6/19/2009 6.666362 3 332 Timb Bridge Railing D-24-RM-080 9/17/2002 10.01845912 3 304 Open Expansion Joint D-12-SG-080 3/25/2010 20 3 311 Moveable Bearing D-10-HC-130 6/17/2009 20 3 326 Bridge Wingwalls D-22-CD-030 4/15/2010 20 3 326 Bridge Wingwalls D-22-CD-095 4/15/2010 20 3 313 Fixed Bearing D-10-HC-130 6/17/2009 20 3 313 Fixed Bearing D-01-CC-018 4/1/2010 20 3 311 Moveable Bearing D-13-HGE-170A 3/18/2010 20 3 313 Fixed Bearing D-26-SWG-080 4/8/2010 20 3 311 Moveable Bearing D-17-WG-068 10/22/2008 20 3 310 Elastomeric Bearing D-16-LG-075 4/8/2010 20 3 335 Culvert Headwalls D-27-MP-180 8/28/2008 20 3 304 Open Expansion Joint D-26-SWG-050 3/26/2010 20 3 335 Culvert Headwalls D-24-RM-380 9/2/2008 20 3 304 Open Expansion Joint D-10-HC-200 6/17/2009 20 3 333 Other Bridge Railing D-26-SWG-040 3/26/2010 20 3 327 Culvert Wingwalls D-24-RM-420 9/2/2008 20 3 332 Timb Bridge Railing D-02-PR-061 6/20/2009 20 3 333 Other Bridge Railing D-02-PR-140 6/14/2006 20 3 335 Culvert Headwalls D-24-RM-090 8/28/2008 20 3 335 Culvert Headwalls D-24-RM-080 9/17/2002 20 3 335 Culvert Headwalls D-24-RM-180 8/29/2008 20 3 335 Culvert Headwalls D-22-CD-150 4/15/2010 20 3 327 Culvert Wingwalls D-22-CD-150 4/15/2010 20 3 335 Culvert Headwalls D-24-RM-420 9/2/2008 20 3 332 Timb Bridge Railing D-24-RM-220 8/29/2008 20 3 335 Culvert Headwalls D-24-RM-120 8/28/2008 20 3 327 Culvert Wingwalls D-24-RM-410 9/2/2008 20 3 327 Culvert Wingwalls D-24-RM-060 9/17/2002 20 3 332 Timb Bridge Railing D-02-PR-106 6/26/2009 34.69388 3 332 Timb Bridge Railing D-01-CC-350 5/1/2009 36 3 335 Culvert Headwalls D-24-RM-070 8/28/2008 50 3 335 Culvert Headwalls D-24-RM-310 9/2/2008 50 3 327 Culvert Wingwalls D-20-MB-020 5/20/2009 55 3 301 Pourable Joint Seal D-26-SWG-080 4/8/2010 60 3 308 Constr Non Exp Jt E-17-Z 8/11/2006 60 3 326 Bridge Wingwalls D-10-HC-200 6/17/2009 60 3 326 Bridge Wingwalls D-22-CD-080 4/15/2010 60 3 327 Culvert Wingwalls D-16-LG-030 6/25/2007 60 3 332 Timb Bridge Railing D-26-SWG-037 3/26/2010 66.67123 3

