uncoated weathering steel bridge data collection …

UNCOATED WEATHERING STEEL BRIDGE DATA COLLECTION

AND PERFORMANCE ASSESSMENT: BRIDGE MAINTENANCE PRACTICES,

DEICING AGENT USE, AND FIELD SAMPLING

by

J.T. Rupp

A thesis submitted to the Faculty of the University of Delaware in partial

fulfillment of the requirements for the degree of Master of Civil Engineering

Spring 2020

© 2020 J.T. Rupp

All Rights Reserved

UNCOATED WEATHERING STEEL BRIDGE DATA COLLECTION

AND PERFORMANCE ASSESSMENT: BRIDGE MAINTENANCE PRACTICES,

DEICING AGENT USE, AND FIELD SAMPLING

by

J.T. Rupp

Approved: __________________________________________________________

Jennifer E. Righman McConnell, Ph.D.

Professor in charge of thesis on behalf of the Advisory Committee

Approved: __________________________________________________________

Sue McNeil, Ph.D.

Chair of the Department of Civil and Environmental Engineering

Approved: __________________________________________________________

Levi T. Thompson, Ph.D.

Dean of the College of Engineering

Approved: __________________________________________________________

Douglas J. Doren, Ph.D.

Interim Vice Provost for Graduate and Professional Education and

Dean of the Graduate College

iii

ACKNOWLEDGMENTS

I would like to thank my advisors Dr. Jennifer Righman McConnell and Dr.

Tripp Shenton for providing me the opportunity to work on this research project

along with them, Tian Bai, and Gary Wenczel. They constantly commended me for

my hard work and allowed me to demonstrate my own ideas while providing me

with assistance when needed. The knowledge and experience I have gained from

this research project have allowed me to advance in my professional career as well

as my personal life. I am tremendously grateful for all of their guidance and support

throughout my college career. I would also like to thank Chris Reoli for all of her

help and cheerful attitude that I was pleased to be in the presence of throughout my

graduate program. I would also like to acknowledge the Long-term Bridge

Performance Program (LTBPP) state coordinators and agencies which provided me

with opportunities to collect valuable research data for this project. My gratitude

also goes to the sponsor of this research project, the Federal Highway Association

(FHWA).

Thank you again to all who supported me throughout this journey and

provided me with the opportunity to pursue my life goals.

iv

TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................... x LIST OF FIGURES .................................................................................................... xiii ABSTRACT ................................................................................................................. xix

Chapter

1 INTRODUCTION .............................................................................................. 1

1.1 Project Overview ....................................................................................... 1 1.2 Project Objectives and Scope..................................................................... 2 1.3 Thesis Organization ................................................................................... 3

2 BACKGROUND ................................................................................................ 5

2.1 Uncoated Weathering Steel Background ................................................... 5

2.1.1 Environmental Effects on Corrosion of UWS ............................... 5 2.1.2 Advantages of UWS ...................................................................... 6 2.1.3 Disadvantages of UWS .................................................................. 7

2.2 Methods Previously Used for Assessing UWS .......................................... 8

2.2.1 Visual Inspection ........................................................................... 8 2.2.2 Clear Tape Adhesion Test ............................................................ 11 2.2.3 Ion Chromatography (IC) Analysis ............................................. 11 2.2.4 XRD Analysis .............................................................................. 12

2.3 Past Field Studies ..................................................................................... 12

2.3.1 Coastal Environment Field Studies .............................................. 13

2.3.1.1 Relationship Between Atmospheric Corrosion Rates

and Chloride Concentrations......................................... 13 2.3.1.2 Rust Phases Formed in Coastal Environments ............. 16 2.3.1.3 Goethite/Lepidocrocite Relation to Protective Ability

Index in Coastal Environments ..................................... 17 2.3.1.4 Effects on Corrosion Rates Based on Chemical

Composition of UWS in Coastal Environments ........... 18

v

2.3.1.5 Relationship Between Physical Corrosion

Characteristics and Chloride Concentrations ................ 20

2.3.2 Industrial Environment Field Studies .......................................... 22

2.3.2.1 Relationship Between Atmospheric Corrosion Rates

and Sulfur Oxide Concentrations .................................. 23 2.3.2.2 Rust Phases Formed in Industrial Environments .......... 25 2.3.2.3 Goethite/Lepidocrocite Relation to Protective Ability

Index in Industrial Environments.................................. 26 2.3.2.4 Effects on Corrosion Rates Based on Chemical

Composition of UWS in Industrial Environments ........ 27

2.3.3 Rural Environment Field Studies ................................................. 29

2.3.3.1 Rust Characteristics in Rural Environments ................. 29 2.3.3.2 Rust Phases Formed in Rural Environments ................ 30 2.3.3.3 Goethite/Lepidocrocite Relation to Protective Ability

Index in Rural Environments ........................................ 31

2.3.4 UWS Bridge Washing Field Studies............................................ 32

2.4 Phase 1 and Phase 2 Work ....................................................................... 33

3 FIELD METHODOLOGY ............................................................................... 37

3.1 Bridge Selection ....................................................................................... 37

3.1.1 GIS Database ............................................................................... 39 3.1.2 Reference Bridges ........................................................................ 40 3.1.3 Proximate Bridges ........................................................................ 41 3.1.4 Cluster Bridges............................................................................. 42 3.1.5 Field Bridges ................................................................................ 44

3.2 Field Work ............................................................................................... 46

3.2.1 Equipment .................................................................................... 46 3.2.2 Prior to Field Visit ....................................................................... 48 3.2.3 Once in the Field .......................................................................... 48

3.2.3.1 Visual Documentation .................................................. 49 3.2.3.2 Sample Areas ................................................................ 52

3.2.3.2.1 Locations ..................................................... 52 3.2.3.2.2 Measurements ............................................. 55 3.2.3.2.3 Photographs................................................. 56

vi

3.2.3.3 Dry-Film Thickness ...................................................... 57 3.2.3.4 Tape Samples ................................................................ 57 3.2.3.5 Rust Samples ................................................................. 58 3.2.3.6 Ultrasonic Thickness Measurements ............................ 58 3.2.3.7 Severe Corrosion, Pitting, and Section Loss ................. 59

3.3 Data Collection ........................................................................................ 59

3.3.1 Clear Tape Adhesion Test ............................................................ 59 3.3.2 Ion Chromatography Analysis ..................................................... 60

4 RESULTS ......................................................................................................... 61

4.1 Qualitative Assessments of Bridges ......................................................... 61

4.1.1 Colorado Bridges ......................................................................... 66 4.1.2 Connecticut Bridges ..................................................................... 68

4.1.2.1 CT 3830 ........................................................................ 68 4.1.2.2 CT 4382 ........................................................................ 68 4.1.2.3 CT 5796 ........................................................................ 69

4.1.3 Iowa Bridges ................................................................................ 71

4.1.3.1 IA 004111 ..................................................................... 71 4.1.3.2 IA 041331 ..................................................................... 71 4.1.3.3 IA 042711 ..................................................................... 71

4.1.4 Minnesota Bridges ....................................................................... 73

4.1.4.1 MN 04019 ..................................................................... 73 4.1.4.2 MN 19811 ..................................................................... 75 4.1.4.3 MN 62861 ..................................................................... 76

4.1.5 North Carolina Bridges ................................................................ 77

4.1.5.1 NC 190083 .................................................................... 77 4.1.5.2 NC 1290057 .................................................................. 78 4.1.5.3 NC 1290058 .................................................................. 80

4.1.6 New Hampshire Bridges .............................................................. 81

4.1.6.1 NH 017201120011300 .................................................. 81 4.1.6.2 NH 11101120017900 .................................................... 82 4.1.6.3 NH 017701460003700 .................................................. 84

vii

4.1.7 Ohio Bridges ................................................................................ 86

4.1.7.1 OH 7701977 .................................................................. 86 4.1.7.2 OH 7701993 .................................................................. 86 4.1.7.3 OH 7700105 .................................................................. 86

4.2 Findings Related to Bridge Maintenance Practices ................................. 89

4.2.1 Findings from Review of Maintenance Manuals ......................... 89

4.2.1.1 Response Rates ............................................................. 89 4.2.1.2 Review of Maintenance Manuals Results ..................... 89

4.2.2 Findings from Washing Practices Survey .................................... 94

4.2.2.1 Response Rates ............................................................. 95 4.2.2.2 Washing Practices Survey Results ................................ 97

4.3 Findings Related to Deicing Agent Usage ............................................... 99

4.3.1 Findings from Deicing Agent Usage Survey ............................... 99

4.3.1.1 Response Rates ............................................................. 99 4.3.1.2 Deicing Agent Usage Survey Results ......................... 102

4.4 Field Results........................................................................................... 103

4.4.1 Tape Test Results ....................................................................... 103

4.4.1.1 Cluster Performance Based on Tape Test Results ...... 104 4.4.1.2 Field Bridge Performance Based on Tape Test

Results ......................................................................... 106 4.4.1.3 Standard Sample Area Location Performance Based

on Tape Test Results ................................................... 108

4.4.2 Ion Chromatography Results ..................................................... 111

4.4.2.1 Cluster Ion Chromatography Results .......................... 112 4.4.2.2 Field Bridge Ion Chromatography Results ................. 114 4.4.2.3 Standard Sample Area Location Ion

Chromatography Results ............................................. 118

5 DATA CORRELATIONS DISCUSSION ..................................................... 122

5.1 Introduction ............................................................................................ 122

viii

5.2 Correlations Between Bridge Maintenance Manual Ratings and Tape

Test Results ............................................................................................ 123 5.3 Correlations Between Bridge Washing Practices and Tape Test

Results .................................................................................................... 125 5.4 Correlations Between Bridge Washing Practices and IC Analysis

Results .................................................................................................... 128 5.5 Correlations Between Deicing Agent Usage and Tape Test Results ..... 131 5.6 Correlations Between Deicing Agent Usage and IC Analysis Results .. 133 5.7 Correlations Between IC Analysis Results and Tape Test Results ........ 136

5.7.1 Correlations Between Cluster IC Analysis Results and Tape

Test Results ................................................................................ 137 5.7.2 Correlations Between Field Bridge IC Analysis Results and

Tape Test Results ....................................................................... 140 5.7.3 Correlations Between Sample Location IC Analysis Results

and Tape Test Results ................................................................ 144

5.8 Correlations Between Tape Test Results and Condition Ratings of

Field Bridges .......................................................................................... 148 5.9 Summary of Correlations ....................................................................... 153

6 CONCLUSIONS............................................................................................. 158

6.1 Summary ................................................................................................ 158 6.2 Overview of Results ............................................................................... 158 6.3 Main Takeaways .................................................................................... 164 6.4 Future Work ........................................................................................... 166

REFERENCES ........................................................................................................... 169

Appendix

A CLUSTER BRIDGE CHARACTERISTICS ................................................. 173 B PARAMETRIC COMBINATIONS ............................................................... 177 C FIELD DATA ENTRY SHEETS ................................................................... 180 D MATLAB Script for Digital Image Processing of Tape Samples .................. 204 E MAINTENANCE SURVEYS ........................................................................ 208

E.1 Original Survey ...................................................................................... 208 E.2 Follow-Up Survey for Prior Participants ............................................... 209 E.3 Follow-Up Survey for Agencies with No Prior Response ..................... 211

F SURVEY DATA ............................................................................................ 212 G TAPE SAMPLE RESULTS ........................................................................... 218

ix

G.1 Tape Sample Images .............................................................................. 218 G.2 Tape Test Results Data Tables ............................................................... 225 G.3 Tape Test Results Standard Deviations ................................................. 232 G.4 Tape Test Results Graphs ...................................................................... 234

H ION CHROMATOGRAPHY ANALYSIS RESULTS .................................. 241

H.1 IC Analysis Results Data Tables............................................................ 241 H.2 IC Analysis Results Standard Deviations .............................................. 248

x

LIST OF TABLES

Table 3.1: Phase 3 Cluster Overview ........................................................................ 38

Table 3.2: Characteristics of Phase 3 Reference Bridges ......................................... 41

Table 3.3: Summary Bridge Statistics of Phase 3 Clusters ....................................... 43

Table 3.4: Characteristics of Phase 3 Field Bridges ................................................. 45

Table 3.5: Characteristics of Bridges NH 019700810009300 and NH

017700960015300.................................................................................... 46

Table 3.6: Standard Sample Area Location Descriptions ......................................... 55

Table 4.1: Field Bridge Qualitative Assessment Condition Ratings......................... 65

Table 4.2: Standard Sample Area Location Descriptions of Colorado Bridges ....... 67

Table 4.3: Maintenance Manual Review .................................................................. 91

Table 4.4: Regional Deicing Agent Use Statistics .................................................. 101

Table 5.1: Summary of Overall Bridge Maintenance Manual Ratings and

Average Percent Area of Rust Particles Greater than or Equal to an

1/8 inch for Each Agency/Cluster .......................................................... 124

Table 5.2: Summary of Bridge Washing Practice Ratings and Average Percent

Area of Rust Particles Greater than or Equal to an 1/8 inch for Each

Agency/Cluster ...................................................................................... 126

Table 5.3: Summary of Bridge Washing Practice Ratings and Average Chloride,

Nitrate, and Sulfate Concentrations for Each Agency/Cluster .............. 128

Table 5.4: Summary of Deicing Agent Usage and Average Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch for Each

Agency/Cluster ...................................................................................... 131

Table 5.5: Summary of Deicing Agent Usage and Average Chloride

Concentrations for Each Agency/Cluster............................................... 134

Table 5.6: Summary of Average Chloride, Nitrate, and Sulfate Concentrations

and Average Percent Area of Rust Particles Greater than or Equal to

an 1/8 inch of Each Cluster .................................................................... 138

xi

Table 5.7: Summary of Average Chloride, Nitrate, and Sulfate Concentrations and

Average Percent Area of Rust Particles Greater than or Equal to

an 1/8 inch of Each Field Bridge ........................................................... 141



an 1/8 inch of Each Standard Sample Area Location ............................ 145

Table 5.9: Summary of Average Percent Area of Rust Particles Greater than or

Equal to an 1/8 inch, SCR, and Weighted Girder CS Ratings for

Each Field Bridge .................................................................................. 149



an 1/8 inch Sorted from Highest to Lowest of Each Standard Sample

Area Location......................................................................................... 155

Table 6.1: Summary of Chapter 5 Cause and Effect Correlations .......................... 159

Table 6.2: Summary of Chapter 5 IC Analysis and Tape Test Correlations ........... 160

Table 6.3: Summary of Chapter 5 Methods to Assess UWS Performance

Correlations ............................................................................................ 161

Table 6.4: Summary of Cluster and Field Bridge Data Types ................................ 162

Table A.1: CO Cluster Bridges ................................................................................ 173

Table A.2: CT Cluster Bridges ................................................................................ 174

Table A.3: IA Cluster Bridges ................................................................................. 174

Table A.4: MN Cluster Bridges ............................................................................... 175

Table A.5: NC Cluster Bridges ................................................................................ 175

Table A.6: NH Cluster Bridges ................................................................................ 176

Table A.7: OH Cluster Bridges ................................................................................ 176

Table B.1: Deicing Cluster Parametric Combinations ............................................. 177

Table B.2: Deicing + Coastal Cluster Parametric Combinations ............................ 178

Table B.3: Coastal Cluster Parametric Combinations ............................................. 179

xii

Table F.1: Maintenance Manual Responses ............................................................ 212

Table F.2: Washing Practices Responses ................................................................ 214

Table F.3: Deicing Agent Usage Responses ........................................................... 215

Table F.4: Deicing Agent Usage Statistics .............................................................. 217

Table G.1: Tape Test Results Cluster Standard Deviations ..................................... 232

Table G.2: Tape Test Results Field Bridge Standard Deviations ............................ 233

Table G.3: Tape Test Results Standard Sample Area Location Standard

Deviations .............................................................................................. 234

Table H.1: IC Analysis Results Cluster Standard Deviations .................................. 248

Table H.2: IC Analysis Results Field Bridge Standard Deviations ......................... 248

Table H.3: IC Analysis Results Standard Sample Area Location Standard

Deviations .............................................................................................. 249

xiii

LIST OF FIGURES

Figure 3.1: Phase 3 Cluster Locations ........................................................................ 39

Figure 3.2: Photo Example of Wide View of Bridge ................................................. 50

Figure 3.3: Photo Example of View of Bearing Location .......................................... 50

Figure 3.4: Photo Example of Wide View of Interior Girders ................................... 51

Figure 3.5: Photo Example of View of Girder Splice Plate ....................................... 51

Figure 3.6: Photo Example of Lateral Bracing to Girder Connection........................ 52

Figure 3.7: Photo Example of Overall View of General Environment of Bridge ...... 52

Figure 3.8: I-Girder Cross-Section Sample Locations ............................................... 54

Figure 3.9: Example of Complete Sample Area Photograph ..................................... 56

Figure 3.10: Example of Closer Perspective Sample Area Photograph ....................... 57

Figure 4.1: Typical Example of a Compact Rust Patina ............................................ 62

Figure 4.2: Typical Example of Small Rust Flakes.................................................... 63

Figure 4.3: Typical Example of Large Thick Rust Flakes ......................................... 63

Figure 4.4: Typical Web Patina of Colorado Bridges ................................................ 66

Figure 4.5: Typical Bottom of Bottom Flange Patina of Colorado Bridges .............. 68

Figure 4.6: Typical Flange Patina of Connecticut Bridges ........................................ 70

Figure 4.7: Typical Web Patina of Connecticut Bridges............................................ 70

Figure 4.8: Typical Web Patina of Iowa Bridges ....................................................... 72

Figure 4.9: Typical Interior Flange Patina of Iowa Bridges....................................... 73

Figure 4.10: Typical Interior Web Patina of Bridge MN 04019 .................................. 74

Figure 4.11: Typical Interior Flange Patina of Bridge MN 04019 ............................... 74

xiv

Figure 4.12: Exterior Flange and Web Patinas of the Fascia Girder of Bridge MN

19811 ....................................................................................................... 75

Figure 4.13: Typical Interior Flange and Web Patinas of Bridge MN 19811 .............. 76

Figure 4.14: Typical Flange and Web Patinas of Bridge MN 62861 ........................... 77

Figure 4.15: Typical Flange and Web Patinas of Bridge NC 190083 .......................... 78

Figure 4.16: Typical Exterior Flange and Web Patinas of Bridge NC 1290057 .......... 79

Figure 4.17: Typical Interior Flange Patina of Bridge NC 1290057............................ 79

Figure 4.18: Typical Exterior Flange and Web Patinas of Bridge NC 1290058 .......... 80

Figure 4.19: Typical Interior Flange Patina of Bridge NC 1290058............................ 81

Figure 4.20: Typical Flange and Web Patinas of Bridge NH 017201120011300 ....... 82

Figure 4.21: Typical Interior Flange Patina of Bridge NH 11101120017900 ............. 83

Figure 4.22: Typical Exterior Flange and Web Patina of Bridge NH

11101120017900 ..................................................................................... 83

Figure 4.23: Typical Interior Web Patina of Bridge NH 11101120017900 ................. 84

Figure 4.24: Typical Interior Flange Patina of Bridge NH 017701460003700 ........... 85

Figure 4.25: Typical Exterior Flange and Web Patinas of Bridge NH

017701460003700 ................................................................................... 85

Figure 4.26: Typical Exterior Flange Patinas of Ohio Bridges .................................... 87

Figure 4.27: Typical Interior and Exterior Web Patinas of Ohio Bridges ................... 88

Figure 4.28: Typical Interior Flange Patinas of Ohio Bridges ..................................... 88

Figure 4.29: Objective Manual Ratings, by Agency .................................................... 93

Figure 4.30: Subjective Manual Rating, by Agency .................................................... 94

Figure 4.31: Approximate Percentages of Bridges Washed, by Agency ..................... 96

Figure 4.32: Frequency of Bridge Washing, by Agency .............................................. 96

Figure 4.33: Frequency of Girder Washing, by Agency .............................................. 97

xv

Figure 4.34: Deicing Agent Usage, by Agency with Available Data ........................ 103

Figure 4.35: Average Percent Area of Rust Particles Greater than or Equal to an

1/8 inch, by Cluster ................................................................................ 105


1/8 inch, by Field Bridge ....................................................................... 107


1/8 inch, by Standard Sample Area Location ........................................ 109

Figure 4.38: Average Concentration of Chloride, Nitrate, and Sulfate Ions, by

Cluster .................................................................................................... 113

Figure 4.39: Average Concentration of Chloride, by Field Bridge ............................ 115

Figure 4.40: Average Nitrate Concentration, by Field Bridge ................................... 116

Figure 4.41: Average Sulfate Concentration, by Field Bridge ................................... 117

Figure 4.42: Average Concentration of Chloride, Nitrate, and Sulfate Ions, by

Standard Sample Area Location ............................................................ 119

Figure 5.1: Scatter Plot of Overall Bridge Maintenance Manual Ratings Versus

Average Percent Area of Rust Particles Greater than or Equal to an

1/8 inch for Each Agency/Cluster .......................................................... 124

Figure 5.2: Scatter Plot of Bridge Washing Practice Ratings Versus Average

Percent Area of Rust Particles Greater than or Equal to an 1/8 inch

for Each Agency/Cluster ........................................................................ 127


Chloride Concentrations for Each Agency/Cluster ............................... 129

Figure 5.4: Scatter Plot of Average Bridge Washing Practice Ratings Versus

Nitrate Concentrations for Each Agency/Cluster .................................. 129


Sulfate Concentrations for Each Agency/Cluster .................................. 130

Figure 5.6: Scatter Plot of Corrosive Solids’ Usages Versus Average Percent


Agency/Cluster ...................................................................................... 132

xvi

Figure 5.7: Scatter Plot of Corrosive Brines’ Usages Versus Average Percent


Agency/Cluster ...................................................................................... 132

Figure 5.8: Scatter Plot of Corrosive Solids’ Usages Versus Average Chloride

Concentrations for Each Agency/Cluster .............................................. 134

Figure 5.9: Scatter Plot of Corrosive Brines’ Usages Versus Average Chloride

Concentrations for Each Agency/Cluster .............................................. 135

Figure 5.10: Scatter Plot of Average Chloride Concentration Versus Average


of Each Cluster....................................................................................... 138

Figure 5.11: Scatter Plot of Average Nitrate Concentration Versus Average Percent

Area of Rust Particles Greater than or Equal to an 1/8 inch of Each

Cluster .................................................................................................... 139

Figure 5.12: Scatter Plot of Average Sulfate Concentration Versus Average


of Each Cluster....................................................................................... 139



of Each Field Bridge .............................................................................. 142

Figure 5.14: Scatter Plot of Average Nitrate Concentration Versus Average



Figure 5.15: Scatter Plot of Average Sulfate Concentration Versus Average





of Each Standard Sample Area Location ............................................... 146

Figure 5.17: Scatter Plot of Average Nitrate Concentration Versus Average Percent



Figure 5.18: Scatter Plot of Average Sulfate Concentration Versus Average Percent



xvii

Figure 5.19: Scatter Plot of SCR Versus Average Percent Area of Rust Particles

Greater than or Equal to an 1/8 inch of Each Field Bridge .................... 150

Figure 5.20: Scatter Plot of SCR Versus WGCS Rating of Each Field Bridge ......... 151

Figure 5.21: Scatter Plot of WGCS Rating Versus Average Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch of Each Field Bridge ..... 152

Figure 5.22: Scatter Plot of Each Standard Sample Area Location Listed for Each

Corresponding Average Chloride Concentration Versus Average

Percent Area of Rust Particles Greater than or Equal to an 1/8 inch ..... 155


Corresponding Average Nitrate Concentration Versus Average



Corresponding Average Sulfate Concentration Versus Average


Figure 6.1: Graph of Average Chloride Concentrations and Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch ....................................... 165

Figure G.1: Average Density of Rust Particles, by Cluster ...................................... 234

Figure G.2: Average Percent Area of Rust Particles Greater than or Equal to an

1/8 inch, by Cluster ................................................................................ 235

Figure G.3: Average Percent Area of Rust Particles Greater than or Equal to a

1/4 inch, by Cluster ................................................................................ 235


1/2 inch, by Cluster ................................................................................ 236

Figure G.5: Average Density of Rust Particles, by Field Bridge .............................. 236







xviii

Figure G.9: Average Density of Rust Particles, by Standard Sample Area

Location ................................................................................................. 238







xix

ABSTRACT

The long-term performance of uncoated weathering steel (UWS) bridges in the

United States is being assessed to update the current FHWA technical advisory regarding

the use of UWS in highway bridges. The main purpose of the larger research project, to

which the research described in this thesis contributed, is to develop quantitative

recommendations about environmental conditions and maintenance practices that may be

the most suitable for UWS highway bridges. Prior work included the development of a

national UWS GIS database in order to quantify environments. This database was used to

strategically select 21 UWS bridges for field evaluations in 7 states in order to qualitatively

assess the performance of rust patinas and collect rust samples for assessment using

quantifiable metrics. Rust samples were assessed by evaluating data from clear tape

adhesion tests and ion chromatography analyses. Part of this research also included

collecting and analyzing maintenance data collected from state highway agencies

throughout the United States. This data included information from 34 state highway

agencies’ bridge maintenance manuals and bridge washing practices from 33 state highway

agencies. Deicing agent usage data was also obtained from 39 state highway agencies and

existing databases. Correlations between the different data types that were collected were

assessed to investigate any trends in UWS bridge performance based on environmental

conditions and maintenance practices. The most significant finding from this research

involved differences in UWS performance as quantified by chloride concentrations and

rust particle sizes of surface rust based on interior, sheltered girder locations and exterior,

exposed girder locations. Quantifiable data obtained from this research will be useful for

xx

evaluating UWS bridge performance trends with a larger dataset in order to update national

specifications and maintenance practices involving UWS bridges.