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171 Table E.2 The 50 lowest-EHI elements with 4 CSs in CCD minor bridge network Element key Element description Bridge key Inspection date EHI CS count 31 Timber Deck D-10-HC-020 6/22/2009 0 4 240 Steel Culvert D-24-RM-210 8/29/2008 0 4 31 Timber Deck D-10-HC-090 6/22/2009 0 4 321 R/Conc Approach Slab D-01-CC-368 4/9/2010 5 4 240 Steel Culvert D-24-RM-320 9/2/2008 10 4 241 Concrete Culvert D-24-RM-360 9/2/2008 10 4 240 Steel Culvert D-27-MP-230 8/25/2008 10 4 32 Timber Deck/AC Ovly D-10-HC-060 6/22/2009 10 4 156 Timber Floor Beam D-22-CD-020 4/15/2010 10 4 338 Conc Curbs/SW D-24-RM-070 8/28/2008 10 4 112 Unpnt Stl Stringer D-10-HC-210 6/17/2009 28 4 125 U/Stl Thru Truss/Top D-22-CD-100 4/15/2010 40 4 120 U/Stl Thru Truss/Bot D-27-MP-140 8/25/2008 40 4 151 Unpnt Stl Floor Beam D-27-MP-140 8/25/2008 40 4 230 Unpnt Stl Cap D-27-MP-140 8/25/2008 40 4 120 U/Stl Thru Truss/Bot D-10-HC-130 6/17/2009 40 4 125 U/Stl Thru Truss/Top D-27-MP-055 8/25/2008 40 4 120 U/Stl Thru Truss/Bot D-27-MP-055 8/25/2008 40 4 125 U/Stl Thru Truss/Top D-27-MP-065 8/26/2008 40 4 120 U/Stl Thru Truss/Bot D-27-MP-065 8/26/2008 40 4 120 U/Stl Thru Truss/Bot D-13-HGE-170A 3/18/2010 40 4 151 Unpnt Stl Floor Beam D-13-HGE-170A 3/18/2010 40 4 120 U/Stl Thru Truss/Bot D-02-PR-270A 6/26/2009 40 4 151 Unpnt Stl Floor Beam D-22-CD-110 4/15/2010 40 4 125 U/Stl Thru Truss/Top D-13-HGE-170A 3/18/2010 40 4 216 Timber Abutment D-10-HC-130 6/17/2009 40 4 31 Timber Deck D-10-HC-105 6/17/2009 40 4 31 Timber Deck D-11-GG-185 3/19/2010 40 4 31 Timber Deck D-01-CC-338 4/9/2010 40 4 31 Timber Deck D-01-CC-335 4/9/2010 40 4 31 Timber Deck D-10-HC-130 6/17/2009 40 4 31 Timber Deck D-12-SG-025 6/18/2007 40 4 31 Timber Deck D-01-CC-350 5/1/2009 40 4 31 Timber Deck D-12-SG-080 3/25/2010 40 4 31 Timber Deck D-02-PR-106 6/26/2009 40 4 117 Timber Stringer D-22-CD-095 4/15/2010 40 4 156 Timber Floor Beam D-22-CD-095 4/15/2010 40 4 112 Unpnt Stl Stringer D-02-PR-061 6/20/2009 40 4 31 Timber Deck D-10-HC-230 6/16/2009 40 4 112 Unpnt Stl Stringer D-22-CD-100 4/15/2010 40 4 112 Unpnt Stl Stringer D-13-HGE-170A 3/18/2010 40 4 215 R/Conc Abutment D-22-CD-080 4/15/2010 40 4 112 Unpnt Stl Stringer D-27-MP-065 8/26/2008 40 4 112 Unpnt Stl Stringer D-27-MP-055 8/25/2008 40 4 151 Unpnt Stl Floor Beam D-02-PR-270A 6/26/2009 40 4 151 Unpnt Stl Floor Beam D-27-MP-065 8/26/2008 40 4 151 Unpnt Stl Floor Beam D-27-MP-055 8/25/2008 40 4 151 Unpnt Stl Floor Beam D-10-HC-200 6/17/2009 40 4 151 Unpnt Stl Floor Beam D-22-CD-120 4/15/2010 40 4 215 R/Conc Abutment D-27-MP-120 8/25/2008 40 4

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172 Table E.3 The 50 lowest-EHI elements with 5 CSs in CCD minor bridge network Element key Element description Bridge key Inspection date EHI CS count 113 Paint Stl Stringer D-10-HC-080 6/11/2003 10 5 334 Metal Rail Coated D-20-MB-770 6/9/2009 10 5 152 Paint Stl Floor Beam D-10-HC-080 6/11/2003 10 5 334 Metal Rail Coated D-01-CC-360 4/9/2010 20.869566 5 334 Metal Rail Coated D-10-HC-270 6/16/2009 30 5 126 P/Stl Thru Truss/Top D-10-HC-020 6/22/2009 30 5 147 Misc Cable Coated D-27-MP-140 8/25/2008 30 5 152 Paint Stl Floor Beam D-10-HC-220 6/16/2009 30 5 152 Paint Stl Floor Beam D-10-HC-230 6/16/2009 30 5 30 Corrug/Orthotpc Deck D-10-HC-250 6/16/2009 30 5 334 Metal Rail Coated D-07-PO-020 10/27/2008 30 5 334 Metal Rail Coated D-16-LG-020 4/8/2010 30 5 121 P/Stl Thru Truss/Bot D-10-HC-230 6/16/2009 30.0004388686 5 126 P/Stl Thru Truss/Top D-10-HC-230 6/16/2009 30.0004388686 5 334 Metal Rail Coated D-01-CC-362 4/9/2010 48.260868 5 334 Metal Rail Coated D-22-CD-020 4/15/2010 49.996095 5 334 Metal Rail Coated D-20-MB-410 6/4/2009 50 5 334 Metal Rail Coated D-24-RM-030 11/7/2008 50 5 334 Metal Rail Coated D-20-MB-660 6/8/2009 50.000004 5 126 P/Stl Thru Truss/Top D-10-HC-090 6/22/2009 60 5 121 P/Stl Thru Truss/Bot D-07-PO-020 10/27/2008 60 5 131 Paint Stl Deck Truss D-07-PO-020 10/27/2008 60 5 126 P/Stl Thru Truss/Top D-01-CC-016 4/1/2010 60 5 126 P/Stl Thru Truss/Top D-02-PR-057 6/19/2009 60 5 126 P/Stl Thru Truss/Top D-02-PR-270A 6/26/2009 60 5 202 Paint Stl Column D-27-MP-140 8/25/2008 60 5 113 Paint Stl Stringer D-01-CC-016 4/1/2010 60 5 113 Paint Stl Stringer D-10-HC-105 6/17/2009 60 5 113 Paint Stl Stringer D-10-HC-070 6/22/2009 60 5 113 Paint Stl Stringer D-10-HC-020 6/22/2009 60 5 107 Paint Stl Opn Girder D-22-CD-020 4/15/2010 60 5 152 Paint Stl Floor Beam D-26-SWG-050 3/26/2010 60 5 152 Paint Stl Floor Beam D-01-CC-016 4/1/2010 60 5 152 Paint Stl Floor Beam D-10-HC-250 6/16/2009 60 5 152 Paint Stl Floor Beam D-27-MP-120 8/25/2008 60 5 152 Paint Stl Floor Beam D-10-HC-020 6/22/2009 60 5 121 P/Stl Thru Truss/Bot D-10-HC-090 6/22/2009 60 5 152 Paint Stl Floor Beam D-10-HC-105 6/17/2009 60 5 113 Paint Stl Stringer D-10-HC-090 6/22/2009 60 5 202 Paint Stl Column D-10-HC-080 6/11/2003 60 5 126 P/Stl Thru Truss/Top D-01-CC-018 4/1/2010 60 5 126 P/Stl Thru Truss/Top D-10-HC-070 6/22/2009 60 5 121 P/Stl Thru Truss/Bot D-10-HC-070 6/22/2009 60 5 131 Paint Stl Deck Truss D-22-CD-100 4/15/2010 60 5 126 P/Stl Thru Truss/Top D-10-HC-105 6/17/2009 60 5 121 P/Stl Thru Truss/Bot D-10-HC-105 6/17/2009 60 5 121 P/Stl Thru Truss/Bot D-01-CC-016 4/1/2010 60 5 126 P/Stl Thru Truss/Top D-27-MP-120 8/25/2008 60 5 121 P/Stl Thru Truss/Bot D-27-MP-120 8/25/2008 60 5 152 Paint Stl Floor Beam D-10-HC-090 6/22/2009 60 5