1

Chapter 1

INTRODUCTION

1.1 Project Overview

Uncoated weathering steel (UWS) bridges have been in service in the U.S. since

1964. In the 1980’s, some states reported that the performance of their UWS bridges was

undesirable because they were experiencing faster corrosion rates than expected. Because

of this, research projects were conducted to assess what may cause undesirable

performance of UWS. AISI (1982) initiated a long-term project to study the performance

of UWS in different environments by inspecting 52 highway bridges. Furthermore,

Albrecht and Naeemi (1984) and Albrecht et al. (1989) conducted research that assessed

the performance of UWS and recommended guidelines for design, construction,

maintenance, and rehabilitation of UWS bridges. These studies are what led to the Federal

Highway Administration’s (FHWA) Technical Advisory (TA) 5140.22 issued on October

3, 1989. The TA contains general information on environments where UWS should be used

with caution, such as in marine coastal areas; areas with frequent high rainfall, high

humidity or persistent fog; industrial areas where concentrated chemical fumes may drift

directly onto the structure; grade separations in “tunnel-like” conditions; and low-level

water crossings. This TA mostly provides broad qualitative guidelines regarding the use of

UWS and states that “[f]urther work is needed to quantify and understand the performance

of uncoated weathering steel in a variety of circumstances and conditions.”

2

1.2 Project Objectives and Scope

Research through the Long-term Bridge Performance Program (LTBPP) has been

underway to better understand the performance of UWS in order to revise the FHWA 1989

TA with quantified data. The University of Delaware is working as a subconsultant to

Rutgers University under FHWA Contract #693JJ318F000133 to conduct this research on

assessing the long-term performance of UWS bridges in the U.S. The main goal of that

research project is to provide quantitative guidelines to define environments that cause

undesirable rates of corrosion in UWS bridges as well as develop a comprehensive database

and field inspection protocols for future UWS bridge evaluation. Maximizing opportunities

to collect quantifiable data on UWS bridge performance will provide updates to generic

language used in TA 5140.22 regarding unsuitable environments for UWS to be used.

Phase 1 of this research project consisted of the development of field-test protocols

and a national inventory of UWS highway bridges. Phase 2 of this project then focused, in

part, on creating a national GIS database of UWS bridges to associate various geographic

and climate variables to each specific bridge. Thirteen bridges in five states were then

selected based on a data-driven selection process and a survey of bridge owners to better

understand the most critical issues affecting UWS bridges. Phase 2 also included field

inspections of selected bridges as well as reviewing inspection reports of additional

bridges. The results were then analyzed to identify preliminary correlations between UWS

performance and parameters that were identified and collected. Additional clusters were

then identified for further study in Phase 3 of this research project, which is currently being

conducted and is the focus of this thesis. The specific objectives of Phase 3 include:

1. determining seven clusters that encompass environments of concern for use

of UWS in highway bridges (i.e., deicing, coastal, and a combination

deicing and coastal) and selecting bridges within them for field evaluations

and additional data collection.

3

2. performing field evaluations of field bridges selected from each of the seven

clusters.

3. updating LTBPP protocols developed in Phase 1 for field evaluations of

UWS bridges.

4. conducting a survey regarding maintenance practices and deicing agent

usage for each agency in the U.S.

5. analyzing the results to identify preliminary correlations between bridge

maintenance and deicing practices, environmental conditions surrounding

UWS bridges, and UWS bridge performance.

The specific objectives of the research reported here are to contribute towards the above

goals for Phase 3 by:

1. assisting in selecting and evaluating field bridges from each of the seven

clusters.

2. summarizing and synthesizing past research related to this research project

in a literature review.

3. conducting a survey regarding maintenance practices and deicing agent

usage for each agency in the U.S.

4. compiling and interpreting tape test and IC analysis results.

5. analyzing results from the survey, tape test, and IC analyses to identify

preliminary correlations between bridge maintenance and deicing agent

usage practices with UWS performance.

1.3 Thesis Organization

The thesis organization is as follows:

• Chapter 1 includes an overview of the research background,

objectives, scope, and organization.

• Chapter 2 discusses background information about UWS and a

literature review of past research conducted relative to UWS.

• Chapter 3 reviews the methodology behind bridge selection, field

evaluation of bridges, as well as data collection and analysis.

4

• Chapter 4 presents the results of the different data types that were

collected.

• Chapter 5 analyzes correlations between the results presented in

Chapter 4.

• Chapter 6 summarizes work carried out in Phase 3, findings from

Chapters 4 and 5, as well as proposes future work.

5

Chapter 2

BACKGROUND

2.1 Uncoated Weathering Steel Background

Weathering steel, originally trademarked as Cor-Ten, is a high strength, low alloy

steel that was first developed by U.S. Steel in the 1930s. In 1964, UWS was developed for

application in bridges and has currently been used as a high-performance steel (HPS) after

further development in the 1990s. One purpose of developing this new material was to

avoid painting the steel due to its capability of being able to form a stable layer of rust

when exposed to weather. The thin layer of rust that coats the steel is known as the patina,

which protects against future corrosion. The patina is produced by the concentration of

alloying elements, such as copper, chromium, nickel, phosphorous, silicon, and manganese

that make up 3-5 wt.% of the steel material. The main goal of UWS is to reduce the overall

costs of construction and maintenance by reducing the thickness and weight of steel

required due to improved mechanical properties as well as by avoiding the need to paint

the steel.

2.1.1 Environmental Effects on Corrosion of UWS

When exposed to different environments, corrosion resistance characteristics of

UWS are affected in various ways. The corrosion rate of UWS increases when the relative

humidity exceeds about 70%. A high percentage of relative humidity effects the time of

wetting (TOW), which is the length of time a metal remains wet enough to corrode at an

appreciable rate (Albrecht et al., 1989). In order to achieve the most desired protective

capability of the rust layer, wet and dry cycles of nearly equal length should allow the

6

patina to form and provide satisfactory corrosion resistance (Raman et al., 1988), yet it is

noted that corrosion cannot occur in the absence of a wetting cycle.

Three main categories of environments that have often been used to describe UWS

performance are marine, industrial, and rural. Albrecht and Naeemi (1984) describes these

environments and the effects they have on corrosion of UWS. Rural environments are

usually the least aggressive towards causing corrosion issues due to relatively low amounts

of air pollutants, such as sulfur oxides and chlorides. Industrial environments cause

corrosion issues due to the sulfur oxides produced by burning automotive and fossil fuels.

The sulfur oxides tend to only be aggressive towards corrosion when the relative humidity

exceeds 60%. Coastal environments contain airborne salt sprays containing chloride that

can keep the UWS damp for long periods of time. Shorter drying cycles as well as the

presence of salt crystals on the UWS in this environment inhibits the ability for the

protective rust layer to form and results in poor corrosion resistance. Morcillo et al. (2013)

summarizes how differences in chloride content from airborne salt spray, sulfur oxides

from industrial pollution, and TOW can cause variations in corrosion characteristics.

Furthermore, the chemical composition of the steel as well as its orientation and exposure

to the atmosphere also contribute to varying corrosion characteristics. These factors cause

variability in the protective capabilities of the patina that forms on UWS and is why further

research has been required.

2.1.2 Advantages of UWS

One of the main advantages of UWS is the savings it can provide in terms of

construction costs. Morcillo et al. (2013) states that early Cor-Ten steel provided 30%

improvement in mechanical properties as compared to carbon steel (CS). This is due to

higher phosphorous content in early Cor-Ten steel, which raised the yield and tensile

7

strengths (Morcillo et al., 2014). Therefore, the thickness and weight of the steel could be

reduced to provide cost savings on materials.

In terms of maintenance costs, Albrecht et al. (1989) notes that UWS can eliminate

the need for initial and periodic painting of the superstructure due to its enhanced

atmospheric corrosion resistance. Avoiding painting and repainting bridges also eliminates

the need to close lanes and disrupt traffic during painting operations. The corrosion

resistance of UWS is about equal to two times that of copper bearing CS, which is

equivalent to four times CS without copper (Albrecht and Naeemi 1984). Morcillo et al.

(2013) mentions that UWS bridges are more economical than painted CS bridges after

about 15 years in moderately aggressive environments due to UWS not requiring paint to

prevent corrosion.

2.1.3 Disadvantages of UWS

Although UWS can be beneficial in terms of cost savings and atmospheric

corrosion resistance, there are also some disadvantages. When considering TOW and

moisture containing chlorides, structural detailing of UWS bridges becomes a concern.

Expansion joints are known to have issues with leaking. Therefore, UWS within the

vicinity of leaking expansion joints tends to perform poorly and thus it is recommended to

paint the ends of girders (FHWA 1989). Albrecht and Naeemi (1984) mentions how girder

ends on either side of expansion joints have been painted to prevent progressive corrosion

of UWS in these areas. Albrecht et al. (1989) also advises that in order to avoid these

problems created by leaking joints, bridges should have a continuous superstructure, fixed

or integral bearings at piers and abutments, and no bridge deck expansion joints. Because

of these issues involving leaking joints with UWS bridges, design limitations must be

considered and can hinder the usage of UWS in certain scenarios. It should be noted that

8

these structural detailing concerns are not specific to UWS bridges and can affect all types

of bridges.

One of the major disadvantages is the uncertainty regarding performance of UWS

bridges based on exposure conditions as mentioned in Section 2.1.1. The most severe

exposure conditions that have been found to cause issues with rust patina performance

include environments where high concentrations of chloride are present, such as coastal

environments and environments where deicing agents are used. This thesis mainly focuses

on attempting to resolve these uncertainties regarding UWS bridge performance in coastal

and deicing environments.

2.2 Methods Previously Used for Assessing UWS

The proceeding section focuses on four methods most commonly used for

assessing UWS that were described in reviewed literature. These methods include visual

inspection, clear tape adhesion test, ion chromatography (IC) analysis, and x-ray

diffraction (XRD) analysis.

2.2.1 Visual Inspection

In the midst of developing more practical methods for quantitatively evaluating

UWS, visual inspections have been utilized to assess the protectiveness of the rust layer.

Hara et al. (2006) reports on how the appearance of the rust layer has been categorized into

5 indices developed by the Japan Iron and Steel Federation (JISF) and Japan Association

of Steel Bridge Construction (JASBC). The indices are based on the rusts’ flaking

characteristics, size, color, and thickness. The appearance index is correlated with

corrosion rates of UWS that are based on thickness loss vs. exposure time, which allows

for corrosion rates of UWS to be estimated Hara et al. (2006).

9

Crampton et al. (2013) reported on Iowa DOT’s evaluation of their UWS bridge

inventory. Visual inspection methods were used to assess the quality of UWS patinas in

environments with deicing salt usage (in Iowa, specifically). The performance of weathering

steel patinas was assessed according to color of the rust and size of the scale (referred to as rust

flakes in this thesis). Crampton et al. (2013) refers to the National Cooperative Highway Research

Program’s (NCHRP) guidelines to evaluate the condition of the oxide layer on weathering steel

structures based on color and texture (Albrecht and Naeemi, 1984). In terms of color of the oxide

layer:

• yellow orange indicated initial stages of exposure

• light brown indicated early stages of exposure

• chocolate brown to purple brown indicated development of protective

oxide

• black indicated nonprotective oxide

In terms of texture of the oxide layer:

• tightly adherent, capable of withstanding hammering or vigorous wire

brushing indicated a protective oxide

• dusty indicated early stages of exposure which should change after a

few years

• granular was a possible indication of problems depending on length of

exposure and location of structure

• small flakes (6mm (~1/4 in.) in diameter) was an initial indication of

nonprotective oxide

• large flakes (12mm (~1/2 in.) in diameter or greater) indicated

nonprotective oxide

• laminar sheets or nodules indicated nonprotective oxide and severe

corrosion

10

Crampton et al. (2013) notes that good patina performance was typically indicated

by a fine grained, dark brownish-black, tightly adhered, stable rust layer on the surface of

the weathering steel. Poor patina performance was typically indicated by the formation of

loose rust scale caused by the formation of a less protective crystalline oxide rust layer. It

was also found that rust scale appearance is proportional to chloride content where

weathering steel surfaces with higher concentrations of chloride in the oxide layer were

found to have developed larger, thicker rust flakes in the patina.

The colors of corrosion products (ferrous oxyhydroxides) relative to UWS have

been discussed by Cornell and Schwertmann (2006). Goethite and akageneite appear as a

yellow-brown color, lepidocrocite appears as an orange color, maghemite appears as a

brown to brownish red color, and magnetite appears black. These corrosion products are

discussed in more detail in Section 2.2.4 and Section 2.3.

AASHTO also has methods established for visually inspecting UWS bridge girders;

however, the methods are not specific to UWS and encapsulate all steel open girders

regardless of protection system (AASHTO, 2013; 2014; 2017). Condition state (CS)

ratings are used to visually assess performance of the girders. CS1 refers to good condition,

CS2 refers to fair condition, CS3 refers to poor condition and CS4 refers to severe

condition. In terms of corrosion, CS1 includes no visually observed corrosion, CS2

includes visual observation of freckled rust indicating that corrosion of the steel has

initiated, CS3 includes visual observation of section loss or pack rust being present, and

CS4 includes visual observation of corrosion defects that impact strength or serviceability.

Each CS is measured in terms of the sum of all the lengths in feet of visually observed

defects along each girder.

11

2.2.2 Clear Tape Adhesion Test

One method for quantitatively evaluating the performance of UWS is known as the

clear tape adhesion test. McConnell et al. (2014 a) discusses how this method can be used

to analyze the sizes and spatial densities of corrosion products using digital image

processing. Standard procedures for the clear tape adhesion test have been developed in

order to provide simple evaluation methods (McConnel and Shenton, 2018). The procedure

includes adhering a 4 to 5 in. long piece of clear tape to the surface of the UWS girder.

Next, a rubber “J” roller is used to roll over the tape with 10 passes using firm pressure

(e.g., approximately 2 lbs. of normal force through the roller). Then the tape is slowly

peeled off, taking no longer than approximately 5 seconds to remove with a shallow angle

between the tape and the steel surface. The tape sample is then adhered to a clean sheet of

white paper to be used for image processing. In Phase 2 of this research project it was found

that evaluating the percent area of rust particle sizes that adhered to the tape, particularly

the percent area of rust particles that were greater than or equal to an 1/8 inch, correlated

with inspectors’ visual assessments of UWS bridges found in inspection reports

(McConnell et al., 2014 b).

2.2.3 Ion Chromatography (IC) Analysis

In Phase 2 of this research project, IC analyses were performed on rust samples

collected from each UWS bridge that was evaluated. The chloride, nitrate and sulfate ion

concentrations were recorded and compared with other data types in order to assess

correlations related to corrosion; however, no strong correlations were found (McConnell

et al., 2014 b).

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2.2.4 XRD Analysis

Collection of rust samples from UWS bridges are being used with X-ray Diffraction

(XRD) analysis to assess the corrosion products that form in different environments and at

different stages of corrosion. XRD analysis can be used to determine the proportions of

various ferrous oxyhydroxides found in these rust samples (McConnell et al., 2014 a).

Morcillo et al. (2014) states that the corrosion products form as a result of reactions

between iron and the environment, therefore, the composition of the rust layer varies

depending on surface electrolytes and atmospheric exposure conditions. The three main

ferrous oxyhydroxides that have been found to form on UWS are goethite (alpha-FeOOH),

akageneite (beta-FeOOH), and lepidocrocite (gamma-FeOOH). Lepidocrocite is

considered the initial corrosion product that forms on UWS. Goethite is formed in acidic

solutions and is transformed from lepidocrocite. The formation of goethite has been found

to be dependent on atmospheric sulfate concentrations and temperature (Morcillo et al.

2014). Akaganeite has been found in coastal environments where the surface electrolytes

contain chlorides. Although XRD analysis can be used to determine the proportions of

these ferrous oxyhydroxides, Morcillo et al. (2014) mentions that it has issues

distinguishing between magnetite and maghemite, which are two minor corrosion products

found in UWS. Because of this, Infrared Spectroscopy (IR) and Mossbauer Spectroscopy

(MS) analysis methods also tend to be used to determine the composition of rust samples.

The combination of these three methods allows for a more accurate correlation between

the corrosion products formed on UWS and atmospheric exposure conditions.

2.3 Past Field Studies

As mentioned in Section 1.1, research projects conducted by AISI (1982), Albrecht

and Naeemi (1984), and Albrecht et al. (1989) are what led to the FHWA TA (1989). The

TA concluded that UWS will perform satisfactorily in atmospheric chloride levels

13

averaging up to at least 0.5 mg/100cm2/day. Also, the United Kingdom Standard BD/7/01

recommended that UWS should not be used when the sulfur trioxide level exceeds an

average of 2.1 mg/100cm2/day; however, this value is rarely exceeded in the U.S.

(Highways Agency, 2001). The accuracy of these limits is not well established as well as

the influence of many other variables, which is why there is a need for further research.

The proceeding sections will focus on research performed after the FHWA advisory that

were done to attempt to better quantitatively define these environmental guidelines.

2.3.1 Coastal Environment Field Studies

One of the environments in which field studies have been conducted is coastal

environments. Coastal environments have not been well quantified and are typically

classified simply as being close to the ocean. As a result of being close to the ocean, these

environments have relatively high chloride concentrations from air-borne salt particles. The

chloride content of the environments was assessed in order to correlate this atmospheric

condition with corrosion characteristics of UWS. Field studies that were performed in

coastal environments focused on the effects chloride concentrations and different chemical

compositions of UWS had on atmospheric corrosion rates, the rust phases that formed on

UWS, and the relationship of chloride concentrations and physical corrosion

characteristics.

2.3.1.1 Relationship Between Atmospheric Corrosion Rates and Chloride

Concentrations

Four field studies focused on evaluating atmospheric corrosion rates of UWS in

coastal environments. These studies include Cook et al. (1998), Oh et al. (1999), Diaz et

al. (2018), and Saha (2013) compared UWS specimens exposed in various coastal

environments.

14

Cook et al. (1998) observed atmospheric corrosion rates of ASTM A-588 UWS

specimens (note specific steel specifications are reported herein wherever they are given

in the cited literature) exposed in three different coastal environments in México, one

coastal environment in Kure Beach, North Carolina and one industrial environment in

Bethlehem, Pennsylvania. Specimens that were exposed in atmospheres with higher

chloride concentrations, such as in Veracruz, Mexico (5.20 mg/100cm2/day) and Kure

Beach, North Carolina (3.11 mg/100cm2/day) experienced higher rates of atmospheric

corrosion (207 µm/y and 164 µm/y, respectively) as compared to specimens exposed in

atmospheres with lower chloride concentrations, such as in Campeche, Mexico (0.45

mg/10cm2/day with a corrosion rate of 15 µm/y). The study also concluded that

environments, such as Coatzacoalcos, Mexico (chloride concentration of 0.90

mg/100cm2/day) with relatively higher mean annual temperature (26°C, 79°F), relative

humidity (75%), and TOW (500 hours/”mth”, where “mth” is assumed to be months based

on typical TOW numbers for coastal environments in the United States) can cause

increased corrosion rates of UWS (300 µm/y). The environmental conditions mentioned

from the Cook et al. (1998) study were obtained from monitoring the exposure sites over

the course of three years.

Oh et al. (1999) performed a field study similar to Cook et al.’s (1998) in which

UWS specimens (ASTM A-242 and A-588), a plain carbon steel specimen, and a copper

bearing steel (steel with copper added to increase corrosion resistance) specimen were

exposed to a coastal environment in Kure Beach, NC (chloride concertation of 1.07

mg/100cm2/day). The specimens were also exposed to industrial and rural environments in

Bethlehem, PA and Saylorsburg, PA, respectively. The field study found that when

comparing corrosion rates of UWS specimens with the same chemical compositions in

each of the three environments, specimens exposed in the coastal environment had higher

15

corrosion rates (between 5.57 µm/y and 10.11 µm/y) than the ones exposed in the industrial

(between 1.33 µm/y and 1.43 µm/y) and rural environments (between 1.85 µm/y and 2.62

µm/y).

Diaz et al. (2018) also compared the corrosion rates of ASTM A-588 and ASTM

A-242 UWS specimens between environments with varying chloride concentrations in

Spain. Within the first year of exposure, UWS specimens in the coastal environments of

Cabo Vilano-1 (chloride concentration of 0.204 mg/100cm2/day) and Cabo Vilano-2

(chloride concentration of 0.714 mg/100cm2/day) experienced corrosion rates of about 30

µm/y and 50 µm/y, respectively. Similarly, Wang et al. (2013) assessed the corrosion rates

of five different UWS specimens exposed for five years in the coastal environment of

Qingdao, Japan (chloride deposition of 0.250 mg/100cm2/day) and reported rates of about

0.05 mm/y between each specimen.