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173 APPENDIX F Methodology to Determine ks J&Rfor Elements with 3 CSs This appendix presents FigureF.1 containi ng ideal transition model of elements with 3 CSsandTable F.1containing computation of the Ks J&Rfor every element with 3 CSs.

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174 Figure F.1 ideal transition mo del of elements with 3 CSs 1 03 3 3 1 3 1 & 1 t t t t t SLC t at RSLC KR J 3 2 3 2 & 2t t t SLC t at RSLC KR J 0 03 3 3 3 3 & 3 t t t t SLC t at RSLC KR JWhere: K1 J&Rthrough K3 J&Rare the actual health index coefficients of CS1 through CS3 for every elementwith 3 CSs, t1through t3are the specific age points, RSLC at t1through t3are the residual service lives of the critical location at t1through t3.

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175 Table F.1 Computation of the Ks J&Rfor every element with 3 CSsNo. Element key T1 iT2 iT3 i (i)K1 J&R (i)K2 J&R (i)K4 J&R 11e01 1 T1 2T1 3T 11 3 1 1 1 3 T T T 1 3 1 2 1 3T T T 01 3 1 3 1 3 T T T ::::::::qqe01qTqT2 qT3 13 1 3 q q qT T T q q qT T T3 2 3 03 3 3 q q qT T T

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176 APPENDIX G Methodology to Determine ks J&Rfor Elements with 5CSs This appendix presents FigureG.1 containi ng ideal transition model of elements with 5CSsandTable G.1containing computation of the Ks J&Rfor every element with 5 CSs.

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177 Figure G.1 ideal transition model of elements with 5CSs 1 05 5 5 1 5 1 & 1 t t t t t SLC t at RSLC KR J 5 2 5 2 & 2t t t SLC t at RSLC KR J 5 3 5 3 & 3t t t SLC t at RSLC KR J 5 4 5 4 & 4t t t SLC t at RSLC KR J 0 05 5 5 5 5 & 5 t t t t SLC t at RSLC KR JWhere: K1 J&Rthrough K5 J&Rare the actual health index coefficients of CS1 through CS5 for every element with 5CSs,

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178 t1through t5are the specific age points, RSLC at t1through t5are the residual service lives of the critical location at t1through t5. Table F.1 Computation of the Ks J&Rfor every element with 5CSsNo Elem key T1 iT2 iT3 iT4 iT5 i(i)K1 J&R (i)K2 J&R (i)K3 J&R (i)K4 J&R (i)K4 J&R 11e01 1 T1 2T1 3T1 4T1 5T 11 5 1 1 1 5 T T T 1 5 1 2 1 5T T T 1 5 1 3 1 5T T T 1 5 1 4 1 5T T T 01 5 1 5 1 5 T T T ::::::::::::qqe01qTqT2 qT3 qT4 qT5 15 1 5 q q qT T T q q qT T T5 2 5 q q qT T T5 3 5 q q qT T T5 4 5 05 5 5 q q qT T T

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