Saha (2013) evaluated the corrosion rates of UWS specimens exposed in the coastal

environment of Digha, India (0.83 mg NaCl/100cm2/day and trace amounts of sulfur

oxide) and compared them with specimens exposed in Chennai, India (0.42 mg

NaCl/100cm2/day and 16.5 mg sulfur oxide/100cm2/day) and Jamshedpur, India (trace

amounts of NaCl and 22 mg sulfur oxide/100cm2/day). After the first 18 months of

exposure, the UWS specimens exposed in the coastal environment of Digha had the highest

corrosion rates (20.2 µm/y) as compared to Chennai (13.4 µm/y) and Jamshedpur (10.3

µm/y). After 42 months of exposure, the corrosion rate continued to increase for UWS

specimens exposed in the coastal environment of Digha (18.2 µm/y), while corrosion rates

began to stabilize for specimens exposed in the two industrial sites of Chennai and

Jamshedpur (12.3 µm/y and 9.5 µm/y, respectively). Saha (2013) claims that the increasing

corrosion rate after 42 months of exposure is due to the high concentration of chloride in

the atmosphere.

16

2.3.1.2 Rust Phases Formed in Coastal Environments

Furthermore, some field studies also evaluated the rust phases that formed on UWS

specimens in coastal environments. These studies include Oh et al. (1999), Wang et al.

(2013), Diaz et al. (2018), Kamimaru et al. (2005), and Hara et al. (2006). Each of these

studies attempted to draw correlations between corrosion products and corrosion rates of

UWS.

Oh et al.’s (1999) study found that UWS specimens (ASTM A-242 and A-588)

exposed in the coastal environment of Kure Beach, NC had the highest fractions of

superparamagnetic goethite (~80%) relative to the other rust products that formed

(magnetic goethite, lepidocrocite, and maghemite) as compared to the copper bearing

specimen (29%). The copper bearing specimen also exhibited the highest corrosion rate in

the coastal environment (>50 µm/y) as compared to the UWS specimens, which ranged

from about 5 µm/y to 20 µm/y. Oh et al. (1999) states that an increase in the fraction of

superparamagnetic goethite decreases the mean particle size of goethite. This can enhance

the function of the protective rust layer by densely compacting rust particles which can

prevent water and oxygen penetration to the steel. Therefore, Oh et al. (1999) concludes

that the corrosion rate may be related to the fraction of superparamagnetic goethite that

forms in the inner layer of rust because of its protective capabilities. Wang et al. (2013)

also notes how goethite is the most stable, compact, and dense corrosion product formed

by UWS. Alloying additions in UWS result in goethite and lepidocrocite corrosion

products that are denser and more stable than ones that form on CS, much like Oh et al.

(1999) reported.

Oh et al. (1999), Wang et al. (2013), and Diaz et al (2018) each reported that the

main corrosion products that formed on all of the UWS specimens, independent of

exposure conditions were lepidocrocite, goethite and spinel-type iron oxide. However,

17

Diaz et al. (2018) also reported the presence of akaganeite on UWS specimens exposed in

Cabo Vilano-2, which has the highest chloride deposition of 0.714 mg/100cm2/day.

Akaganeite formed in the innermost region of the rust layer due to the accumulation of

chloride ions. Notable pitting was observed in this region using polarized light microscopy

to view cross-sections of the rust layers.

2.3.1.3 Goethite/Lepidocrocite Relation to Protective Ability Index in Coastal

Environments

To further analyze corrosion rates in coastal environments, Kamimaru et al. (2005)

performed field studies involving corrosion of UWS in Japan. UWS specimens (JIS SMA

490AW) were exposed to atmospheres with different amounts of air-borne sea salt

particles. The corrosion products formed on the UWS specimens were analyzed using

XRD. The mass ratio of crystalline goethite to lepidocrocite (goethite/lepidocrocite was

found to be closely related to corrosion rates of UWS when the amount of air-borne salt

was less than 0.2 mg NaCl/100cm2/day. However, when the air-borne salt concentrations

were more than 0.2 mg NaCl/100cm2/day, such as in coastal environments, the mass ratio

of crystalline goethite to the total mass of lepidocrocite, akaganeite, and iron oxide

(expressed as goethite/lepidocrocite*) was found to have a correlation with the rate of

corrosion. Corrosion rates of more than 0.01 mm/year were observed when

goethite/lepidocrocite* was less than 1 and when specimens were exposed in coastal

environments with air-borne salt concentrations greater than 0.2 mg/100cm2/day.

Kamimaru et al. (2005) concluded that these correlations present a possibility for

evaluating the protective ability of the patina formed on UWS in coastal environments with

relatively higher chloride concentrations.

Hara et al. (2006) furthered Kamimaru et al.’s (2005) study of assessing

correlations between corrosion rates of UWS bridges with JIS G3144 type (SMA490W)

18

weathering steel exposed in coastal environments in Japan and the composition of the rust

layers that formed. The bridges located in coastal environments were exposed to air-borne

sea salt concentrations of more than 0.1 NaCl mg/100cm2/day. The goethite/lepidocrocite*

of the rust layer that formed on each bridge was analyzed by collecting rust samples and

using XRD. Hara et al. (2006) found that when goethite/lepidocrocite* is less than 1, the

corrosion rate can be classified by the mass ratio of akaganeite and iron oxide to the total

mass of lepidocrocite, akaganeite, and iron oxide (expressed as (akageneite + iron

oxide)/lepidocrocite*). Whether the corrosion rate is more or less than 0.01 mm/y is

determined by the ratio of (akageneite + iron oxide)/lepidocrocite* being greater than or

less than 0.5, respectively. Hara et al. (2006) then concluded that both

goethite/lepidocrocite* and (akageneite + iron oxide)/lepidocrocite* are useful for

quantitatively evaluating the protectiveness of the patina formed on UWS bridges.

2.3.1.4 Effects on Corrosion Rates Based on Chemical Composition of UWS in

Coastal Environments

Two field studies, Wang et al. (2013) and Cano et al. (2017) evaluated the effects

of chemical compositions of UWS on corrosion when exposed in coastal environments.

Wang et al. (2013) tested UWS specimens (W400QN, W450QN, SPA-H, 09CuPTiRE and

WGJ510C) exposed for 6 years in the coastal environment of Qingdao, Japan (chloride

deposition of 2.50 mg/100cm2/day). Wang et al. (2013) reported that chromium could be

an effective alloying element in UWS to reduce corrosion rates when exposed to higher

atmospheric chloride concentrations. The UWS specimen with the highest chromium

content (0.60 wt.%) showcased the lowest corrosion rates after 5 years of about 0.02 mm/y

as opposed to the UWS specimen with the lowest chromium content (0.013 wt.%) which

showcased corrosion rates after 5 years of about 0.03 mm/y.

19

Cano et al. (2017) performed a field study on UWS specimens (ASTM A-242 and

A-588) with varying chemical compositions exposed in two different locations in Cabo

Vilano, Spain (Cabo Villano-1 and Cabo Vilano-2 had chloride depositions of 0.20

mg/100cm2/day and 0.71 mg/100cm2/day, respectively). Cano et al. (2017) concluded that

the addition of 0.5 wt.% chromium noticeably improved the UWS specimen’s atmospheric

corrosion resistance. Two UWS specimens with 0.50 wt.% of chromium showcased

corrosion rates of about 39 μm/y for 2 years of exposure (chloride depositions of 0.71

mg/100cm2/day) and 16 μm/y for 3 years of exposure in a less severe environment relative

to their specimens exposed for 2 years (chloride depositions of 0.20 mg/100cm2/day). This

was compared to two UWS specimens with 0.08 wt.% chromium that had corrosion rates

of about 44 μm/y for 2 years of exposure in an environment with chloride depositions of

0.71 mg/100cm2/day and 18 μm/y for 3 years of exposure in an environment with chloride

depositions of 0,20 mg/100cm2/day. More importantly, Cano et al. (2017) found that the

best atmospheric corrosion resistance was obtained from UWS specimens with a nominal

value of 3.0 wt.% of nickel. One specimen, with an actual composition of 2.83 wt.% of

nickel, had corrosion rates of about 32 μm/y for 2 years of exposure in an environment

with chloride depositions of 0.71 mg/100cm2/day and 13 μm/y for 3 years of exposure in

an environment with chloride depositions of 0.20 mg/100cm2/day. This was compared to

two UWS specimens with 0.12 wt.% nickel that had corrosion rates of about 44 μm/y for

2 years of exposure in an environment with chloride depositions of 0.71 mg/100cm2/day

and 18 μm/y for 3 years of exposure in an environment with chloride depositions of 0.20

mg/100cm2/day.

20

2.3.1.5 Relationship Between Physical Corrosion Characteristics and Chloride

Concentrations

Some field studies focused on physical characteristics of rust layers that formed on

UWS specimens that were exposed in coastal environments. Two of these studies were

done along the Gulf of Mexico in the United States (McDad el al., 2000 and Raman, 1998).

A third was conducted in Spain (Díaz et al., 2018) and a fourth was conducted in India

(Saha, 2013).

McDad et al. (2000) assessed 40 bridges that used UWS and were located in severe

coastal environments as well as other environments within the state of Texas. Rust samples

were collected and section loss of the steel due to corrosion was assessed using ultrasonic

testing. It was found that all thickness measurements were equal to or greater than the

specified nominal thicknesses. McDad et al. (2000) concluded that this is due to the steel

plates of the girders being rolled with a thickness that is slightly greater than the nominal

thickness. Also, when steel rusts, the rust that forms is thicker than the base metal that it

replaces, thus increasing the thickness. The chloride concentrations that were also

measured from the rust samples were found to be inconsistent. Therefore, no correlations

between chloride concentration and corrosion could be made due to no observed section

loss and inconsistent chloride concentrations.

Raman (1988) also assessed 7 UWS bridges built with ASTM A-588 in coastal

environments, specifically within the state of Louisiana. Rust samples were taken to find a

correlation between rust particle sizes and chloride levels in the rust. A pitting evaluation

was performed and the morphology of the rust was determined using scanning electron

microscopy (SEM) and energy dispersive X-ray analysis (EDXA). Raman (1988) found

that higher chloride concentrations of 2.5 ppm to 6.5 ppm found in the rust resulted in

larger rust particles of a maximum size of 5.0 mm and an average size of 3.0 mm. Lower

21

chloride of 0.5 ppm to 1.5 ppm only resulted in rust particles of a maximum size of 0.75

mm and an average size of 0.5 mm.

These two studies (McDad et al., 2000 and Raman, 1988) give contradictory results

based on similarities between the coastal environments they were performed in. Both field

studies were performed in areas along the Gulf of Mexico, yet McDad et al. (2000) reported

increases in steel thicknesses while Raman (1988) reported instances of section loss and

flaking of large rust particles (maximum size of 5.0 mm and average size of 3.0 mm) in

atmospheres with relatively high chloride concentrations (2.5 ppm to 6.5 ppm). The UWS

bridges McDad et al. (2000) evaluated were older (about 20 years old) as compared to the

ones Raman (1988) inspected (about 12 years old). This is also contradictory of their results

being that UWS bridges that have been in service longer tend to experience more section

loss. It would have been helpful to know the chloride concentrations from the UWS bridges

evaluated by McDad et al. (2000) in order to make comparisons with the chloride

concentrations reported in the Raman (1988) study. One conclusion that can be drawn from

both of these studies is that when UWS is exposed to the sun and rain in these

environments, a protective oxide forms and rust particles are prevented from growing into

coarse flakes. Raman (1988) notes that this is due to the wet-dry cycle the UWS is able to

experience when exposed in these situations, such as on exterior portions of fascia girders.

The interior locations of bridge girders do not experience the same natural wet-dry cycle

as the exterior portions of the fascia girders and were found to perform worse relative to

the exterior locations. Raman (1988) indicates that rust formed at interior (sheltered)

locations tended to be “flaky or sheet-type”. Raman (1988) also mentions that chloride and

salt accumulation was higher at interior girder locations.

Saha (2013) researched UWS specimens exposed from 18 to 42 months within

various environments in India. One of these environments was the coastal environment of

22

Digha, India (0.83 mg NaCl/100cm2/day). After 42 months of exposure, Saha (2013)

reported that the UWS specimens exposed in Digha had an appreciable difference in oxide

film morphology as compared to specimens exposed at the other two industrial

environment test sites. An uneven dark brown, coarse granular oxide film layer had

developed on the UWS specimens. More pitting with a blackish appearance was also

observed on the specimens exposed in the coastal environment. Refer to section 2.2.1 for

information regarding assessments of UWS performance in terms of the color of oxide

layers formed on rust patinas.

Research was conducted by Díaz et al. (2018) on ASTM A-242 and ASTM A-588

UWS specimens in different types of environments within Spain. Visual inspection of the

UWS specimens was performed after 5 years of exposure. A tape test was used to visually

inspect the rust texture of the outermost surface of the UWS specimens. The morphology

of the rust was analyzed using a SEM. This field study found that when both types of UWS

were exposed to atmospheres with higher chloride concentrations (0.204 mg/100cm2/day),

the grain size of the rust increased with the corrosivity of the atmosphere. However, the

color of the rust was found to be similar between the different environments.

2.3.2 Industrial Environment Field Studies

Field studies of UWS were conducted in industrial environments, which are

typically classified as having relatively high sulfur oxide concentrations as opposed to

coastal, and rural environments. Therefore, the sulfur oxide concentrations of the

environments were assessed in each study to draw correlations between this atmospheric

condition and corrosion characteristics of UWS. Some of the studies focused on the

different rust phases that formed on the steel, while others focused on the effects of

23

corrosion rates based on sulfur oxide concentrations and varying chemical compositions of

the steel.

2.3.2.1 Relationship Between Atmospheric Corrosion Rates and Sulfur Oxide

Concentrations

Díaz et al. (2018) performed a field study that assessed the corrosion of UWS

specimens (ASTM A-242 and A-588) exposed at six different test sites, two of them being

industrial environments at Avilés, Spain and Kopisty, Czech Repblic, with higher sulfur

oxide contents (0.0464 and 0.1420 mg/100cm2/day, respectively) than the other four test

sites: one in Pardo, Spain (0.0028 mg/100cm/day), one in Madrid, Spain (0.0080

mg/100cm2/day), and two in Cabo Vilano, Spain (0.0066 mg/100cm2/day). Díaz et al.

(2018) reported the first-year corrosion rates of UWS specimens was about 25 μm/y at the

industrial environment test sites of Avilés, Spain and Kopisty, Czech Republic. This was

higher than the rural (Pardo, Spain) and urban (Madrid, Spain) test sites (about 10 μm/y),

but less than the marine test sites of Cabo Vilano-1 and Cabo Vilano-2 (about 30 μm/y and

50 μm/y, respectively).

Saha (2013) also conducted a field study that assessed UWS specimens exposed in

industrial environments of Chennai, India (0.42 mg NaCl/100cm2/day and 16.5 mg sulfur

oxide/100cm2/day) and Jamshedpur, India (trace amounts of NaCl and 22 mg sulfur

oxide/100cm2/day). Similar to Diaz et al. (2018), Saha (2013) found that after 42 months

of exposure, UWS specimens exposed in Chennai and Jamshedpur had lower corrosion

rates (12.1 μm/y and 9.5 μm/y, respectively) as compared to the specimens exposed in the

coastal environment of Digha, India (18.2 μm/y) with 0.83 mg NaCl/100cm2/day and trace

amounts of sulfur oxide. Furthermore, the corrosion rate was found to have stabilized after

42 months of exposure in the industrial environments (steady corrosion rate of <12 μm/y

achieved at sulfur oxide of 0.02-0.05 mg/100cm2/day), whereas the corrosion rate

24

continued to increase with UWS specimens exposed in the coastal environment. Saha

(2013) concluded that the lower corrosion rates observed with WS specimens exposed in

the industrial environments was due to the higher atmospheric sulfur oxide concentrations

present; however, it is noted that this may be due to coupled differences in sulfur oxide and

chloride concentrations.

Wang et al. (2013) assessed UWS specimens (W400QN, W450QN, SPA-H,

09CuPTiRE and WGJ510C) exposed at three different test sites in Japan. The sulfur oxide

depositions were 4.49 mg/100cm2/day in Qingdao, 6.96 mg/100cm2/day in Jiangjin, and

0.98 mg/100cm2/day in Qionghai. Wang et al. (2013) found, similarly to Diaz et al. (2018)

and Saha (2013), that steel specimens exposed in industrial environments with higher sulfur

oxide concentrations had higher corrosion rates in the initial corrosion stages. UWS

specimens exposed in Jiangjin, which had the highest sulfur oxide concentration also had

the highest corrosion rates in the initial corrosion stages (between 0.07 and 0.05 mm/y),

whereas UWS specimens exposed in Qingdao and Qionghai had initial corrosion rates of

about 0.05-0.06 and 0.03 mm/y, respectively. However, Wang et al. (2013) notes that the

effects of sulfur oxide decreased as the protective rust layer forms and exposure time

prolongs (i.e., the rate of corrosion decreased). This can be seen where the corrosion rates

of UWS specimens after 5 years exposure in both Jiangjin and Qingdao were between 0.02

and 0.03 mm/y, while in Qionghai they were between 0.01 and 0.015 mm/y. Even though

Jiangjin had higher sulfur oxide concentrations than Qingdao as well as higher initial

corrosion rates after the first year of exposure, both corrosion rates after 5 years of exposure

began to reach a steady state of around 0.02 mm/y.

25

2.3.2.2 Rust Phases Formed in Industrial Environments

Díaz et al. (2018) and Wang et al. (2013) both assessed the rust phases formed on

UWS specimens after 5 years exposure in multiple environments, including industrial

environments. In each of the field studies it was found that the rust consisted of two layers

with the inner layer being responsible for protection against atmospheric corrosion. It was

also found that the rust layers were mainly composed of ferric oxyhydroxides, an

amorphous substance, and spinel type iron oxides in some cases. The main ferric

oxyhydroxide structures that formed in the industrial environments were goethite and

lepidocrocite. Each of the field studies focused on how atmospheric conditions affected the

composition of the rust layer and in turn affected the rate of corrosion.

Díaz et al. (2018) found that the proportions of goethite in the industrial

environments was 33-36 wt-%, which was higher than those found in the other

environments with lower sulfur oxide concentrations (25-28 wt-%). It was then concluded

that atmospheric sulfur oxide facilitates the dissolution of lepidocrocite and accelerates the

transformation of lepidocrocite to goethite (Díaz et al., 2018). Wang et al. (2013) concluded

that the pollution of sulfur oxide strongly influences the corrosion behavior in the initial

stages. When the transformation of lepidocrocite to goethite is accelerated due to high

sulfur oxide deposition the corrosion rate in the initial corrosion stages is also accelerated.

However, Wang et al. (2013) notes that as exposure time increases, the increase in the

relative abundance of goethite enhances the corrosion resistance of UWS because of

goethite’s stability, compactness, and density. Wang et al. (2013) found that the relative

abundance of goethite on UWS specimens exposed in Jiangjin was higher than the other

two sites. This coincided with a noticeable decrease in the corrosion rates of UWS

specimens after 5 years of exposure at this test site.

26

2.3.2.3 Goethite/Lepidocrocite Relation to Protective Ability Index in Industrial

Environments

Yamashita et al. (1994) and Kamimura et al. (2005) assessed the performance of

UWS in industrial environments by evaluating the mass ratios of goethite to lepidocrocite

rust phases. Yamashita et al. (1994) stated that the inner layer of rust that formed on UWS

test specimens that were exposed for 26 years had a homogenous composition with small

goethite particles (<10 nm) as the main constituent. The small goethite particles in the inner

rust layer are able to pack densely together and provide corrosion resistance while the outer

layer of rust that formed was mainly composed of lepidocrocite. Furthermore, Kamimura

et al. (2005) also found that UWS exposed in industrial environments was mainly

composed of ultra-fine goethite and that the dissolution of lepidocrocite, precipitation, and

phase transformation to goethite with amorphous oxyhydroxide forms the protective inner

layer of ultra-fine goethite after decades of exposure.

Both Yamashita et al. (1994) and Kamimura et al. (2005) found that the

alpha/gamma mass ratio can be used as an index for evaluating the protectiveness of the

rust layer that forms on the steel. The goethite/lepidocrocite mass ratio was found to

increase with an increase in the duration of exposure in industrial environments and when

the goethite/lepidocrocite mass ratio exceeds a certain value, a lower corrosion rate of less

than 0.01 mm/year was observed (Kamimura et al., 2005). However, there is less

correlation between the alpha/gamma mass ratio and the rate of corrosion when the air-

borne sea salt content is greater than 0.2 mg NaCl/100cm2/day due to the formation of

akaganeite and iron oxide. Instead, Kamimura et al. (2005) found that the

goethite/lepidocrocite* mass ratio, which is the mass ratio of crystalline goethite to the total

mass of lepidocrocite, akaganeite and iron oxide, can be used as a protective ability index

in environments with relatively high air-borne sea salt concentrations. When the

27

goethite/lepidocrocite* mass ratio is greater than a certain threshold, the corrosion rate of

more than 0.01 mm/year is not observed.

2.3.2.4 Effects on Corrosion Rates Based on Chemical Composition of UWS in

Industrial Environments

Oh et al. (1999) and Wang et al. (2013) performed field studies that assessed the

effects of corrosion rates in industrial environments based on the different chemical

compositions of UWS test specimens. These field studies also compared UWS and mild

steel specimens to further evaluate how differences in chemical compositions of steel effect

atmospheric corrosion. Each field study found that the main oxides present in each

corrosion product were magnetic goethite, superparamagnetic goethite, and lepidocrocite,

independent of the different environments and type of steel.

Oh et al. (1999) performed a field study in which carbon steel, copper bearing steel

and UWS coupons with varying chemical compositions were exposed for 16 years in a

moderate industrial environment with a sulfur oxide deposition of 0.25 mg/100cm2/day.

Oh et al. (1999) found that when comparing two UWS coupons with a variation in one of

the chemical contents, such as two coupons with different amounts of nickel, two coupons

with different amounts of silicon, and two coupons with different amounts of phosphorous,

the relative fractions of goethite to all other corrosion products and lepidocrocite to all

other corrosion products that formed on the steel coupons were the same when being

exposed in an industrial environment. Differences were found when assessing the relative

fractions of superparamagnetic goethite (goethite particles less than or equal to 15 nm in

diameter) to goethite with particles greater than 15 nm, lepidocrocite and iron oxides

between coupons with varying chemical compositions. Oh et al. (1999) indicates that

increased fractions of superparamagnetic goethite are correlated with decreased mean

particle size of goethite and result in enhanced corrosion resistance. It was observed that

28

the relative fraction of superparamagnetic component of goethite for a coupon with a higher

amount of silicon was higher than that of a coupon with a lower amount of silicon,

independent of the environmental conditions. This suggests that a higher silicon content

can increase the fraction of goethite, resulting in a decrease of the mean particle size of

goethite and possibly resulting in enhanced corrosion resistance. Furthermore, it was found

that the relative fraction of superparamagnetic goethite in the industrial environment

decreased by 6% in the corrosion products of a coupon with a higher phosphorous content

than another coupon. This suggest that a lower phosphorous content can cause an increase

in the relative fraction of superparamagnetic goethite and result in better corrosion

resistance in an industrial environment.

Wang et al. (2013) conducted a similar study to Oh et al.’s (1999) in which the

effect of the chemical composition of UWS on the corrosion rate was assessed in an

industrial environment. Steel specimens were exposed for 5 years at three different sites in

Japan. Jiangjin, Japan was the test site that was representative of an industrial environment

with a sulfur oxide deposition of 6.96 mg/100cm2/day. Wang et al. (2013) notes that

compared with other alloying elements of UWS, chromium is the most abundant in the

inner rust layer and is the most effective at reducing the rate of corrosion. Wang et al.

(2013) mentions how this finding is similar to Yamashita et al.’s (1994), which reported

that chromium could partly replace iron in the iron oxyhydroxide. When exposed to high

concentrations of sulfur oxide this rust phase transformation is accelerated, which can

account for the higher amounts of chromium found in the rust layer of steel specimens

exposed in Jiangjin as well as reduced corrosion rates as compared to the other test sites.

Furthermore, Wang et al. (2013) found that UWS specimens with a lower amount of

phosphorous and copper had a higher corrosion rate than that of the other UWS specimens

at Jiangjin. This result is the opposite of what Oh et al. (1999) found when assessing UWS

29

specimens with lower amounts of phosphorous that resulted in better corrosion resistance

when exposed in industrial environments.

2.3.3 Rural Environment Field Studies

One of the other environments that field studies of UWS were performed in were

rural environments. Rural environments are typically classified as having relatively lower

chloride and sulfur oxide concentrations as compared to coastal and industrial

environments. The effects of low chloride and sulfur oxide concentrations on corrosion

characteristics of UWS and other types of steel were assessed in each study.

2.3.3.1 Rust Characteristics in Rural Environments

Wang et al. (2013) and Diaz et al. (2018) conducted similar field studies in which

the corrosion of UWS was assessed after 5 years exposure in multiple environments. Wang

et al. (2013) looked at the effects of UWS exposed in Qionghai, Japan, which was classified

as a rural environment with a sulfur oxide deposition of 0.98 mg/100cm2/day and chloride

deposition of 1.99 mg/100cm2/day in comparison to a coastal environment (Qingdao,

Japan) and industrial environment (Jiangjin, Japan). Thickness losses of about 0.8 mm for

the carbon steel specimen and about 0.05 mm for the UWS specimens were found from

exposure after 5 years in the rural environment. These values are both lower than the

corrosion losses reported for both types of steel specimens exposed in the coastal and

industrial environments. Similarly, Diaz et al. (2018) found that steel specimens exposed

in the rural environment of Pardo, Spain with a sulfur oxide deposition of 0.0028

mg/100cm2/day and chloride deposition of 0.027 mg/m2/day had the lowest corrosion rates.

Therefore, it was concluded that UWS exposed in environments with relatively lower

sulfur oxide and chloride depositions would experience lower corrosion losses.

30

Additionally, Wang et al. (2013) found differences in the initial stages of corrosion

rates between specimens exposed in coastal (Qingdao, Japan) and rural (Qionghai, Japan)

environments, however, the long-term corrosion trend was similar. Qionghai, Japan had

both the highest average temperature of 24.3°C and relative humidity of 86% compared to

the other two environments. These two factors contribute to a greater time of wetting

(TOW), which occurs when the temperature and humidity are beyond 0°C and 80%,

respectively (Wang et al., 2013).

2.3.3.2 Rust Phases Formed in Rural Environments

Similar to UWS specimens exposed in an industrial environment, when evaluating

the rust phases that formed on specimens exposed in a rural environment, Oh et al. (1999),

Cook et al. (1999), Diaz et al. (2018), and Kamimura et al. (2005) each found goethite and

lepidocrocite to be the main oxides in each corrosion product.

Oh et al. (1999) studied the corrosion products formed on different UWS (ASTM

A-242 and A-588), carbon steel, and copper bearing steel specimens exposed for 16 years

in industrial, marine, and rural environments. It was found that the fractions of goethite,

maghemite, and lepidocrocite were almost the same in the corrosion products of each steel

specimen, independent of environmental exposure conditions. Cook et al. (1999) also

assessed UWS specimens exposed for 16 years in the rural environment of Saylorsburg,

Pennsylvania and found that the outer rust layer was composed of equal fractions of

lepidocrocite and goethite. Similarly, Diaz et al. (2018) noted that the outer surface of the

rust layer formed on UWS specimens exposed in the rural environment of Pardo, Spain

(sulfur oxide concentration of 0.0028 mg/100cm2/day and chloride deposition of 0.027

mg/100cm2/day) were an “orangey colouring”, which is typical of the lepidocrocite phase.

Cook et al. (1999) states that the inner rust layer of UWS specimens exposed in the rural

31

environment of Saylorsburg, Pennsylvania were entirely made up of goethite (distribution

of particle size 5-30 nm with 60% of particles less than 15 nm) containing small amounts

of maghemite and magnetite (both types of iron oxides). This is comparative with

Kamimura et al. (2005)’s field study where UWS specimens exposed in the rural

environment of Kitahiroshima-city, Japan (amount of air-borne salt particles: 0.01

mg/100cm2/day) were mainly composed of ultra-fine goethite. The ultra-fine particle size

of the goethite rust phase is the main characteristic that contributes to corrosion resistance

of UWS. Thus, it was concluded that ultra-fine goethite forms with exposure conditions in

rural environments with relatively low chloride and sulfur oxide concentrations as well as

a low TOW.

2.3.3.3 Goethite/Lepidocrocite Relation to Protective Ability Index in Rural

Environments

Kamimura et al. (2005) also found that UWS specimens (JIS SMA 490AW)

exposed in the rural environment of Kitahiroshima-city, Japan (amount of air-borne salt

particles: 0.01 mg/100cm2/day) had a relationship between the goethite/lepidocrocite* and

rate of corrosion. Hara et al. (2006)’s field study furthered Kamimura et al. (2005)’s

findings by reporting information about 5 UWS bridges exposed between 1 and 18 years

in the rural environments of Hokkaido, Yamagata, and Fukushima, Japan (<0.05 airborne

sea salt mg/100cm2/day). The goethite/lepidocrocite* was found to be less than 1 and

(akageneite + iron oxide)/lepidocrocite* was less than 0.5. Both field studies reported that

the corrosion rates of the UWS exposed in these rural environments was found to be less

than 0.01 mm/y. Wang et al. (2013) performed a field study in the rural environment of

Qionghai, Japan (sulfur oxide deposition 0.98 mg/100cm2/day & chloride deposition 1.99

mg/100cm2/day) where UWS specimens were exposed for five years. Wang et al. (2013)

found that the rust layer was mainly composed of goethite and lepidocrocite, however the

32

reported corrosion rates were around 0.03 mm/y, which is greater than those reported by

Kamimura et al. (2005) and Hara et al. (2006). Wang et al. (2013) only reported the initial

stages of corrosion (less than 5 years of exposure); however, the corrosion rates of UWS

specimens were beginning to drop below 0.02-0.01 mm/y and stabilize after the initial five

years of exposure.

2.3.4 UWS Bridge Washing Field Studies

Crampton et al. (2013) reported on Iowa DOT’s evaluation of their UWS bridge

inventory. The quality of UWS patinas in environments with deicing salt usage were

assessed to evaluate the potential benefits of routine bridge washing. Clear tape adhesion

and chloride testing as well as patina evaluations were performed at selected areas before

and after the washed areas were permitted to fully dry to assess the immediate effects of

power washing. Overall, Crampton et al. (2013) found that the most effective, efficient,

and practical patina evaluation techniques were the visual inspections and tape adhesion

tests. Bridges that were inspected and washed ranged between being built in 1970-2007.

Crampton et al. (2013) found that bridge washing performed immediately following the

winter deicing season removed many of the chloride contaminants on the surface of the

patinas, but not with chlorides embedded within the rust layer. Crampton et al. (2013) also

notes that the maximum benefits of routine bridge washing are likely to be achieved on

newer UWS bridges where chloride has had less time to build-up and penetrate the

protective oxide layer; however, further study may be needed to confirm these

assumptions. In addition to removing chloride build-up, washing can also help remove

loose rust flakes, dirt, debris, and other contaminants that often retain moisture and prolong

TOW that may result in damaging the patina (Crampton et al., 2013). A proposed

33

inspection rating scale was developed from the study along with maintenance actions based

on each rating.

Ault and Dolph (2018) also notes the benefits of bridge washing (not specific to

UWS) in terms of corrosion prevention. Ault and Dolph (2018) reported a study done by

Alland et al. (2013) at the University of Pittsburgh regarding investigation of the

effectiveness of washing practices on Pennsylvania bridges subject to deicing agents.

Alland et al. (2013) tested the steel surfaces of bridges before and after washing with the

ARP Instruments soluble salt meter (SSM). The reported data showed a salt reduction

between 20% and 95% depending on the location being washed and assessed; however, no

specific information is given on which locations were most or least affected. Ault and

Dolph (2018) reported another study done by Palle et al. (2003) of the Kentucky

Transportation Center (KTC) that evaluated bridge washing. Palle et al. (2003) found

nearly complete removal of chloride, even when there were already low quantities of

chloride present after assessing chloride concentrations before and after washing steel

surfaces of bridges.

2.4 Phase 1 and Phase 2 Work

The main goal of this research project is to provide quantitative guidelines to define

environments that cause undesirable rates of corrosion in UWS, which were previously

described qualitatively in FHWA TA 5140.22. In Phase 1 of this research project, data was

gathered from a survey of UWS bridges across 56 owner agencies (46 states, the District

of Columbia, Puerto Rico, and eight federal agencies) in the U.S. A UWS Geographic

Information System (GIS) database with over 10,000 UWS bridges was put together to

develop a complete macroclimate of all known UWS bridges in the U.S. to make

correlations with corresponding National Bridge Inventory (NBI) data. As a result of a

34

complementary survey of bridge owners, two environments of greatest concern for UWS

bridges were determined: coastal environments and highway overpasses over roadways

treated with deicing agents. (McConnell et al., 2014 c)

Phase 2 of this project then focused on evaluating the field performance of UWS

bridges in these two environments. Thirteen UWS bridges were evaluated in the field as

well as a review of their inspection reports along with 56 other UWS bridges that are

collectively referred to as cluster bridges. In the field, tape adhesion tests for digital image

processing and rust samples for IC analysis were collected. Data collected from these field

samples, inspection reports, and photographic records were used to assess which of the

cluster bridges were experiencing overall corrosion and to determine quantitative

correlations that could be made with their surrounding environmental conditions

(McConnell et al., 2014 b).

Conclusions from this work included correlations between UWS performance and

environmental conditions. Characteristics that contributed to either good or poor

performance of UWS bridges in the coastal environment (Gulf Coast) and deicing

environments were summarized. The consistently poor coastal environment was found to

have a small distance to the coast, high humidity, and high atmospheric chemical

concentrations, while crossing a waterway. The consistently good coastal environments in

comparison to the poor coastal environment were found to have a combination of the

following parameters:

• A greater distance to the coast, equal or lower humidity scores, lower

atmospheric chemical concentrations, while crossing a highway.

• A greater distance to the coast, lower humidity scores, equal or lower

atmospheric chemical concentrations, while crossing a highway.

• A greater distance to the coast, even lower humidity scores than the

environmental combination referenced in the previous bullet point,

35

equal or lower atmospheric chemical concentrations, while having any

crossing type.

Specific quantities for these parameters can be found from McConnell et al. (2014

b). These quantitative conclusions on correlations between coastal environment conditions

and UWS performance were based on a review of data from nearly 50% of the UWS

inventory within 50 miles of the Gulf Coast. Because of this, the careful sampling of

bridges, and the relatively small number of influential parameters affecting UWS

performance in this region, there was a modest level of confidence in the general

applicability of these observed correlations in the absence of a larger data set.

The consistently poor deicing environments were described as highway crossings

with vertical under-clearances less than or equal to 5 meters along with one of the following

additional combinations of influential parameters:

• High ADT.

• Moderate ADT combined with high snowfall.

• Moderately high snowfall combined with moderately high chloride

levels.

The consistently good deicing environments in comparison to the poor deicing

environments consisted of:

• Highway crossings with high vertical under-clearance.

• Highway crossings with low ADT and low chloride concentration

without very high snowfall.

• Highway crossings with low chloride concentration without a more

modest level of high snowfall.

• Highway crossings with low snowfall.

• Waterway crossings that cross no other features.

36

Specific quantities for these parameters can be found from McConnell et al. (2014

b). These quantitative conclusions on correlations between deicing environment conditions

and UWS performance were based only on a small fraction (likely less than 1 percent) of

the total UWS inventory subjected to deicing agents. Therefore, Phase 3 of this research

project is being conducted to further assess these preliminary correlations.

37

Chapter 3

FIELD METHODOLOGY

This chapter describes methodology that was used to select UWS bridges for

analysis and evaluation, bridge evaluation practices carried out prior to field visits and once

in the field, and data collection processes of clear tape adhesion tests as well as IC analyses.

3.1 Bridge Selection

One main aspect of this research project was to maximize opportunities for collecting

quantifiable data on UWS bridge performance. One way of doing this was by compiling

data and identifying UWS bridges for field evaluations. The goal of this section is to

explain how UWS bridges were selected for field evaluation. The proceeding data

collection and analysis plan is based on procedures used in prior work (Phase 2 Report

(McConnell et. al, 2014 b) with refinements as documented in the draft field and laboratory

protocols of Phase 3 (McConnell and Shenton, 2018). The location of seven proposed

“clusters” were chosen based on environments where UWS bridge performance was found

to be unsatisfactory in Phase 1 of this research project (McConnell et. al, 2012). These

environments include regions where atmospheric chloride concentrations are relatively

high, such as in areas near the coast and where large quantities of deicing agents are used

on roadways beneath bridges. These clusters were also defined as being “good” or

“inferior” performing based on overall UWS bridge performance within those regions Ohio

(good deicing), Colorado (good deicing), Minnesota (inferior deicing), Iowa (inferior

deicing), North Carolina (inferior and good coastal), New Hampshire (good coastal and

deicing), and Connecticut (inferior coastal and deicing) were the seven clusters determined

38

to be representative of these environments and part of Phase 3 of this research project. It is

noted that the definition of “good” used in this work is not merely good performance, but

good performance in a severe environment. The rationale for selecting these clusters is

discussed in the Task 3 Report for this research project (McConnell et. al, 2018). Refer to

Table 3.1 for an overview of the Phase 3 clusters and Figure 3.1 for their locations in the

United States.

Table 3.1 Phase 3 Cluster Overview

Deicing Coastal

Deicing

+

Coastal

Inferior 1. MN

5. NC

6. CT 2. IA

Good 3. CO

7. NH 4. OH

39

Figure 3.1 Phase 3 Cluster Locations

3.1.1 GIS Database

A database of UWS highway bridges was compiled after working with 46 LTBPP

state coordinators (representatives from each state in the U.S., Washington D.C., and

Puerto Rico that assist in work related to the LTBPP) and representatives from 8 Federal

agencies to identify UWS bridges within their agencies (McConnell et al., 2014 a). The

National Bridge Inventory (NBI) was then used to collect superstructure condition ratings

(SCR), latitudinal and longitudinal coordinates, average daily traffic (ADT) under

structures, crossing types (highway, railroad, waterway, etc.), year built as well as year

reconstructed, and vertical under-clearances into a database. Climate data (time of wetness,

monthly humidity, and annual snowfall); atmospheric chemical concentrations of chloride,

nitrate, and sulfate; and distance to the coast were also added to the database. All of this

data was compiled to make up the UWS geographic information system (GIS) database,

which consisted of over 10,000 bridges. Overall, this database supplied information

40

regarding the complete macroclimate of UWS bridges in the United States in association

with corresponding NBI data. McConnell et al. (2014 a) describes that this database can be

used in future research to assess specific correlations between environmental parameters

and UWS bridge conditions.

3.1.2 Reference Bridges

After developing the UWS GIS database, bridges were systematically selected for

further analysis. Parametric evaluations based on data from the UWS database were used

to determine good performing reference bridges. Bridges in the most severe environments

were compared with their SCR to determine those in the best condition despite being in a

relatively severe environment. The inferior performing reference bridges were identified

by state-coordinators during Phase 1 (McConnell et. al, 2012). This same concept was

applied to determine “inferior” performing reference bridges, when state-coordinators

could not supply recommendations for specific suitable reference bridge candidates. In

cases where state-coordinators could supply recommendations of inferior performing

reference bridges, inspection reports of these bridges were reviewed and compared based

on SCR as well as condition state ratings of the girders in order to select the most viable

candidate as a reference bridge.

When the reference bridge did not originate from the direct suggestion of the state-

coordinator, candidates were identified based on the representation of specific geography-

related variables, such as bridges that were within a specific distance to the coast or where

snowfall exceeded a particular threshold. The next step was then to consider the

performance of bridges within these constraints to determine a suitable reference bridge.

Furthermore, a suitable reference bridge candidate also required a minimum of 10 bridges

to be within a specified radius and older than a specified age so that these surrounding

41

bridges could be considered as proximate bridges (which are described in Section 3.1.3).

Refer to Table 3.2 for characteristics of each reference bridge that was selected for

evaluation.

Table 3.2 Characteristics of Phase 3 Reference Bridges

3.1.3 Proximate Bridges

Proximate bridges were identified by using the longitudinal and latitudinal

coordinates of the reference bridge as the center of a circle with a specified radius (typically

50 miles) in GIS. GIS was used to identify all UWS bridges within the circle and then the

list of identified bridges was filtered to remove bridges that were built or reconstructed

prior to 1964 and younger than 20 years old. UWS bridges that were reported to be built

or reconstructed prior to 1964 were considered to be an error in the database based on the

fact that UWS was first used in bridges in 1964. Furthermore, UWS bridges that were

younger than 20 years old were generally deemed to be too young for a meaningful

evaluation of their long-term performance. The specified radius of the MN cluster was

increased to 200 miles in order to include the Minneapolis urban area and environments

with relatively higher snowfall. The IA cluster did not contain a minimum of 10 bridges

ClusterStructure

Number

Crossing

Type

Distance

to

Coast

(miles)

ADT

Under

Structure

Vert. Under-

Clearance

(ft.)

Relative

Humidity

Snow

(in.)

Chloride

(mg/L)

Age

(years)SCR

OH 7701993 Highway NA 26000 4.67 0F, 10G, 2H 49.2 0.101 40 8

MN 62861 Highway NA 130000 4.91 4F, 8G, 0H, 0I 52.9 0.057 40 6

IA 041331 Highway NA 88500 5.23 3F, 9G 34.8 0.066 11 7

NH 11101120017900 Highway 2.1 65610 4.92 1F, 4G, 6H, 1I 59.2 0.75 14 8

NC (Inferior) 190083 Rail 4.2 NA NA 1F, 8G, 3H 2.3 0.328 33 5

NC (Good) 1290058 Highway 2.1 23000 5.00 0F, 9G, 3H 2.3 0.717 28 8

NC (Good) 1290057 Highway 2.4 29000 4.93 0F, 9G, 3H 2.3 0.717 28 8

CO E-16-JZ Highway NA 183000 6.00 9E, 3F 62.6 0.045 28 7

CT 3830 Highway 2.2 19400 4.95 5F, 7G, 0H 31.9 0.315 37 6

42

that were within a 50-mile radius of the chosen reference bridge as well as not built or

reconstructed prior to 1964 and older than 20 years old, so younger bridges were included

in the cluster instead of increasing the radius in order to avoid the IA cluster overlapping

with the MN cluster.

3.1.4 Cluster Bridges

Cluster bridges were next selected as a subset of the proximate bridges. Refer to

Table 3.3 for a summary of each cluster’s bridge statistics. Cluster bridges were determined

based on statistical evaluation of key influential parameters from NBI data (SCR, ADT

under structure, crossing type, age, and vertical under-clearance), atmospheric chemical

concentrations (chloride, nitrate, and sulfate), climate data (time of wetness, monthly

humidity, and annual snowfall), and distance to coast. Refer to Appendix A for breakdowns

of the data parameters mentioned above for each cluster bridge within each cluster.

Statistical analysis of this data for the proximate bridge population was then used to

determine the mean, standard deviation, maximum, minimum, and median values of each

parameter. The value of each parameter of each bridge was then categorized as “high”

(greater than the median value of that parameter) or “low” (less than or equal to the median

value of that parameter).

43

Table 3.3 Summary Bridge Statistics of Phase 3 Clusters

The cluster bridges were selected based on parametric combinations of parameters

of interest. For deicing clusters, ADT under structure, vertical under-clearance, relative

humidity, annual snowfall, atmospheric chloride concentration, age, and SCR were key

parameters of consideration. For the combined deicing and coastal clusters, distance to

coast along with the parameters mentioned for deicing clusters were considered. For the

coastal cluster, distance to coast, relative humidity, and chloride concentrations were

considered. The parametric combinations provided each UWS bridge in the list of

proximate bridges with a numbered category. Next, the numbered category associated with

the reference bridge was used to determine other numbered categories of interest by

varying one parameter of interest at a time. For instance, if the reference bridge had a

“high” distance to coast, “high” ADT, and “high” atmospheric chloride concentration, the

Cluster

Distance

to Coast

(miles)

ADT

Under

Structure

Vertical

Uncer-

Clearance

(ft.)

Relative

Humidity

Snow

(in.)

Chloride

(mg/L)

Age

(years)SCR

OH (MAX) NA 99400 329.16 0F, 10G, 2H 95.3 0.101 46 9

OH (MIN) NA 295 53.04 2F, 10G, 0H 23.8 0.070 2 4

MN (MAX) NA 303000 145.92 1F, 5G, 4H, 2I 88.1 0.105 48 9

MN (MIN) NA 35 57.48 5F, 7G, 0H, 0I 25.7 0.030 2 0

IA (MAX) NA 108300 113.40 3F, 9G 34.8 0.066 41 9

IA (MIN) NA 2030 49.44 3F, 9G 21.7 0.066 2 7

NH (MAX) 67.5 246700 219.48 5F, 9G, 6H, 2I 80.8 0.750 59 9

NH (MIN) 0.0 100 37.20 0F, 4G, 0H, 0I 41.6 0.102 2 4

NC (MAX) 32.2 29000 64.32 3F, 9G, 3H 3.0 0.717 33 8

NC (MIN) 2.1 1800 57.60 0F, 8G, 0H 2.3 0.328 28 5

CO (MAX) NA 183000 96.00 9E, 3F 62.6 0.045 33 8

CO (MIN) NA 62500 60.00 9E, 3F 62.6 0.045 28 3

CT (MAX) 30.4 161900 167.88 6F, 8G, 2H 100.1 0.315 78 9

CT (MIN) 0.0 900 50.88 3F, 6G, 0H 23.6 0.127 2 3

44

atmospheric chloride concentration parameter would first be varied to “low” and the

corresponding parametric combination number of “high” distance to coast, “high” ADT,

and “low” atmospheric chloride concentration would be considered to select UWS bridges

matching or close to that numbered category in the list of proximate bridges. Next, ADT

would be varied to “low”, so the parametric combination would be “high” distance to coast,

“low” ADT, and “high” atmospheric chloride concentration, and so on. Refer to Appendix

B, Table B.1 for the list of parametric combinations used for the deicing clusters. Refer to

Appendix B, Table B.2 for the list of parametric combinations used for the deicing and

coastal clusters. Refer to Appendix B, Table B.3 for the list of parametric combinations

used for the coastal cluster.

3.1.5 Field Bridges

Field bridges were next selected after receiving inspection reports on all selected

cluster bridges. These inspection reports were evaluated with close attention to condition

state information of core elements, key words, and photographs relating to UWS issues or

corrosion. Two bridges from the list of cluster bridges were then selected as field bridges,

one generally representing a bridge in the “best” condition and the other representing a

bridge in the “worst” condition within the cluster. Best and worst condition states were

based largely on percentage of girders and other UWS elements in various condition states

considered relative to age and severity of the assigned parametric category. Refer to Table

3.4 for a summary of characteristics for each field bridge.

45

Table 3.4 Characteristics of Phase 3 Field Bridges

It should be noted that the NH cluster originally included four field bridges that

were going to be evaluated; however, two of the bridges (NH 019700810009300 and NH

017700960015300) were found to be painted so they could not be assessed. Refer to Table

3.5 for a summary of bridges NH 019700810009300 and NH 017700960015300

characteristics. Instead, bridge NH 011101120017900 was included as a field bridge and

evaluated after determining it to be a viable replacement of bridge NH 019700810009300

based on a review of the bridge’s characteristics being similar to NH 019700810009300 as

well as it being one of the only bridges that was not painted and within a reasonable travel

distance. A replacement bridge could not be found in time for NH 017700960015300 due

to travel plans.

Cluster CombinationStructure

Number

Crossing

Type

Distance

to Coast

(miles)

ADT

Under

Structure

Vertical

Under-

Clearance

(ft.)

Relative

Humidity

Snow

(in.)

Chloride

(mg/L)

Age

(years)SCR

MN 2 04019 Highway NA 9700 58.92 2F, 9G, 1H, 0I 53.1 0.030 34 5



NC 3 190083 Rail 4.2 NA NA 1F, 8G, 3H 2.3 0.328 33 5

NC 32,W 1290058 Highway 2.1 23000 60.00 0F, 9G, 3H 2.3 0.717 28 8

NC 38 1290057 Highway 2.4 29000 59.16 0F, 9G, 3H 2.3 0.717 28 8

IA 8 041331 Highway NA 88500 62.76 3F, 9G 34.8 0.066 11 7

IA 16 042711 Highway NA 78360 87.60 3F, 9G 34.8 0.066 10 8

IA 16 004111 Highway NA 73990 66.48 3F, 9G 34.8 0.066 12 8

CO 12 E-16-JZ Highway NA 18300 72.00 9E, 3F 62.6 0.045 28 7

CO 20 E-16-JW Highway NA 62500 60.00 9E, 3F 62.6 0.045 31 8

CO 20 E-16-JX Highway NA 84000 60.00 9E, 3F 62.6 0.045 31 8

OH 1 7701977 Highway NA 23599 57.00 0F, 10G, 2H 49.2 0.101 39 8

OH 1 7701993 Highway NA 26000 56.04 0F, 10G, 2H 49.2 0.101 40 8

OH 24 7805934 Highway NA 7832 55.44 2F, 10G, 0H 40.1 0.100 21 5

CT 4 4382 Highway 2.9 48400 60.60 5F, 7G, 0H 31.9 0.315 32 6

CT 5 5796 Highway 0.6 11600 57.36 5F, 7G, 0H 47.4 0.315 26 7

CT 12 3830 Highway 2.2 19400 59.40 5F, 7G, 0H 31.9 0.315 37 6

NH 3 011101120017900 Highway 2.1 65610 59.04 1F, 4G, 6H, 1I 59.2 0.750 14 8

NH 54 017201120011300 Highway 28.0 3900 53.28 5F, 7G, 0H, 0I 68.2 0.180 36 6

NH 64 017701460003700 Highway 17.6 21000 63.72 5F, 7G, 0H, 0I 55.4 0.180 20 8

46

Table 3.5 Characteristics of Bridges NH 019700810009300 and NH 017700960015300

3.2 Field Work

Field work was conducted in accordance with LTBPP previsit (PRE) and field visit

(FLD) protocols published in FHWA-HRT-16-007 (Hooks and Weidner, 2016) as well as

refinements to these protocols documented in the draft field and laboratory protocols of

Phase 3 (McConnell and Shenton, 2018). Refer to Section 3.2.2 and Section 3.2.3 for the

specific “PRE” and “FLD” protocols that were followed throughout field work conducted

in Phase 3 of this research project.

3.2.1 Equipment

• PRE-PL-LO-004, Personal Health and Safety Plan.

• Ladder, access platform, snooper, bucket truck, man lift, and/or high-

reach equipment (if necessary).

• Pelican case (optional).

• Tool pouch (optional).

• Tool tray (optional).

• Pencil, sketch pad, and clipboard.

• Soap stone/white chalk.

• Permanent marker.

• Temporary marker.

• Field data entry sheets.

Cluster CombinationStructure

Number

Crossing

Type

Distance

to Coast

(miles)

ADT

Under

Structure

Vertical

Under-

Clearance

(ft.)

Relative

Humidity

Snow

(in.)

Chloride

(mg/L)

Age

(years)SCR

NH 3 019700810009300 Highway 3.2 51318 60.00 1F, 4G, 6H, 1I 59.2 0.750 38 7

NH 38 017700960015300 Highway 19.5 71000 59.64 5F, 7G, 0H, 0I 68.2 0.180 26 8

47

• Manila folder (optional).

• Spring clips (optional).

• Tape measure.

• 6-ft. folding ruler.

• Carpenters square.

• Laser measuring device (optional).

• Sample area marking template (4 in. by 6 in., optional).

• Digital camera.

• Magnetic bar with white, black, green, yellow, blue, and red electrical

tape.

• Magnetic lights (optional).

• Clear plastic packing tape with a minimum width of 1.89 in. (48 mm)

and minimum adhesive strength to steel of 55 oz./ in. width according

to ASTM D3330 (e.g., 3M Scotch® Superior Performance Box Sealing

Tape 375).

• Firm rubber “J” roller, 3 in. width minimum.

• Letter size white paper.

• Stainless-steel scoopula.

• Stainless-steel wire brush.

• Stainless-steel chisel, preferably with wide blade.

• Electrical power grinder and stainless-steel wire cup wheel.

• Gram digital scale.

• Clear plastic sealable bags.

• Ultrasonic measuring device and associated coupling agent (optional).

• Lever pit gage.

48

• Dry-film thickness gage.

3.2.2 Prior to Field Visit

Before carrying out field work at each cluster location, work was conducted to plan

out bridge inspections with the goal of minimizing impacts to the traveling public, ensuring

a safe work environment, and collecting high quality data. All pre-field work was

conducted in accordance with LTBPP “PRE-ED-BD-001”, “PRE-ED-BD-005”, “PRE-PL-

LO-001”, “PRE-PL-LO-003”, “PRE-PL-LO-004”, and “PRE-PL-LO-005” protocols

published in FHWA-HRT-16-007 (Hooks and Weidner, 2016) as well as refinements to

these protocols documented in the draft field and laboratory protocols of Phase 3

(McConnell and Shenton, 2018). Pre-field work steps included activities such as:

1. Requesting and reviewing general bridge plan and elevation drawings as

well as steel framing plans and inspection records from state LTBPP

coordinators.

2. Determining where samples would be collected based on draft field and

laboratory protocols of Phase 3 (McConnell and Shenton, 2018), as well as

alignment of the FLD-OP-SP protocols (Hooks and Weidner, 2016), steel

framing plans to assess bridge geometry, accessibility, and traffic control.

3. Coordinating with the bridge owner to identify suitable dates for conducting

the field work, planning for maintenance of traffic (MOT), and any other

logistics. MOT and access equipment were then arranged for the date of the

inspection.

3.2.3 Once in the Field

Evaluations of reference and field bridges were conducted in accordance with

LTBPP “FLD-OP-SP-001”, “FLD-OP-SC-001”, “FLD-OP-SC-002”, “FLD-OP-SC-003”,

“FLD-DC-PH-001”, “FLD-DC-PH-002”, “FLD-DC-PH-003”, “FLD-DC-VIS-002”, and

“FLD-DS-LS-001” protocols published in FHWA-HRT-16-007 (Hooks and Weidner,

2016) as well as refinements to these protocols documented in the draft field and laboratory

49

protocols of Phase 3 (McConnell and Shenton, 2018). A cursory inspection of the structure

to assess any unanticipated issues or problems that affected the ability to collect samples

as planned was performed. If there were unanticipated issues, the plan was modified on

site as needed. Access equipment was setup to gain access to the girders in the specified

locations. The locations on the girders where samples were to be taken were located using

a tape measure or laser measuring device.

3.2.3.1 Visual Documentation

A total of six different types of photos were taken at each site visit as per LTBPP

protocol FLD-DC-PH-002, Photographing for Documentation Purposes (Hooks and

Weidner, 2016) as well as refinements to these protocols documented in the draft field and

laboratory protocols of Phase 3 (McConnell and Shenton, 2018).These include:

4. A wide view of the bridge viewing fascia girders/beams that captures all

girder segments. This photograph was taken from a distance of

approximately 100 ft. back from the bridge, but within the limits of site

traffic control if possible, or on the shoulder of the road if necessary. An

example is shown in Figure 3.2.

5. Girders at all bearing locations. An example is shown in Figure 3.3.

6. A wide view of interior girders for each span. An example is shown in

Figure 3.4.

7. One close-up photo of each splice plate on fascia girders (if applicable). An

example is shown in Figure 3.5.

8. One close-up photo of a lateral bracing to girder connection (if applicable).

An example is shown in Figure 3.6. This photograph focused on bolted

connections, such as between cross-frame members and transverse

stiffeners serving as lateral bracing connection plates, in areas where any

pack rust was developing if applicable.

9. At least one photo depicting the general environmental exposure of the

structure was included if not captured in the wide view of the fascia girder.

An example is shown in Figure 3.7.

50

A hand sketch was provided with these pictures depicting the observer’s location and

viewing angle relative to the bridge.

Figure 3.2 Photo Example of Wide View of Bridge

Figure 3.3 Photo Example of View of Bearing Location

51

Figure 3.4 Photo Example of Wide View of Interior Girders

Figure 3.5 Photo Example of View of Girder Splice Plate

52

Figure 3.6 Photo Example of Lateral Bracing to Girder Connection

Figure 3.7 Photo Example of Overall View of General Environment of Bridge

3.2.3.2 Sample Areas

3.2.3.2.1 Locations

A total of 12 sample area locations was to be used for each bridge that was

inspected. These locations included two different longitudinal cross-sections. If the bridge

was a highway crossing, one longitudinal cross-section location was over the center of the

right travel lane. The second longitudinal cross-section location was typically over a

53

shoulder lane if applicable. If no shoulder lane was present, the second longitudinal cross-

section location was selected by moving about 12 ft. away from the longitudinal cross-

section location over the center of the right travel lane towards the nearest abutment. One

of the bridges that was inspected was a railroad crossing, in which case one longitudinal

cross-section location was about 9 ft. away from the nearest abutment and the second

longitudinal cross-section location was about 18 ft. away from the nearest abutment.

Within each bridge cross-section, one fascia girder and one interior girder was

sampled. The two girders were identified and denoted as per LTBPP protocol FLD-OP-

SC-002, Structure Segmentation and Element Identification System (Hooks and Weidner,

2016). If the bridge was a highway crossing, these girders were on the side of the bridge

facing oncoming traffic in the lanes over which the sampled cross-section was located. On

each of these 2 girders at each (of the 2) longitudinal cross-section locations, samples were

taken in three locations (for a total of 12 sample areas per bridge). One field bridge that

was evaluated from the MN cluster (MN 04019) had only 10 samples taken because two

of the sample locations on the exterior of the fascia girder were obstructed by a sign. The

three locations to be sampled on each girder cross-section were: the top surface of the

bottom flange on both sides of the web and the side of the web facing traffic (if applicable)

approximately one-third of the height of the web above the bottom flange. Specific

locations of sample areas on a typical I-girder cross-section are shown in Figure 3.8.

54

Figure 3.8 I-Girder Cross-Section Sample Locations

When collecting samples from bridges in the field, the 12 sample locations were

numbered based on the order in which they were inspected. Therefore, one field bridge

may have a different sample location numbering system than another. When recording

data, this numbering system was denoted as the Field Test Sample Area ID. In order to

standardize the sample location numbering system between each field bridge a Standard

Sample Area Location ID was also assigned to each sample location. These Standard

Location IDs along with their descriptions can be seen in Table 3.6. This standardized

numbering system allowed data collected at each sample area to be compared between each

field bridge.

55

Table 3.6 Standard Sample Area Location Descriptions

3.2.3.2.2 Measurements

At each sample location, a 4-inch by 6-inch rectangular area along with a

corresponding sample number (for reference) was marked using white chalk and a sample

area marking template cut from cardboard. The longer dimension of the rectangle was

oriented vertically for web locations and longitudinally for flange locations. Sample

locations on the bridge were numbered sequentially starting with 1 and ending with the

maximum number of samples taken from the bridge (typically 12, as previously discussed

in Section 3.2.3.2.1). The sample locations were recorded in field data entry sheets using

the center of the sample location (in x, y, z coordinates) per LTBPP protocol FLD-OP-SC-

002, Structure Segmentation and Element Identification System (Hooks and Weidner,

2016). The vertical distance of the sample area from the roadway or ground, and the

horizontal distance from the nearest joint, pier or abutment was measured (usually with a

Standard

Location

ID

Location Description

1 top of bottom flange of interior girder facing traffic over the shoulder

2 lower web of interior girder facing traffic over the shoulder

3 top of bottom flange of interior girder facing backside of traffic over the shoulder

4 top of bottom flange of exterior girder facing traffic over the shoulder

5 lower web of exterior girder facing traffic over the shoulder

6 top of bottom flange of exterior girder facing backside of traffic over the shoulder

7 top of bottom flange of interior girder facing traffic over the right travel lane

8 lower web of interior girder facing traffic over the right travel lane

9 top of bottom flange of interior girder facing backside of traffic over the right travel lane

10 top of bottom flange of exterior girder facing traffic over the right travel lane

11 lower web of exterior girder facing traffic over the right travel lane

12 top of bottom flange of exterior girder facing backside of traffic over the right travel lane

56

laser rangefinder) and recorded in field data entry sheets. Refer to Appendix C for field

data entry sheets of each field bridge that was evaluated in Phase 3.

3.2.3.2.3 Photographs

Two photographs were taken of each sample area per draft field and laboratory

protocols of Phase 3 (McConnell and Shenton, 2018) while following FLD-DC-PH-002,

Photographing for Documentation Purposes (Hooks and Weidner, 2016). One showed the

complete sampled area and one showed a closer perspective where the entire 4-inch by 6-

inch sample area filled the entire field of view. A magnetic steel bar with white, black,

green, yellow, blue, and red electrical tape was placed on the girder at the bottom of the

sample area as a color reference. These photographs may be used to assess rust

colorization characteristics with spectroscopy methods in the future and may serve as

visual references of each sample area. An example of a complete sample area photograph

is shown in Figure 3.9. An example of a closer perspective sample area photograph is

shown in Figure 3.10.

Figure 3.9 Example of Complete Sample Area Photograph

57

Figure 3.10 Example of Closer Perspective Sample Area Photograph

3.2.3.3 Dry-Film Thickness

A dry-film thickness gage was used to take an average of 9 thickness readings of

the rust layer formed on the girder at each sample location. One thickness reading was

taken at each corner of the sample area (4 readings), one thickness reading in between each

corner of the sample area (4 readings), and one thickness reading at the center of the sample

area. The average and standard deviation of the 9 readings was recorded in field data entry

sheets. Refer to Appendix C for field data entry sheets of each field bridge that was

evaluated in Phase 3.

3.2.3.4 Tape Samples

A clear tape adhesion test was performed by cutting a piece of clear tape

approximately 4 to 5 inches long. The tape was placed on the surface of the steel and rolled

over using a firm rubber “J” roller, making 10 passes with firm pressure (e.g.,

58

approximately 2 lbs. of normal force through the roller) was applied to the tape. The tape

was then removed by slowly peeling one end off from the surface of the steel at a shallow

angle, taking no longer than approximately 5 seconds to completely remove the tape. The

tape was then adhered to a sheet of white paper and the sample number was noted next to

the tape sample.

3.2.3.5 Rust Samples

Samples of rust were collected from each sample location and placed in a clean

clear, sealable plastic bag. Samples were obtained by scraping the steel surface with a

stainless-steel chisel or stainless-steel wire brush and collecting the rust in the plastic bag.

A portable scale was used to attempt to collect at least 2 grams of rust from each sample

location. If the rust sample weight was not sufficient due to good surface conditions, then

additional rust from the surrounding area was collected to total 2 grams wherever possible.

In cases with exceptionally good condition, 2 grams could not be collected after an

excessive amount of time. In all cases, the bag was labeled with the bridge ID and sample

reference number, and whether the sample was from an outer or inner lamina where

applicable.

3.2.3.6 Ultrasonic Thickness Measurements

Ultrasonic thickness measurements were taken in two locations, one representing

typical corrosion and another representing most severe corrosion. Measurements were

taken by removing surface rust and debris with a power grinder and stainless-steel wire

cup wheel, applying coupling gel and taking 5 thickness measurements within the sample

area. It was attempted that one thickness measurement was taken at the middle of the

sample area and one thickness measurement was taken at each corner of the sample area.

The average of the five thickness measurements, or the number for which a reading could

59

be obtained, was recorded in field data entry sheets. Refer to Appendix C for field data

entry sheets of each field bridge that was evaluated in Phase 3.

3.2.3.7 Severe Corrosion, Pitting, and Section Loss

Any severe corrosion, pitting, and section loss was measured and recorded if

applicable. The max length and width of any severely corroded areas was measured with a

tape measure. The depth of any severe pitting was measured with a lever pit gage. The

thickness of any severe section loss after collecting rust samples was measured with a tape

measure. Measurements were recorded in field data entry sheets. Refer to Appendix C for

field data entry sheets of each field bridge that was evaluated in Phase 3.

3.3 Data Collection

3.3.1 Clear Tape Adhesion Test

Tape samples obtained from the field work were analyzed to determine the rust

particle size distribution and percentage of the area of the tape that had rust particles

adhered to it. The tape samples were first scanned to create a digital image of the sample.

The image was then processed using a procedure developed in MATLAB to provide

quantifiable data. Refer to Appendix D for the MATLAB code used for this procedure. The

rust particles were assumed to be circular in order to easily group particles according to

size in diameter, using bins 0 to 1/32 in., 1/32 to 1/16 in., 1/16 to 1/8 in., 1/8 to 1/4 in. 1/4

to 1/2 in., 1/2 to 1 in., 1 in to 2 in., 2 in to 4 in., and greater than 4 in. The percent area of

rust particles represented by each bin as well as the total area of rust particles, or spatial

density of rust that adhered to each tape sample was determined.

60

3.3.2 Ion Chromatography Analysis

IC analyses were done to determine the soluble concentrations of chloride, sulfate,

and nitrate ions in rust samples obtained from the field work. Details regarding this data

collection process are documented in the draft field and laboratory protocols of Phase 3

(McConnell and Shenton, 2018).

61

Chapter 4

RESULTS

4.1 Qualitative Assessments of Bridges

After traveling to each of the seven clusters, a qualitative assessment of the 21 UWS

bridges that were inspected was done based on rust patina characteristics observed in the

field. Refer to Table 3.6 and Figure 3.8 for sample locations that were observed in the field.

This qualitative assessment does not refer to NBI condition ratings of the field bridges but

is rather the author’s subjective interpretation of the relative rust patina conditions of the

field bridges. Each field bridge was given a qualitative condition rating of either good or

poor based on observed characteristics of rust patinas. The locations of observed rust

patinas were organized into four groups: exterior flange of fascia girder (includes standard

sample area locations 4 and 10), exterior web of fascia girder (includes standard sample

area locations 5 and 11), interior flanges (includes standard sample area locations 1, 3, 6,

7, 9, and 12), and interior webs (includes standard sample area locations 2 and 8) as the

locations within these groups exhibited similar rust patina conditions of individual field

bridges.

The observed conditions of rust patinas were grouped into three different categories

of either compact rust patina (rating = 1), small rust flakes (rating = 2), or large thick rust

flakes (rating = 3) as these were typical rust patina conditions of all field bridges that were

assessed. Compact rust patinas were defined as surfaces where the patina was adherent and

it was difficult to remove rust. See Figure 4.1 for a typical example of a compact rust patina.

Small rust flakes and large thick rust flakes were defined as steel surface conditions where

62

the corrosion particles were more easily removed from the surface. Small rust flakes were

defined as granular (spherical like) corrosion products. See Figure 4.2 for a typical example

of small rust flakes. Large thick rust flakes were defined as sheet-like formations of

corrosion products. See Figure 4.3 for a typical example of large thick rust flakes.

Figure 4.1 Typical Example of a Compact Rust Patina

63

Figure 4.2 Typical Example of Small Rust Flakes

Figure 4.3 Typical Example of Large Thick Rust Flakes

The rust patina ratings assigned to each of the four categorized locations were then

summed to obtain an overall qualitative assessment rating of each field bridge. Prior to

64

summing the ratings of the individual categories, the rust patina condition rating of the

interior flange location was multiplied by three because this qualitative assessment location

contained three times the number of standard sample area locations as compared to the

other categories. An overall qualitative assessment rating greater than or equal to 6 (the

minimum possible qualitative assessment rating) and less than or equal to 11 signified good

condition (i.e., the sample locations of these bridges had compact rust patinas throughout

the bridge or a combination of compact rust patinas in some locations and small rust flakes

on one or more flange locations). Conversely, an overall qualitative assessment rating

greater than 11 and less than or equal to 18 (the maximum possible qualitative assessment

rating) signified poor condition (i.e., these bridges had large thick rust flakes observed in

at least one sample location). Refer to Table 4.1 for a summary of qualitative assessment

condition ratings of each field bridge. The colors of the rust patinas are also described in

the following subsections; however, this was mainly done for additional information rather

than an assessment of performance. Future work can be done to assess the relevance of

color to UWS performance, which is discussed in Section 2.2.1.

65

Table 4.1 Field Bridge Qualitative Assessment Condition Ratings

Field Bridge

Rust Patina Condition

of Exterior Flange

of Fascia Girder

Rating

of Exterior Flange

of Fascia Girder

Rust Patina Condition

of Exterior Web

of Fascia Girder

Rating

of Exterior Web

of Fascia Girder

Rust Patina

Condition

of Interior Flanges

Rating

of Interior

Flanges

Rust Patina

Condition

of Interior Webs

Rating

of Interior

Webs

Overall

Qualitative

Assessment

Rating

Condition

CO E-16-JW compact rust patina 1 compact rust patina 1 compact rust patina 1 compact rust patina 1 6 good

CO E-16-JX compact rust patina 1 compact rust patina 1 compact rust patina 1 compact rust patina 1 6 good

CO E-16-JZ compact rust patina 1 compact rust patina 1 compact rust patina 1 compact rust patina 1 6 good

CT 3830 compact rust patina 1 compact rust patina 1 small rust flakes 2 compact rust patina 1 9 good

CT 4382 compact rust patina 1 compact rust patina 1 small rust flakes 2 compact rust patina 1 9 good

CT 5796 small rust flakes 2 compact rust patina 1 small rust flakes 2 compact rust patina 1 10 good

IA 004111 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 compact rust patina 1 12 poor

IA 041331 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 small rust flakes 2 13 poor

IA 042711 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 compact rust patina 1 12 poor

MN 04019 small rust flakes* 2 compact rust patina* 1 large thick rust flakes 3 compact rust patina 1 13 poor

MN 19811 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 small rust flakes 2 13 poor

MN 62861 large thick rust flakes 3 large thick rust flakes 3 large thick rust flakes 3 large thick rust flakes 3 18 poor

NC 190083 compact rust patina 1 compact rust patina 1 compact rust patina 1 compact rust patina 1 6 good

NC 1290057 compact rust patina 1 compact rust patina 1 small rust flakes 2 compact rust patina 1 9 good

NC 1290058 compact rust patina 1 compact rust patina 1 small rust flakes 2 compact rust patina 1 9 good

NH 017201120011300 compact rust patina 1 compact rust patina 1 compact rust patina 1 compact rust patina 1 6 good

NH 11101120017900 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 small rust flakes 2 13 poor

NH 017701460003700 compact rust patina 1 compact rust patina 1 large thick rust flakes 3 compact rust patina 1 12 poor

OH 7700105 compact rust patina 1 small rust flakes 2 large thick rust flakes 3 small rust flakes 2 14 poor



*The exterior of the fascia girder of bridge MN 04019 was obstructed by a sign, so samples were taken from the interior portion of the fascia girder instead

66

4.1.1 Colorado Bridges

All three Colorado bridges (CO E-16-JW, CO E-16-JX, and CO E-16-JZ) were in

the best condition out of all of the bridges that were inspected. The webs consisted of

smooth, compact rust patinas that were a light orange-maroon color. Refer to Figure 4.4

for a typical example of the webs of the Colorado bridges. It should be noted that all three

of the Colorado bridges were box girders, so samples were taken from the bottom of the

bottom flange rather than the top of the bottom flange. Refer to Table 4.2 for descriptions

of sample locations of the Colorado field bridges.

Figure 4.4 Typical Web Patina of Colorado Bridges

67

Table 4.2 Standard Sample Area Location Descriptions of Colorado Bridges

The bottoms of the bottom flanges appeared to not yet developed a full rust patina

as most of the steel was a smooth texture and grey in color (the research team members

who inspected these bridges questioned whether the bottom flanges were UWS because of

this observation). The rust that had developed so far was compact and an orange-maroon

color at these locations. Refer to Figure 4.5 for a typical example of the bottom of the

bottom flanges of the Colorado bridges. It was very difficult to scrape any material off of

these bridges. Overall, these bridges were in good condition because of their compact rust

patinas at all sampled locations.

Standard

Sample Area

Location ID

Colorado Field Bridge Location Descriptions

1 bottom of bottom flange of interior girder facing traffic over the shoulder

2 lower web of interior girder facing traffic over the shoulder

3 bottom of bottom flange of interior girder facing backside of traffic over the shoulder

4 bottom of bottom flange of exterior girder facing traffic over the shoulder

5 lower web of exterior girder facing traffic over the shoulder

6 bottom of bottom flange of exterior girder facing backside of traffic over the shoulder

7 bottom of bottom flange of interior girder facing traffic over the right travel lane

8 lower web of interior girder facing traffic over the right travel lane

9 bottom of bottom flange of interior girder facing backside of traffic over the right travel lane

10 bottom of bottom flange of exterior girder facing traffic over the right travel lane

11 lower web of exterior girder facing traffic over the right travel lane

12 bottom of bottom flange of exterior girder facing backside of traffic over the right travel lane

68

Figure 4.5 Typical Bottom of Bottom Flange Patina of Colorado Bridges

4.1.2 Connecticut Bridges

4.1.2.1 CT 3830

The exterior flange of the fascia girder of bridge CT 3830 was comprised of a

compact rust patina that was a light maroon color. Both the interior and exterior webs of

the bridge also had compact rust patinas that were a dark maroon color with dark orangey

streaks. The interior flanges’ patinas consisted of small rust flakes that were a light grey-

brown color with many maroon spots. Overall, this bridge was in good condition based on

the compact rust patina formed on the fascia beam and interior webs. The interior flanges

posed minor concerns with the small rust flakes.

4.1.2.2 CT 4382

The exterior flange of the fascia girder of bridge CT 4382 had a compact rust patina

that was a light brown-maroon color. Both of the exterior and interior webs also had

compact rust patinas. The exterior web location was a dark maroon color with dark orangey

69

spots. The interior webs were a dark maroon color with some dark orangey spots and

streaks. The interior flanges had small rust flakes and were a light grey-brown color with

some maroon spots. Overall, this bridge was in good condition based on the compact rust

patinas on the exterior portions of the fascia beam and interior webs. The interior flanges

posed minor concerns based on the small rust flakes observed.

4.1.2.3 CT 5796

Bridge CT 5796 had small rust flakes present on all of the flanges. The rust patina

on the flanges was a light grey-brown color with many maroon spots. Refer to Figure 4.6

for a typical example of the flanges of the Connecticut bridges. The interior and exterior

webs both had compact rust patinas that were a dark maroon color with dark orangey

streaks. Refer to Figure 4.7 for a typical example of the interior and exterior webs of the

Connecticut bridges. Overall, this bridge was in good condition because of the compact

rust patinas formed on the webs. The small rust flakes that were observed on the flanges

posed minor concerns.

70

Figure 4.6 Typical Flange Patina of Connecticut Bridges

Figure 4.7 Typical Web Patina of Connecticut Bridges

71

4.1.3 Iowa Bridges

4.1.3.1 IA 004111

The exterior web of the fascia girder of bridge IA 004111 had a compact rust patina

that was a dark maroon color. The fascia girder’s exterior flange and interior webs both

had compact rust patinas that were a dark maroon color with orangey spots. The interior

flanges had large thick rust flakes that were a grey color with orangey spots. Overall, this

bridge was in poor condition because of the large thick rust flakes found on the interior

flanges.

4.1.3.2 IA 041331

The exterior flange and web of the fascia girder of bridge IA 041331 both had

compact rust patinas that were a dark maroon color with orangey-grey spots. The interior

webs had small rust flakes that were easy to scrape off and were a dark orange color. The

interior flanges had large thick rust flakes that were easy to peel off and were a grey color

with orangey spots. Overall, this bridge was in poor because condition when considering

the large thick rust flakes found on the interior flanges.

4.1.3.3 IA 042711

The exterior flange of the fascia girder of bridge IA 042711 had a compact rust

patina that was a grey color with orangey spots. The interior and exterior webs had compact

rust patinas that were a dark maroon color with some orangey spots. Refer to Figure 4.8

for a typical example of the exterior and interior webs of the Iowa bridges. The interior

flanges had large thick rust flakes that were easily peeled off and were a grey color with

orangey spots. Refer to Figure 4.9 for a typical example of the interior flanges of the Iowa

bridges. The interior flange of the exterior girder was in the worst condition of the locations

that were assessed on the bridge because most of the rust patina was already flaked off.

72

Overall, this bridge was in poor condition because the interior flanges were comprised of

large thick rust flakes.

Figure 4.8 Typical Web Patina of Iowa Bridges

73

Figure 4.9 Typical Interior Flange Patina of Iowa Bridges

4.1.4 Minnesota Bridges

4.1.4.1 MN 04019

The exterior of the fascia girder of bridge MN 04019 was obstructed by a sign, so

samples were taken from the interior portion of the fascia girder. The interior web of the

fascia girder had a compact rust patina that was a dark brown color with some orangey

streaks. The web of the interior girder also had a compact rust patina but showcased a dark

brown color with orangey spots instead of streaks. Refer to Figure 4.10 for a typical

example of the interior webs of bridge MN 04019. The interior flanges of the bridge

exhibited large thick rust flakes that were a grey color with brown spots. Refer to Figure

4.11 for a typical example of the interior flanges of bridge MN 04019. Overall, this bridge

was in poor condition because of the flaking rust patinas found at the flange locations.

74

Figure 4.10 Typical Interior Web Patina of Bridge MN 04019

Figure 4.11 Typical Interior Flange Patina of Bridge MN 04019

75

4.1.4.2 MN 19811

The exterior flange of the fascia girder of bridge MN 19811 had a compact rust

patina that was a brown color with white spots. The exterior web of the fascia girder also

had a compact rust patina that was a dark brown color with some orangey streaks. Refer to

Figure 4.12 for an example of the exterior flange and web of the fascia girder of bridge

MN 19811. The interior flanges’ rust patinas were comprised of large thick rust flakes and

were grey and brown in color. The interior webs had small rust flakes and was brown with

dark maroon spots. Refer to Figure 4.13 for typical example of the interior flanges and

webs of bridge MN 19811. Overall, this bridge was in poor condition because of the flaking

rust patinas of the interior sections of the bridge.

Figure 4.12 Exterior Flange and Web Patinas of the Fascia Girder of Bridge MN 19811

76

Figure 4.13 Typical Interior Flange and Web Patinas of Bridge MN 19811

4.1.4.3 MN 62861

The exterior flange of the fascia girder of bridge MN 62861 had large thick rust

flakes that were a greyish brown color. The interior flanges also had rust patinas with large

thick rust flakes that had a smooth texture and were a grey color. Underneath the smooth

grey flakes the steel was a dark marron color with some orangey spots. The rust patinas of

the both the exterior and interior webs also had large thick rust flakes which were a grey

color with some orangey spots. Refer to Figure 4.14 for a typical example of the flanges

and webs of bridge MN 62861. Overall, this bridge was in poor condition and was

subjectively the worst performing bridge relative to the other two Minnesota bridges

because all portions that were evaluated exhibited large thick rust flakes.

77

Figure 4.14 Typical Flange and Web Patinas of Bridge MN 62861

4.1.5 North Carolina Bridges

4.1.5.1 NC 190083

Bridge NC 190083 was the only field bridge that was evaluated that crossed a

railroad. All portions of bridge NC 190083 consisted of compact rust patinas with no

observed flaking or pitting. The exterior and interior flanges were both a light brown-

maroon color with dark maroon spots. The exterior web was a dark maroon color with dark

orangey-brown streaks while the interior webs were a dark maroon color with dark maroon

spots. Refer to Figure 4.15 for a typical example of the flanges and webs of bridge NC

190083. Overall, this bridge was in good condition because of the compact rust patinas.

78

Figure 4.15 Typical Flange and Web Patinas of Bridge NC 190083

4.1.5.2 NC 1290057

. The exterior of the fascia girder of bridge NC 1290057 exhibited compact rust

patinas that were both a dark maroon color with orange and brown spots. The interior webs

of the bridge also had compact rust patinas that were a dark maroon color with orangey

streaks. Refer to Figure 4.16 for a typical example of the exterior flange and webs of bridge

NC 1290057. The interior flanges of the bridge had small rust flakes and was a greyish

maroon color with orangey spots. Refer to Figure 4.17 for a typical example of the interior

flanges of bridge NC 1290057. Overall this bridge was in good condition because all

inspected locations of the girders showcased compact rust patinas with only minor

concerns of small rust flakes on the interior flanges.

79

Figure 4.16 Typical Exterior Flange and Web Patinas of Bridge NC 1290057

Figure 4.17 Typical Interior Flange Patina of Bridge NC 1290057

80

4.1.5.3 NC 1290058

The exterior flange of the fascia girder of bridge NC 1290058 consisted of a

compact rust patina and were a light brown-maroon color with dark maroon spots. The

exterior and interior webs both exhibited compact rust patinas and were a dark maroon

color with dark orangey-brown streaks. Refer to Figure 4.18 for a typical example of the

exterior flange and webs of bridge NC 1290058. The interior flanges had a mysterious

moist dark grey film covering them. This can be seen in Figure 4.19, which shows a typical

example of the interior flanges of bridge NC 1290058. It appeared the actual rust patina

was underneath the film when scrapping for samples which had some small rust flakes and

was a spotty maroon-orangey color. Overall, this bridge was in good condition because it

mostly consisted of compact rust patinas. There were only minor concerns regarding the

interior flanges which had a mysterious moist dark grey film covering them and exhibited

small rust flakes.

Figure 4.18 Typical Exterior Flange and Web Patinas of Bridge NC 1290058

81

Figure 4.19 Typical Interior Flange Patina of Bridge NC 1290058

4.1.6 New Hampshire Bridges

4.1.6.1 NH 017201120011300

All exterior and interior locations of the bridge that were evaluated showcased

compact rust patinas with no observed flaking or pitting. Refer to Figure 4.20 for a typical

example of the flanges and webs of bridge NH 017201120011300. The flanges were

typically a light maroon color while the webs were typically a dark spotty grey and maroon

color with a lot of orangey spots. Overall, this bridge was in good condition and was

subjectively the best performing bridge of the three bridges inspected in the NH cluster.

82

Figure 4.20 Typical Flange and Web Patinas of Bridge NH 017201120011300

4.1.6.2 NH 11101120017900

Bridge NH 11101120017900 was not originally included as a field bridge for the

NH cluster. Refer to Section 3.1.5 for reasoning of including this bridge as a field bridge

in the NH cluster. The interior flanges of bridge NH 11101120017900 had a smooth texture

on the surface and were a light grey-brown color. When scraping the interior flanges to

collect rust samples, large thick rust flakes would chip off and pitting was created. Refer

to Figure 4.21 for a typical example of the interior flanges of bridge NH 11101120017900.

The patina of the exterior flange and web of the fascia girder had compact rust patinas that

were a rough texture and a dark maroon color with some orangey spots. Refer to Figure

4.22 for a typical example of the exterior flange and webs of bridge NH 11101120017900.

The interior webs exhibited small rust flakes as can be seen in Figure 4.23. Overall, this

bridge was in poor condition because of the large thick rust flakes that easily chipped off

when physically scraping the patina on the interior flanges as well as the small rust flakes

on the interior webs.

83

Figure 4.21 Typical Interior Flange Patina of Bridge NH 11101120017900

Figure 4.22 Typical Exterior Flange and Web Patina of Bridge NH 11101120017900

84

Figure 4.23 Typical Interior Web Patina of Bridge NH 11101120017900

4.1.6.3 NH 017701460003700

Bridge NH 017701460003700 had a lot of birds living on the underside of the

structure. There were multiple nests and bird droppings found along the flanges of the

interior beams. The interior flanges showcased thick large rust flakes. The flanges were

typically a light maroon color. Refer to Figure 4.24 for a typical example of the interior

flanges of bridge NH 017701460003700. The exterior flange of the fascia girder as well as

the interior and exterior webs both consisted of compact rust patinas. The webs were

typically a dark maroon color with some orangey streaks. Refer to Figure 4.25 for a typical

example of the exterior flange and webs of bridge NH 017701460003700. Overall, this

bridge was in poor condition because of the large thick rust flakes and bird nests observed

on the interior flange locations.

85

Figure 4.24 Typical Interior Flange Patina of Bridge NH 017701460003700

Figure 4.25 Typical Exterior Flange and Web Patinas of Bridge NH 017701460003700

86

4.1.7 Ohio Bridges

4.1.7.1 OH 7701977

The exterior flange of the fascia girder of bridge OH 7701977 had a compact rust

patina that was a brown color with grey spots. Both the interior and exterior webs had small

rust flakes that were a dark maroon color with some orangey stripes. The interior flanges

had large thick rust flakes that were a grey and dark brown color. Overall, this bridge was

in poor condition because of the flaking rust patinas observed at interior sections of the

bridge.

4.1.7.2 OH 7701993

The exterior flange of the fascia girder of bridge OH 7701993 had a compact rust

patina that was a brown color with grey spots. Both the interior and exterior webs had small

rust flakes that were a dark maroon color with some orangey stripes. The interior flanges

had large thick rust flakes with a rough texture on the surface. The rust flakes were easy to

peel off and were a brown color. Overall, this bridge was in poor condition because of the

flaking rust patinas observed at interior sections of the bridge.

4.1.7.3 OH 7700105

The exterior flange of bridge OH 7700105 had a compact rust patina that was a

brown color with grey spots. Refer to Figure 4.26 for a typical example of the exterior

flanges of the Ohio bridges Both the interior and exterior webs had small rust flakes that

were a dark maroon color with some orangey spots. Refer to Figure 4.27 for a typical

example of the interior and exterior webs of the Ohio bridges. The interior flanges had

large thick rust flakes with some already flaked off that were a grey color with small orange

spots. Refer to Figure 4.28 for a typical example of the interior flanges of the Ohio bridges

87

Overall, this bridge was in poor condition because of the flaking rust patinas observed at

interior sections of the bridge.

Figure 4.26 Typical Exterior Flange Patinas of Ohio Bridges

88

Figure 4.27 Typical Interior and Exterior Web Patinas of Ohio Bridges

Figure 4.28 Typical Interior Flange Patinas of Ohio Bridges

89

4.2 Findings Related to Bridge Maintenance Practices

4.2.1 Findings from Review of Maintenance Manuals

As part of the maintenance survey that was sent out to 52 agencies, each agency

was asked to provide any bridge maintenance manuals, as described by Question 1 in

Appendix E.1. The purpose of this was to ultimately form possible correlations between

UWS bridge performance and the maintenance practices described in each agencies’

maintenance manual.

4.2.1.1 Response Rates

A summary of the type of responses received (or lack thereof) from the 52 agencies

in terms of maintenance manuals is shown in Appendix F, Table F.1. Overall, bridge

maintenance manuals were available for review from a total of 34 agencies. Twenty-one

(21) agencies that responded to the survey supplied maintenance manuals pertaining to

bridge maintenance practices and manuals were available from an additional thirteen (13)

agencies from prior work (Shenton, 2016). Twelve (12) agencies responded but were

unable to provide a manual. Of these twelve (12), four (4) agencies responded explaining

that they were in the process of working on a manual and eight (8) agencies responded

stating that they did not have a bridge maintenance manual. There were 19 agencies that

did not respond to the survey, and therefore did not provide a manual.

4.2.1.2 Review of Maintenance Manuals Results

The bridge maintenance manuals that were available from the 34 agencies were

each reviewed in terms of information relevant to UWS bridge performance. There were

common categories that were found between most of the manuals. These categories

included joint maintenance, bearing maintenance, bridge washing, girder maintenance,

90

information specific to UWS bridges, and corrosion. Table 4.3 lists the specific categories

of information that were established to organize the contents provided in various manuals.

91

Table 4.3 Maintenance Manual Review

AgencyJoint

Clean

Joint

Repair/

Maint.

Joint

Elim.

Bearing

Clean

Bearing

Repair/

Maint.

Girder

Clean

Bridge

Wash

(gen.)

Beam

End

Wash

(only)

Girder

Repair/

Maint.

UWS

SpecificCorrosion

Objective

Manual

Rating1

Subjective

Manual

Rating2

AL X X — X — — — — X — — 2 2

AZ — — — — — — — — — — — 0 1

AR X X X — — — — X — — — 2 3

CA — X — — — — — — X — — 1 2

CO — X X — X — X — — X X 2 3

CT — X — — X — X — — — X 2 2

DE — X — X X X — — X — — 2 3

FL — X — X X — — — X — X 2 3

GA X X — — X — — — X — — 2 3

HI — X — — X — — — X — — 1 2

IN X — — X — — — X — — — 1 2

IA X X — — X X — — X — — 2 3

MA — — — — — — — — — — — 0 1

MD X X — X X — — — — — — 2 2

MI — X — — — — X — — — — 1 2

MN X X — X X — — — X X — 2 2

MO X X — X X X — — — — — 2 3

MT X X — X — X — — — — — 2 2

NE X X — X X X — — X — X 3 3

NV — X — — X — — — X — X 2 3

NH — — — — — — X — — — — 1 1

NJ — X — — X — — — X X X 2 3

NM — X — — — — — — X — — 1 2

NY — X — X X — — X X X — 2 3

ND X X — X X X — — — — — 2 3

OH — X — — X — — — X — X 2 3

PA X X X X X X — — X — X 3 3

TX X — — — — — — — — — — 1 2

UT — — — — — — — — — — — 0 1

VA — X X — — — — — X — X 2 3

WA X X — — — — — — — — — 1 2

WI X X X X X — — X — — X 3 3

WY X X — X X — — — — — — 2 2

Total 16 27 5 14 19 7 4 4 16 4 10

X = relevant information available

— = no relevant information1 manual rated based on the number of categories it contained with maintenance information relevant to WS performance:

3 = greater than 6 categories

2 = greater than 3, but less than 6 categories

1 = greater than 0, but less than or equal to 3 categories

0 = 0 categories2 manual rated based on qualitative judgement of information provided relevant to WS maintenance:

1 = no repair/maintenance information

2 = mentions repair/maintenance information

3 = extensive repair/maintenance information

(ie. provides information on defects along with suggested repairs/maintenance)

92

Of the 34 manuals that were reviewed, a majority of them (27) included information

about maintaining and / or repairing joints and 16 manuals had information about joint

cleaning. Maintaining and / or repairing bearings was another category that was found in

most (19) of the manuals that were reviewed and 14 had information about bearing

cleaning. Sixteen (16) manuals provided information regarding girder repair and / or

maintenance and ten (10) had information about corrosion issues. The less common

categories of information included bridge washing in terms of girder cleaning (7), joint

elimination (5), information specific to UWS (4), and general bridge washing information

(4), and washing beam ends (4).

To experiment with different ways of quantifying this information, an objective

rating, ranging between 0 and 3, was given to each manual based on the number of the

categories listed in Table 4.2 that it contained. An objective rating of 3 corresponded to

more than 6 categories, a rating of 2 corresponded to 4 or 5 categories, a rating of 1

corresponded to 1 to 3 categories, and a 0 corresponded to 0 categories. A subjective rating,

ranging between 1 and 3, was also given based on the extent of information provided in

the manual. For the subjective rating, a rating of 3 corresponded to extensive information

such as likely defects along with suggested repair and / or maintenance activities; a rating

of 2 corresponded to a mention of repair and / or maintenance activities, and a rating of 1

corresponded to no repair and / or maintenance information.

Figure 4.29 and Figure 4.30 show a summary of the objective and subjective

manual ratings, respectively. Figure 4.29 shows that most of the manuals that were

reviewed contained information on 4 or 5 of the categories listed in Table 4.3 based on

there being 19 agencies with an objective manual rating of 2. The two most common

categories of maintenance information that these manuals included were related to joint

repairs/maintenance (19 manuals) and bearing repairs/maintenance (15 manuals). Figure

93

4.30 shows that the scope of information provided in the manuals is generally rather

detailed. There were three manuals (one from Nebraska, one from Pennsylvania, and one

from Wisconsin) that received both an objective and subjective rating of 3, meaning they

included a majority of the maintenance categories and provided information about bridge

defects along with suggested repairs or maintenance practice protocols. These were the

overall highest rated manuals. Three manuals (one from Arizona, one from Massachusetts,

and one from Utah) received an objective rating of 0 and subjective rating of 1 meaning

they included none of the maintenance categories and had no maintenance or repair

information. These were the overall lowest rated manuals. Future work will include using

these objective and subjective manual ratings to assess correlations with UWS bridge

performance.

Figure 4.29 Objective Manual Ratings, by Agency

Rating = 3, 3

Rating = 2, 19

Rating = 1, 8

Rating = 0, 3

94

Figure 4.30 Subjective Manual Rating, by Agency

4.2.2 Findings from Washing Practices Survey

The survey on washing practices aimed to:

• Determine whether agencies washed bridges or not.

• Quantify the approximate percentage of UWS bridges washed: none, <

10%, 10 – 50%, or > 50%.

• Quantify washing frequency: more than once per year, annually, every

two years, or less frequently than every two years.

• Determine if bridges were: not washed in any particular time of year,

typically washed in the Spring, or typically washed during some other

time of year.

• Determine if the washing practices for UWS bridges included the

girders: always, at least half of the time (i.e., mostly), less than half of

the time (i.e., rarely), or never.

• Determine if different washing practices existed for UWS bridges and

other bridges.

Rating = 3, 16

Rating = 2, 13

Rating = 1, 4

95

The focus of this section is to discuss the responses to the above questions. In

addition to the surveying conducted as part of the present research, it was also discovered

that the American Association of State Highway and Transportation Officials’ (AASHTO)

Highway Subcommittee on Bridges and Structures (SCOBS) conducts an annual “State

Bridge Engineers Survey” (AASHTO, 2018). This survey contains the following questions

regarding bridge washing:

• Does your Agency utilize bridge-washing contracts?

• Has your Agency successfully included any of these preventative

maintenance activities on a bridge-washing contract?

• Has your Agency conducted a comprehensive study of the cost-

effectiveness of bridge cleaning and washing measures?

• Has your Agency evaluated the effect of a periodic program of bridge

cleaning and washing on the service life of bridge elements?

The comparison of the responses to these questions for the most recent surveying year is

also discussed in Section 4.2.2.1 (AASHTO, 2018).


Responses in terms of washing practices were received from the 33 state highway

agencies listed in Appendix F, Table F.2. The data in Table F.2 is summarized by Figures

4.31 – 4.33. These figures report the numbers of agencies in different categories, based on

a fixed sample size of 52 possible participants (one from each state highway agency in

addition to the highway agencies in the District of Columbia and Puerto Rico).

96

Figure 4.31 Approximate Percentages of Bridges Washed, by Agency

Figure 4.32 Frequency of Bridge Washing, by Agency

>50%, 7

10-50%, 8

<10%, 4

0%, 11

No Data, 22

Annually, 12

Every 2 Years, 5

Less Frequently, 5

No Data, 30

97

Figure 4.33 Frequency of Girder Washing, by Agency

4.2.2.2 Washing Practices Survey Results

Figure 4.31 shows that 19 agencies reported performing bridge washing to some

extent (sum of the agencies in the >50%, 10-50%, <10% approximate percentage of bridges

washed categories). This represents a majority of the agencies that responded to the survey,

63% of respondents, but only 37% of all agencies. In comparison, 12 agencies out of 40

respondents reported utilizing bridge washing contracts in the AASHTO survey

(AASHTO, 2018). It is possible that the discrepancy in the present survey and the

AASHTO survey is related to the specificity of asking if bridges were washed by contracts

in the AASHTO survey, given that some owners may use their own resources to perform

bridge washing (AASHTO, 2018).

Even though the majority of the respondents in the present survey indicated that

they do perform bridge washing, this should not be interpreted to mean that bridge washing

Always, 2 Typically, 1

Rarely, 8

No, 8

No data, 33

98

is a common practice. This conclusion is based on that fact that Figure 4.31 indicates that

only 7 agencies reported that they wash more than 50% of their bridges.

Figure 4.32 shows that 22 agencies provided information on the frequency of bridge

washing. Figure 4.32 also shows that for bridges that are washed, this is typically conducted

annually (55% of respondents) or bi-annually (23% of respondents). More frequently than

annually was an option provided in the survey, but no agencies reported washing more

frequently than annually. Regarding the time of year during which washing is performed,

16 agencies reported that this was performed in the spring. No other regular time of year

was reported, but 1 agency reported that the time of year varied based on contracts.

Figure 4.33 shows that it is relatively rare for the girders of the bridge to be washed,

with the washing typically limited to other components such as decks, bearings, and / or

drainage systems. Only 3 agencies reported typically or always washing the girders:

Minnesota, Rhode Island, and Washington.

The majority of respondents indicated that they have equivalent washing practices

for UWS and other bridge types. Five agencies reported that they have different practices,

but no details on the differences were provided or could be discerned from the agencies’

maintenance manuals.

Regarding the other aspects of bridge washing that were queried by the AASHTO

survey, no more than one agency indicated the successful use of any other preventative

maintenance activity on a bridge washing contract (AASHTO, 2018). These items

represent fairly generic items. Those that were reported to be successful by a single agency

were: joint sealing, spot painting, joint closures, bridge repairs, drainage repairs, bearing

replacement, corrosion protection, and other. No agencies reported having conducted any

analysis of cost-effectiveness or service life related to bridge washing.

99

4.3 Findings Related to Deicing Agent Usage

4.3.1 Findings from Deicing Agent Usage Survey

Each of the 52 LTBPP state coordinators were asked to supply information

regarding their use of deicing agents. The specific wording of this request can be found as

Question 3 in the survey shown in Appendix E.1. The purpose of this was to assess possible

correlations between UWS bridge performance and amounts of corrosive deicing agents

being applied to roadways. For the purposes of this research, corrosive deicing agents were

defined as those containing chloride, which is known to negatively impact performance of

UWS.


In the original survey (Appendix E.1) that was sent out, each agency was asked to

supply as much information as possible regarding salts or chemicals used for deicing and

snow removal. A wide range of types of responses was received in terms of the deicing

chemicals that were used and the level of detail of the data. To refine this information, the

chemicals that were reported by each agency were categorized into corrosive solids,

corrosive brines, and other. Corrosive solids included chloride containing chemicals such

as sodium chloride, magnesium chloride, and calcium chloride. Corrosive brines included

brines containing chloride chemicals such as, sodium chloride brine, magnesium chloride

brine, calcium chloride brine, and prewetting brine. Quantities of other (non-chloride

containing) deicing agents were deemed too variable to be meaningfully synthesized. A

total of 30 responses were received from the original survey. The data that was provided

revealed that the most relevant data that was widely available was agency-wide average of

annual quantities per lane mile. Deicing agent quantities per lane mile provided more

valuable information by normalizing the data between each agency.

100

Thus, a follow-up survey (Appendix E.2 and Appendix E.3) requested deicing

agent usage to be reported in amounts per lane mile if not already provided. A total of 24

responses were received from the follow-up survey, representing 21 clarifying responses

and 3 new responses. At this stage, the existing normalized deicing agent data was

reviewed and found to be highly variable, even between agencies with relatively similar

environments. Thus, the data that had been received from each agency was compared to

the average quantities. Then, this information was shared with the owners and the owners

were asked to confirm if this comparison was reasonable in their opinion or if the data

should be updated (see example text for this inquiry in Question 1 of the follow-up survey

for prior participants shown in Appendix E.2). This process resulted in correcting some

misunderstandings about the type of data that was being sought (e.g., cumulative annual

totals versus application rates per pass of a plow truck) and other reporting errors. All final

data has been reviewed for reasonableness and found to be satisfactory.

Near the conclusion of the data collection period, one of the respondents forwarded

state-level data on deicing agents collected by Clear Roads. Clear Roads “is a national

research consortium focused on rigorous testing of winter maintenance materials,

equipment and methods for use by highway maintenance crews” (Clear Roads, 2019). The

Clear Roads quantities and lane miles were found to be in general agreement with the

deicing agent data that had been previously collected. In 21 cases, the Clear Roads data

contained information from agencies for which no data or incomplete data had been

received as part of the maintenance survey. In these situations, the Clear Roads data was

extracted and added to the data set. In total, deicing agent data is available from 39 agencies

and available in terms of quantities per lane mile from 37 agencies.

There were 4 agencies (Maryland, New Hampshire, Pennsylvania, and Wisconsin)

that supplied a geographic breakdown of their deicing agent usage. The geographic

101

breakdowns included deicing agent usage by different regions (e.g., districts or counties)

within each agency. Specifically, Maryland supplied records for each of their 7 districts,

Pennsylvania and Wisconsin provided data for each of their counties, and New Hampshire

differentiated their data by northern or southern half of the state and the type of roadway

(e.g., interstate, primary and secondary highways). Table 4.4 shows the state average

compared to the local jurisdiction maximum and minimum deicing agent usage for the 4

agencies that supplied regional data.

Table 4.4 Regional Deicing Agent Use Statistics

The data in Table 4.4 is in terms of corrosive solids applied per lane mile, because

only Wisconsin supplied information on corrosive brines at this level of detail. The data

in Table 4.4 shows that the maximum is between 2 and 9 times the minimum deicing agent

use for these four agencies and the maximum is on average twice the average deicing agent

use for these four agencies. The region corresponding with the maximum and minimum

application rates for each agency is also listed. The county level corrosive brine data

provided by Wisconsin showed a variability between 0 (in multiple counties) and 477

gallons/lane mile in Florence County (which is in a rural area), with an average of 76.8

gallons/lane mile. This suggests that maximum deicing agent use is more driven by

topography than population as the maximum deicing agent use generally occurs in rural

Agency

Average

Corrosive

Solids/Lane

Mile

(tons/lane

mile)

Maximum

Corrosive

Solids/Lane

Mile

(tons/lane

mile)

Region with Maximum Corrosive

Solids/Lane Mile

Minimum

Corrosive

Solids/Lane

Mile

(tons/lane

mile)

Region with Minimum

Corrosive Solids/Lane Mile

Maryland 12.1 31.2 District 6 (mountainous and rural) 3.5 District 1 (rural)

New Hampshire 28 41 Interstates in Northern Half of NH 22 Primary and Secondary Highways in Southern Half of NH

Pennsylvania 7.7 14.1 Butler (suburban) 2.7 Juniata (rural)

Wisconsin 15.3 25.3 Vilas (rural) 5.4 Richland (urban)

102

areas. This information may be used to further assess what effects deicing agent usage has

on UWS bridges located in specific regions for these 4 agencies.

4.3.1.2 Deicing Agent Usage Survey Results

Appendix F, Table F.3 shows amounts of corrosive solids and corrosive brines for

the 38 agencies from which this data was available. Total quantities are reported in terms

of tons of corrosive solids (defined as chloride containing chemicals, which included

sodium chloride, magnesium chloride, and calcium chloride) and gallons of corrosive

brines (defined as brines containing chloride, which included sodium chloride brine,

magnesium chloride brine, calcium chloride brine, and prewetting brine). The total number

of lane miles that these deicing agents were applied to by each agency is also reported in

Appendix F, Table F.3. In some cases, there are differing numbers of lane miles for solids

and brines and both numbers are reported, respectively. Deicing agent usage was also

recorded in terms of quantities per lane mile in order to normalize the data and be able to

compare usage rates between each agency.

Appendix F, Table F.4 shows the statistics for the deicing agent data. The median,

mean, standard deviation, maximum, and minimum are reported for corrosive solids,

corrosive solids per lane mile, corrosive brines, and corrosive brines per lane mile. These

statistics can allow comparisons to be made between the deicing agent data for each

agency. For instance, the individual data for each agency can be compared with the mean

or median to see if that agency uses a relatively high or low amount of deicing agents

compared with the rest of the data set. The average amount of corrosive solids per lane

mile was 10.0 tons per lane mile and the average amount of corrosive brines per lane mile

was 156.0 gallons per lane mile.

103

Figure 4.34 graphs the amounts of corrosive solids and corrosive brines in amounts

per lane mile for each agency for which deicing agent usage data was available. This graph

demonstrates the variability in amounts of corrosive chemicals that each agency applied to

their roadways. This data will be used to assess its correlation with UWS bridge

performance.

Figure 4.34 Deicing Agent Usage, by Agency with Available Data

4.4 Field Results

4.4.1 Tape Test Results

The clear tape adhesion test was performed on each field bridge at each of the 12

sample locations except for bridge MN 04019, which only had 10 samples taken (no

samples taken from standard sample area locations 4 and 10) because the top of the bottom

flange of the exterior of the fascia was obstructed by a sign. It was assumed that the test

could provide insight regarding the performance of UWS based on a digital image

0.00

200.00

400.00

600.00

800.00

1,000.00

1,200.00

1,400.00

0

10

20

30

40

50

60

Ala

bam

a

Ala

ska

Ari

zona

Cal

iforn

ia

Colo

rado

Conn

ecticu

t

Del

aw

are

Flo

rida

Geo

rgia

Idah

o

Illi

no

is

India

na

Iow

a

Kan

sas

Ken

tuc

ky

Mai

ne

Mar

yla

nd

Mas

sach

use

tts

Mic

hig

an

Min

nes

ota

Mis

souri

Mon

tana

Neb

rask

a

New

Ham

psh

ire

New

York

Nort

h D

ako

ta

Ohio

Ore

gon

Pen

nsy

lvan

ia

Rho

de

Isla

nd

South

Dak

ota

Texas

Uta

h

Ver

mon

t

Wash

ingto

n

West

Vir

gin

ia

Wis

co

nsi

n

Gal

lon

s per

Lan

e M

ile

Tons

per

Lan

e M

ile

Agency

Corrosive Solids Corrosive Brines

104

processing assessment of particle sizes and spatial densities of rust particles that adhered

to the tape. Appendix G.1 shows cropped digital images of each tape sample. Each bin of

rust particle sizes that were categorized (e.g., 0 to 1/32 inch, 1/32 to 1/16 inch, 1/16 to 1/8

inch, 1/8 to 1/4 inch, 1/4 to 1/2 inch, 1/2 to 1 inch, 1 to 2 inches, and 2 to 4 inches) was

used to assess the percent area of rust particles in each size range that occupied the tape

sample. Refer to Appendix G.2 for data tables of tape test results and Appendix G.3 for

tape test results standard deviations. It was assumed that assessing each percent area of rust

particles within each size range would provide insight into which size range correlated best

with determining performance of UWS. This assumption was made based on the idea that

if large rust particles adhered to the tape when peeled off of the steel that the rust patina

may be performing insufficiently. It was found that assessing the percent area of rust

particles greater than or equal to an 1/8 inch provided the most information relative to UWS

performance. Bar graphs of other rust particle size thresholds that were assessed (i.e.,

average overall percentage of rust particles (density), rust particles greater than or equal to

a 1/4 inch, and rust particles greater than or equal to a 1/2 inch) for the clusters, field

bridges, and standard sample area locations are shown in Appendix G.4. Considering rust

particle sizes less than a 1/8 inch made it difficult to assess differences between results

relative to UWS performance. The 1/8 inch threshold used for the tape test results is also

what was used in Phase 2 of this research project as discussed in Section 2.2.2.

4.4.1.1 Cluster Performance Based on Tape Test Results

The percent area of rust particles greater than or equal to an 1/8 inch from the tape

tests were averaged across each of the three field bridges within a cluster in order to

compare the relative performance of the seven clusters. The average percent area of rust

particles greater than or equal to an 1/8 inch for each cluster along with standard deviations

105

are shown in Figure 4.35. It should be noted that if the standard deviation of a cluster caused

the error bars to include negative values, the range of values was limited to a minimum

value of zero.

Figure 4.35 Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch,

by Cluster

The CO cluster had an average percent area of rust particles greater than or equal

to an 1/8 inch of 0.23%. This was the lowest value between the seven clusters and therefore

was assumed to be the best performing cluster in terms of the average percent area of rust

particles greater than or equal to an 1/8 inch. The CO cluster also had the smallest range in

values according to the standard deviation shown in Figure 4.35. The MN cluster had an

average percent area of rust particles greater than or equal to an 1/8 inch of 11.07%. This

was the greatest value between each of the seven clusters and, therefore was the worst

performing cluster in terms of the tape test results. The remaining five clusters’ average

0.23

8.15

3.41

11.0710.10

7.038.17

0.00

5.00

10.00

15.00

20.00

25.00

CO(good

deicing)

CT(inferior

deicing & coastal)

IA(inferior

deicing)

MN(inferior

deicing)

NC(good & inferior

coastal)

NH(good

deicing & coastal)

OH(good

deicing)

Per

cen

t A

rea

(%)

Cluster

106

percent area of rust particles greater than or equal to an 1/8 inch values from least to greatest

were IA – 3.41%, NH – 7.03%, CT – 8.15%, OH – 8.17%, and NC – 10.10%. The MN,

NC, NH, and OH clusters had the largest ranges in values according to the standard

deviations shown in Figure 4.35. It appears that clusters that had relatively higher average

percent areas of rust particles greater than or equal to an 1/8 inch also had a larger range in

their data sets, and vice versa when looking at the standard deviations shown in Figure

4.35. Bar graphs of the average percent area of rust particles greater than or equal to a 1/4

inch, average percent area of rust particles greater than or equal to a 1/2 inch, and the

average overall percentage of rust particles (density) from the tape tests are shown in

Appendix G.4, Figures G.4.1 – G.4.4.

4.4.1.2 Field Bridge Performance Based on Tape Test Results

The percent area of rust particles greater than or equal to a 1/8 inch from the tape

tests were averaged for each field bridge in order to compare the relative performance of

individual bridges. The average percent area of rust particles greater than or equal to a 1/8

inch along with standard deviations for each field bridge are shown in Figure 4.36. It should

be noted that if the standard deviation of a field bridge caused the error bars to include

negative values, the range of values was limited to a minimum value of zero.

107


by Field Bridge

Each field bridge shown in Figure 4.36 is categorized by color based on the cluster

they belong to in order to make it simpler to compare bridges and clusters. It can be seen

that one field bridge in the NH cluster, two field bridges in the MN cluster, and one field

bridge in the NC cluster had the highest average percent areas of rust particles greater than

or equal to an 1/8 inch (NH 017701460003700 – 13.56%, MN 19811 – 13.93%, MN 62861

– 15.28%, NC 1290058 – 17.40%). These field bridges were the worst performing relative

to the other field bridges in terms of their tape test results. Field bridge NC 1290058 was

found to have a mysterious dark film present on the tops of the bottom flanges at interior

girder locations as mentioned in Section 4.1.5.3. This mysterious dark film was found to

cover most of the tape sample and may be why this field bridge had significantly higher

percent area value. Field bridges from CO had the lowest average percent areas of rust

particles greater than or equal to an 1/8 inch (CO E-16-JW – 0.13%, CO E-16-JX – 0.01%

CO E-16-JZ – 0.56%). These field bridges were the best performing relative to the other

field bridges in terms of their tape test results. The CT, NH, and OH clusters had field

0.13 0.01 0.56

6.82

12.23

5.403.07

5.521.63 2.58

13.9315.28

1.75

11.15

17.40

1.975.58

13.56

5.59

11.56

7.36

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

CO

E-1

6-JW

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NC 1

2900

58

NH 0

1720

1120

0113

00

NH 1

1101

1200

1790

0

NH 0

1770

1460

0037

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Per

cen

t A

rea

(%)

Field Bridge

108

bridges with similar results in terms of the average percent area of rust particles greater

than or equal to an 1/8 inch. It appears that field bridges that had relatively higher average

percent areas of rust particles greater than or equal to an 1/8 inch also had a larger range in

their data sets and vice versa when looking at the standard deviations shown in Figure 4.36.

Bar graphs of the average percent area of rust particles greater than or equal to a 1/4 inch,

average percent area of rust particles greater than or equal to a 1/2 inch, and the average

overall percentage of rust particles (density) for each field bridge are shown in Appendix

G.4, Figures G.4.5 – G.4.8.

4.4.1.3 Standard Sample Area Location Performance Based on Tape Test Results

The relative performance of individual sample area locations was assessed by

averaging the percent area of rust particles greater than or equal to an 1/8 inch from the

tape tests for each standard sample area location across all field bridges except for the CO

bridges because of the different standard sample area locations used for these bridges (refer

to Table 4.2). See Table 3.6 for descriptions of each of the 12 different standard sample

area locations. The average percent area of rust particles greater than or equal to an 1/8

inch along with standard deviations for each standard sample area location are shown in

Figure 4.37. It should be noted that if the standard deviation of a standard sample area

location caused the error bars to include negative values, the range of values was limited

to a minimum value of zero.

109


by Standard Sample Area Location

It was found that sample locations 4, 5, 10, and 11 performed the best relative to

the rest of the locations. When looking at Figure 4.37 these four locations had the lowest

average percent area of rust particles greater than or equal to an 1/8 inch (sample location

4 had 2.56%, sample location 5 had 2.51%, sample location 10 had 2.40%, and sample

location 11 had 2.51%). One aspect that these four sample areas had in common was that

they were all located on the fascia of the exterior girders of each field bridge. Refer to Table

3.6 for the standard sample area location descriptions. The performance of these four

locations may relate to their exposure to environmental conditions such as rain and sunlight

being that they are on the fascia of the exterior girder. It has been reported that TOW plays

an important role in UWS patina formation. Therefore, the fascia of the exterior girder

being easily exposed to rain and sunlight may be a reason for these four locations

performing relatively well.

11.23

8.40

12.83

2.56 2.51

9.22

12.83

7.06

15.22

2.40 2.51

8.14

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

1 2 3 4 5 6 7 8 9 10 11 12

Per

cen

t A

rea

(%)

Standard Sample Area Location ID

110

Furthermore, it was found that sample locations 2, 6, 8, and 12 performed fairly

relative to the rest of the locations when looking at Figure 4.37 (sample location 2 had

8.40%, sample location 6 had 9.22%, sample location 8 had 7.06%, and sample location

12 had 8.14%). Sample locations 6 and 12 were located on the top of the bottom flanges of

the interior of the fascia girders. Sample locations 2 and 8 were located on the webs of the

interior girders. Refer to Table 3.6 for the standard sample area location descriptions. The

performance of these four locations may relate to exposure conditions being that they are

located on the interior portions of the bridge. The interior portions of the bridge are more

susceptible to retaining moisture because they are not exposed to sunlight that can allow

the steel to dry. This means that they may experience a longer TOW. Therefore, these four

interior locations may perform worse relative to the exterior locations on the fascia girder.

Finally, sample locations 1, 3, 7, and 9 performed the worst relative to the rest of

the locations. When looking at Figure 4.37 these three locations had the highest average

percent area of rust particles (sample location 1 had 11.23%, sample location 3 had 12.83%,

sample location 7 had 12.83%, and sample location 9 had 15.22%). One aspect that these

four sample areas had in common was that they were all located on the top of the bottom

flanges of the interior girders of each field bridge. Refer to Table 3.6 for the standard

sample location descriptions. The performance of these four locations may be correlated

with their exposure conditions. Similar to locations 2, 6, 8, and 12, locations 1, 3, 7, and 9

were also located on the interior portions of the bridge. However, locations 1, 3, 7, and 9

were each located on the top of the bottom flanges. The top surfaces of the bottom flanges

are susceptible to retaining moisture and the interior girder locations are more sheltered,

which may be why locations 1, 3, 7, and 9 performed the worst relative to the rest of the

sample area locations.

111

It appears that field bridges that had relatively higher average percent areas of rust

particles greater than or equal to an 1/8 inch also had a larger range in their data sets and

vice versa when looking at the standard deviations shown in Figure 4.37. Bar graphs of the

average percent area of rust particles greater than or equal to a 1/4 inch, average percent

area of rust particles greater than or equal to a 1/2 inch, and the average overall percentage

of rust particles (spatial density) for each standard sample area location are shown in

Appendix G.4, Figures G.4.9 – G.4.12.

4.4.2 Ion Chromatography Results

Ion chromatography tests were performed on rust samples collected from each field

bridge (except for bridges CO E-16-JW, NC 1290058, NH 017701460003700, and NH

11101120017900 as samples from these bridges were not yet processed at the time of this

writing due to the COVID-19 pandemic causing the University of Delaware’s labs to be

shut down during the semester in which this thesis was completed) at each of the sampled

locations. It was the objective to assess the influence of chloride, nitrate, and sulfate

concentrations on the characteristics of the rust samples based on prior knowledge

regarding the effects that these ions have on patina performance. Chloride ions typically

originate from deicing agents and salt spray from the ocean, nitrate ions typically come

from rainwater that originated from organic matter found in soil, and sulfate ions typically

come from air pollutants. From past research discussed in Chapter 2, chloride has been

found to be the most problematic ion affecting rust patina performance of UWS, sulfate

typically negatively impacts UWS performance within the initial stages of patina

formation, and nitrate was found to have minor effects on UWS performance. Refer to

Appendix H.1 for data tables IC analysis results and Appendix H.2 for IC analysis results

standard deviations.

112

Ion concentrations presented in Sections 4.4.2.1, 4.4.2.2, and 4.4.2.3 are relatively

larger than ion concentrations presented in Chapter 2. This difference may be due to what

the results are relative to. The ion concentrations in units of parts per million presented in

Sections 4.4.2.1, 4.4.2.2, and 4.4.2.3 are relative to the number of ions present in 1 kg of

rust. The ion concentrations in units of parts per million presented in Chapter 2 are

unknown what they are relative to because the field studies did not specify this information.

However, it is assumed they are relative to the amount of ions in a liquid solution.

4.4.2.1 Cluster Ion Chromatography Results

The ion chromatography results were used to assess the concentrations of ions

found in rust samples of each of the seven clusters by averaging results across each of the

three field bridges within a cluster. Figure 4.38 shows the results of the averaged chloride,

nitrate, and sulfate concentrations along with standard deviations for each cluster. Chloride

concentrations were typically the highest of the three different ions that were assessed in

each of the clusters except for the NC cluster, which had sulfate as the highest average ion

concentration between the three ions (chloride – 582 ppm, nitrate – 359 ppm, and sulfate –

2,670 ppm). It should be noted that if the standard deviation of a cluster caused the error

bars to include negative values, the range of values was limited to a minimum value of

zero.

113

Figure 4.38 Average Concentration of Chloride, Nitrate, and Sulfate Ions, by Cluster

One aspect of this research project was to assess the effects that the distance of

UWS bridges from the coast had on performance of the rust patina due to salt spray from

the ocean causing atmospheres in these environments to contain relatively higher

concentrations of chloride. The NC cluster was categorized as a coastal cluster with all

three field bridges being within less than 5 miles from the coast. However, the NC cluster

had the lowest average chloride concentration of 583 ppm relative to the other clusters.

This suggests that the effects of this coastal climate are less severe than the effects of

deicing agents in all other Phase 3 locations considered. The CO cluster, which is not

located near a coast and was considered a deicing cluster had the highest ion concentrations

12009

1816

4825

6229

583

1923

3537

1264

377 273 315 361 311 341

3545

9521517 1569

2745

396

2630

0

5000

10000

15000

20000

CO CT IA MN NC NH OH

Co

nce

ntr

atio

n (

pp

m)

Cluster

Average Chloride Average Nitrate Average Sulfate

114

of all three ions that were assessed (chloride – 12,009 ppm, nitrate – 1,264 ppm, and sulfate

– 3,545 ppm) relative to the other clusters. The CT (chloride – 1,816 ppm, nitrate – 377

ppm, and sulfate – 952 ppm) and NH (chloride – 1,923 ppm, nitrate – 311 ppm, and sulfate

– 396 ppm) clusters were categorized as combination deicing and coastal clusters; however,

they both had relatively low concentrations of chloride ions found in rust samples. The IA

(chloride – 4,825 ppm, nitrate – 273 ppm, and sulfate – 1,517 ppm), and MN (chloride –

6,229 ppm, nitrate – 315 ppm, and sulfate – 1,569 ppm), and OH (chloride – 3,537 ppm,

nitrate – 341 ppm, and sulfate – 2,630 ppm) clusters were categorized as deicing clusters.

Aside from the CO cluster, these three clusters had relatively high concentrations of ions

found in rust samples, especially chloride concentrations from the MN cluster.

4.4.2.2 Field Bridge Ion Chromatography Results

The ion chromatography results were averaged across all field bridges to compare

concentrations of chloride, nitrate, and sulfate ions between individual bridges. The

average concentrations of chloride, nitrate, and sulfate ions along with standard deviations

for each field bridge are shown in Figure 4.39, Figure 4.40, and Figure 4.41, respectively.

It should be noted that if the standard deviation of a field bridge caused the error bars to

include negative values, the range of values was limited to a minimum value of zero.

115

Figure 4.39 Average Concentration of Chloride, by Field Bridge

13437

10438

16682658

1052

4300

54094813 4650 4425

9216

563 601

1923

40323343 3236

0

5000

10000

15000

20000

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NH 0

1720

1120

0113

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Co

nce

ntr

atio

n (

pp

m)

Field Bridge

116

Figure 4.40 Average Nitrate Concentration, by Field Bridge

1582

913

393 406329

154

346 330220

456

246312

405311

365305

355

0

500

1000

1500

2000

2500

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NH 0

1720

1120

0113

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Co

nce

ntr

atio

n (

ppm

)

Field Bridge

117

Figure 4.41 Average Sulfate Concentration, by Field Bridge

Each field bridge shown in Figure 4.39, Figure 4.40, and Figure 4.41 is categorized

by color based on the cluster they belong to in order to make it simpler to compare bridges

and clusters. The average chloride concentrations were considerably much higher than the

average nitrate and sulfate concentrations for each field bridge. The CO E-16-JX bridge

had the highest average chloride (13,437 ppm), nitrate (1,582 ppm), and sulfate (5,094

ppm) concentrations as compared to the other field bridges. Bridge MN 62861 had a

relatively high average chloride concentration (9,216 ppm) as compared to the other field

bridges. The NC 190083 and NC 1290057 bridges had the lowest average chloride

concentrations (563 ppm and 601 ppm, respectively) as compared to the other field bridges.

Bridge NC 1290057 had a relatively high average sulfate concentration (4,396 ppm) as

compared to the other field bridges. Bridge NH 017201120011300 had a relatively low

5094

1841

1253

809 776 781

1843 19941634

2192

898 944

4396

396

1893

2781

3216

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NH 0

1720

1120

0113

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Co

nce

ntr

atio

n (

pp

m)

Field Bridge

118

average sulfate concentration (396 ppm) as compared to the other field bridges. The rest of

the field bridges had relatively intermediate average chloride, nitrate, and sulfate

concentrations that were comparable between each other.

4.4.2.3 Standard Sample Area Location Ion Chromatography Results

To compare concentrations of chloride, nitrate, and sulfate ions of individual

sample area locations, the ion chromatography results were averaged across all field

bridges except for the CO bridges because of the different standard sample area locations

used (refer to Table 4.2). See Table 3.6 for a description of each of the 12 different standard

sample area locations. Figure 4.42. shows the average chloride, nitrate, and sulfate ion

concentrations along with standard deviations for each of the 12 standard sample area

locations. It should be noted that if the standard deviation of a standard sample area location

caused the error bars to include negative values, the range of values was limited to a

minimum value of zero.

119

Figure 4.42 Average Concentration of Chloride, Nitrate, and Sulfate Ions, by Standard

Sample Area Location

The concentrations of nitrate found in rust samples across all standard sample area

locations were very similar as can be seen in Figure 4.42. Therefore, nitrate concentrations

were excluded from the proceeding discussion of comparing standard sample area locations

IC results. It was found that sample locations 5 and 11 had the lowest average

concentrations of chloride and sulfate ions relative to the rest of the locations (sample

location 5 had chloride – 1,473 ppm and sulfate – 646 ppm and sample location 11 had

chloride – 1,410 ppm and sulfate 452 ppm). These two sample areas were both located on

the exterior web of the fascia girders for each field bridge. Refer to Table 3.6 for the

standard sample area location descriptions. The low concentration of ions found at these

two sample locations may relate to their exposure to rainwater and vertical orientation.

Rainwater being able to run down the exterior webs may have aided in washing the ions

4755

2287

4364

2363

1473

4768

5356

2906

4879

2390

1410

4847

381 276 399 354 270 347 380 243 413 266 221401

2148

1231

2749

1283

646

2931

2103

1686

2406

964

452

2265

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1 2 3 4 5 6 7 8 9 10 11 12

Co

nce

ntr

atio

n (

pp

m)


Average Chloride Average Nitrate Average Sulfate

120

off of the steel and may relate to these two sample areas having the lowest concentrations

of ions relative to the other sample locations.

Sample locations 1, 3, 6, 7, 9, and 12 from Figure 4.42 had the highest average

chloride and sulfate concentrations relative to the rest of the locations (sample location 1

had chloride – 4,755 ppm and sulfate – 2,148 ppm, sample location 3 had chloride – 4,364

ppm and sulfate – 2,749 ppm, sample location 6 had chloride – 4,768 ppm and sulfate –

2,931 ppm, sample location 7 had chloride – 5,356 ppm and sulfate – 2,103 ppm, sample

location 9 had chloride – 4,879 ppm and sulfate – 2,406 ppm, and sample location 12 had

chloride – 4,847 ppm and sulfate – 2,265 ppm). These six sample areas were all located on

the tops of the bottom flanges at interior portions of each field bridge. Refer to Table 3.6

for the standard sample area location descriptions. The interior location and horizontal

orientation of these sample areas may relate to higher concentration of ions being found in

the rust. The interior portions of bridges have no direct exposure to rainwater or sunlight

to rinse ions and dry off the steel. The horizontal orientation of the flanges makes these

locations susceptible to retaining moisture or debris. Furthermore, the interior portions of

bridges that cross over roadways may be subject to salt spray caused by vehicles driving

under the bridge. Uplift may cause deicing agents and other pollutants to blow up onto the

tops of the bottom flanges of interior portions of bridges and therefore cause higher

concentrations of ions to reside in these locations.

Sample locations 2, 4, 8, and 10 had intermediate average concentrations of

chloride and sulfate ions relative to the other sample locations as can be seen in Figure 4.42

(sample location 2 had chloride – 2,287 ppm and sulfate – 1,231 ppm, sample location 4

had chloride – 2,363 ppm and sulfate – 1,283 ppm, sample location 8 had chloride – 2,906

ppm and sulfate 1,686 ppm, and sample location 10 had chloride – 2,390 ppm and sulfate

– 964 ppm). Sample locations 4 and 10 were located on the tops of the bottom flanges of

121

the exterior portion of the fascia girder similar to locations 5 and 11; however, locations 5

and 11 were on the exterior web of the fascia girder. Refer to Table 3.6 for the sample

location descriptions. Locations 4 and 10 having slightly higher ion concentrations than 5

and 11 may be due to the horizontal orientation of the flanges being susceptible to retaining

moisture or debris. Sample areas 2 and 8 were located on the web of the interior girder and

may be susceptible to “tunnel-like” conditions as well as poor exposure to rainwater and

sunlight resulting in higher concentrations of ions found in the rust, but not as high

concentrations as the interior flange locations (e.g., sample locations 1, 3, 6, 7, 9, and 12)

122

Chapter 5

DATA CORRELATIONS DISCUSSION

5.1 Introduction

Correlations between various data types collected throughout this research project

(e.g., bridge maintenance manual ratings, reported bridge washing practices, reported

deicing agent usage, tape test results, IC analysis results, and UWS bridge condition

ratings) were assessed to understand any data trends. Data trends between bridge

maintenance manual ratings and tape test results, bridge washing practices and tape test

results, bridge washing practices and IC analysis results, deicing agent usage and tape test

results, as well as deicing agent usage and IC analysis results were compared to assess any

cause (maintenance practices and deicing agent use practices) and effect (tape test results

and IC analysis results) relationships. The IC analysis results and tape test results were also

compared to assess if there was a cause (IC analysis results) and effect (tape test results)

relationship. Data trends between UWS bridge condition ratings and tape test results were

compared to assess whether or not there was any agreement between these two methods

used for evaluating bridge performance. Evaluating these trends may provide insight

regarding maintenance practices, environments that are or are not suitable for UWS

bridges, and measures of UWS bridge performance.

The bridge maintenance manuals, bridge washing practices, and deicing agent

usage data received from the Colorado, Connecticut, Iowa, Minnesota, New Hampshire,

and Ohio agencies were compared with the tape test results as well as the IC analysis results

for each corresponding cluster. It should be noted that the data received from each agency

123

was applied to the corresponding cluster within that agency (hence the use of the term

“agency/cluster” in the proceeding sections). There was no bridge maintenance manual,

bridge washing practices, or deicing agent usage data provided by North Carolina,

therefore, correlations of these three data types could not be compared with the tape test

results and IC analysis results from the North Carolina cluster.

5.2 Correlations Between Bridge Maintenance Manual Ratings and Tape Test

Results

The ratings of bridge maintenance manuals that were reviewed were compared with

tape test results because it was assumed that both of these data types may have a

relationship in terms of cause (bridge maintenance manuals) and effect (tape test results)

regarding UWS bridge maintenance practices and performance. The objective and

subjective bridge maintenance manual ratings were summed together to create an overall

bridge maintenance manual rating for each agency in order to make it simpler to compare

with the tape test results. Table 5.1 provides a summary of the overall bridge maintenance

manual ratings for each agency that was considered along with the average percent area of

rust particles greater than or equal to an 1/8 inch from each corresponding cluster. Figure

5.1 shows a scatter plot of the overall bridge maintenance manual ratings versus the average

percent area of rust particles greater than or equal to an 1/8 inch for each agency/cluster.

124

Table 5.1 Summary of Overall Bridge Maintenance Manual Ratings and Average Percent

Area of Rust Particles Greater than or Equal to an 1/8 inch for Each Agency/Cluster

Figure 5.1 Scatter Plot of Overall Bridge Maintenance Manual Ratings Versus Average

Percent Area of Rust Particles Greater than or Equal to an 1/8 inch for Each

Agency/Cluster

It was assumed that as the overall bridge maintenance manual rating decreased, the

average percent area of rust particles greater than or equal to an 1/8 inch would increase

Agency/Cluster

Objective

Manual

Rating

Subjective

Manual

Rating

Overall

Manual

Rating

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

Colorado 2 3 5 0.23

Connecticut 2 2 4 8.15

Iowa 2 3 5 3.41

Minnesota 2 2 4 11.07

New Hampshire 1 1 2 7.03

Ohio 2 3 5 8.17

R² = 0.1436

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0 2 4 6

Av

erag

e P

erce

nt A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)

Overall Manual Rating

125

because of the assumption that a higher percent area of rust particles greater than or equal

to an 1/8 inch coincided with poorer performance of the rust patina, which may be a result

of poor maintenance practices (e.g., lower overall bridge maintenance manual rating).

Although Figure 5.1 shows a decreasing linear trendline for the data, there is little to no

correlation between the average percent area of rust particles greater than or equal to an

1/8 inch and the overall bridge maintenance manual ratings based on the low R2 value of

0.1436. Therefore, while these results suggest that there may be no correlation between

these two data types, there may be a weak correlation that is obscured by the influence of

other variables, or more data points may be required to assess this correlation.

5.3 Correlations Between Bridge Washing Practices and Tape Test Results

Reported bridge washing practices from the maintenance survey and results from

the tape tests were compared to assess if these two data types may have a cause (bridge

washing practices) and effect (tape test results) relationship regarding UWS bridge

maintenance practices and performance. In order to compare bridge washing practices with

the tape test results, a rating system was developed to summarize and quantify bridge

washing practices of each agency. The responses to the bridge washing survey regarding

approximate percentage of UWS bridges washed, frequency of washing, and girder

washing practices were determined to be the most useful for rating and comparing the

overall bridge washing practices of each agency because they supply information about

how likely and how often the girders of UWS bridges are washed. Refer to section 4.2.2

for findings related to these bridge washing practices. In terms of approximate percentage

of UWS bridges washed, agencies that answered with >50% were given a rating of 3, 10 –

50% were given a rating of 2, <10% were given a rating of 1, and 0% were given a rating

of 0. In terms of frequency of washing, agencies that answered with more than once per

126

year were given a rating of 4, annually were given a rating of 3, every 2 years were given

a rating of 2, less frequently were given a rating of 1, and no indication of frequency of

washing were given a rating of 0. In terms of girder washing practices, agencies that

answered with always wash girders were given a rating of 3, at least half of the time were

given a rating of 2, less than half of the time were given a rating of 1, and never washing

the girders or no indication of washing the girders were given a rating of 0. Next, the rating

from each bridge washing practices category was summed in order to give each agency an

overall bridge washing practice rating. While it is not expected that there is necessarily a

linear relationship between this rating system and the effectiveness of bridge washing, this

provides a simple numerical scale for assessing possible influences of washing.

Table 5.2 shows bridge washing practice ratings given to each agency that was

considered along with the average percent area of rust particles greater than or equal to an

1/8 inch from each corresponding cluster’s tape test results. Figure 5.2 shows a scatter plot

of bridge washing practice ratings versus the average percent area of rust particles greater

than or equal to an 1/8 inch for each agency/cluster.

Table 5.2 Summary of Bridge Washing Practice Ratings and Average Percent Area of

Rust Particles Greater than or Equal to an 1/8 inch for Each Agency/Cluster

Agency/Cluster

Percentage

Washed

(%)

Frequency Wash GirdersWashing

Rating

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

Colorado 0 — — 0 0.23

Connecticut 0 — — 0 8.15

Iowa <10 Less frequently Rarely (less than half of the time) 3 3.41

Minnesota >50 Annually Typically (at least half of the time) 8 11.07

New Hampshire 10-50 Every 2 years Rarely (less than half of the time) 5 7.03

Ohio 10-50 Annually No (never) 5 8.17

127

Figure 5.2 Scatter Plot of Bridge Washing Practice Ratings Versus Average Percent Area

of Rust Particles Greater than or Equal to an 1/8 inch for Each Agency/Cluster

It was assumed that as the bridge washing practice rating decreased, the average

percent area of rust particles greater than or equal to an 1/8 inch would increase because of

the assumption that a higher percent area of rust particles greater than or equal to an 1/8

inch coincided with poorer performance of the rust patina, which may be a result of poor

bridge washing practices (e.g., lower bridge washing practice rating). Figure 5.2 shows the

opposite of this assumption by the increasing linear trendline for the data. Furthermore,

there is a slight correlation between the area of rust particles and the bridge washing

practices’ ratings based on the low R2 value of 0.4634, however, as mentioned, it is

opposite to the trend one might expect. Overall, there may be no correlation between these

two data types, there may be a weak correlation that is obscured by the influence of other

variables, or more data points may be required to assess this correlation.

R² = 0.4634

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0 2 4 6 8 10

Av

erag

e P

erce

nt A

rea o

f R

ust

P

art

icle

s ≥

1/8

" (%

)

Washing Rating

128

5.4 Correlations Between Bridge Washing Practices and IC Analysis Results

Reported bridge washing practices from the maintenance survey and results from the

IC analyses were compared to assess if these two data types may have a cause (bridge

washing practices) and effect (IC analysis results) relationship regarding UWS bridge

maintenance practices and ion concentrations on the rust patinas. Table 5.3 shows bridge

washing practice ratings (refer to Section 5.3 for an explanation of the bridge washing

practice rating system) given to each agency that was considered along with the average

concentrations of chloride, nitrate, and sulfate ions from each corresponding cluster’s IC

analyses. Figure 5.3, Figure 5.4, and Figure 5.5 show scatter plots of bridge washing

practice ratings versus the average chloride concentrations, nitrate concentrations, and

sulfate concentrations, respectively for each agency/cluster.

Table 5.3 Summary of Bridge Washing Practice Ratings and Average Chloride, Nitrate,

and Sulfate Concentrations for Each Agency/Cluster

Agency/Cluster

Percentage

Washed

(%)

Frequency Wash GirdersWashing

Rating

Average

Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulfate

(ppm)

Colorado 0 — — 0 12009 1264 3545

Connecticut 0 — — 0 1816 377 952

Iowa <10 Less frequently Rarely (less than half of the time) 3 4825 273 1517

Minnesota >50 Annually Typically (at least half of the time) 8 6229 315 1569

New Hampshire 10-50 Every 2 years Rarely (less than half of the time) 5 1923 311 396

Ohio 10-50 Annually No (never) 5 3537 341 2630

129

Figure 5.3 Scatter Plot of Bridge Washing Practice Ratings Versus Average Chloride

Concentrations for Each Agency/Cluster

Figure 5.4 Scatter Plot of Average Bridge Washing Practice Ratings Versus Nitrate


R² = 0.0593

0

2000

4000

6000

8000

10000

12000

14000

0 2 4 6 8 10

Av

erag

e C

hlo

ride

(ppm

)

Washing Rating

R² = 0.3302

0

200

400

600

800

1000

1200

1400

0 2 4 6 8 10

Aver

age

Nit

rate

(p

pm

)

Washing Rating

130

Figure 5.5 Scatter Plot of Bridge Washing Practice Ratings Versus Average Sulfate


It was assumed that as the bridge washing practice rating increased, the average ion

concentration would decrease because of the assumption that better bridge washing

practices would result in lower amounts of ions present in the rust patina after being washed

away. Figure 5.3, Figure 5.4, and Figure 5.5 each agree with this assumption exhibited by

the decreasing linear trendlines. However, there is little to no correlation between the

average concentration of chloride, nitrate, and sulfate ions and the bridge washing practice

ratings based on the low R2 values of 0.0593, 0.3302, 0.0739; respectively. The MN agency

was assigned a significantly higher washing rating as compared to the other agencies. The

bridge washing practices rating system may be too heavily biased towards higher ratings.

Overall, there may be no correlation between these two data types, there may be a weak

correlation that is obscured by the influence of other variables, or more data points may be

required to assess this correlation.

R² = 0.0739

0

500

1000

1500

2000

2500

3000

3500

4000

0 2 4 6 8 10

Av

erag

e S

ulf

ate

(ppm

)

Washing Rating

131

5.5 Correlations Between Deicing Agent Usage and Tape Test Results

Reported deicing agent usage values from the deicing agent survey and results from

the tape tests were compared to assess if these two data types may have a cause (deicing

agent usage) and effect (tape test results) relationship regarding environmental conditions

of UWS bridges and performance. Table 5.4 shows a summary of the deicing agent usage

values in terms of corrosive solids per lane mile and corrosive brines per lane mile for each

agency that was considered along with the average percent area of rust particles greater

than or equal to an 1/8 inch from each corresponding cluster. Figure 5.6 and Figure 5.7

show scatter plots of the corrosive solids usages and corrosive brines usages, respectively

versus the average percent area of rust particles greater than or equal to an 1/8 inch for each

agency/cluster.

Table 5.4 Summary of Deicing Agent Usage and Average Percent Area of Rust Particles

Greater than or Equal to an 1/8 inch for Each Agency/Cluster

Agency/Cluster

Corrosive

Solids

(tons/lane

mile)

Corrosive

Brines

(gal./lane

mile)

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

Colorado 7.53 498.73 0.23

Connecticut 20.37 141.13 8.15

Iowa 5.71 1,167.35 3.41

Minnesota 8.10 151.19 11.07

New Hampshire 24.69 41.21 7.03

Ohio 17.44 247.59 8.17

132

Figure 5.6 Scatter Plot of Corrosive Solids’ Usages Versus Average Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch for Each Agency/Cluster

Figure 5.7 Scatter Plot of Corrosive Brines’ Usages Versus Average Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch for Each Agency/Cluster

It was assumed that as both of the corrosive solids and corrosive brines usages

increased, the average percent area of rust particles greater than or equal to an 1/8 inch

R² = 0.1592

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)

Corrosive Solids (tons/lane mile)

R² = 0.3829

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0.00 200.00 400.00 600.00 800.00 1,000.00 1,200.00 1,400.00

Av

era

ge P

erc

en

t A

rea o

f R

ust

P

art

icle

s ≥

1/8

" (%

)

Corrosive Brines (gal./lane mile)

133

would also increase because of the assumption that a higher percent area of rust particles

greater than or equal to an 1/8 inch coincided with poorer performance of the rust patina,

which may be a result of greater deicing agent usages. Figure 5.6 shows an increasing linear

trendline for the data, which matches these assumptions; however, the correlation was low

(R2 value of 0.1592). Figure 5.7 shows a decreasing linear trendline for the data, which is

the opposite of the previously mentioned assumptions. Furthermore, there is little

correlation between the average percent area of rust particles greater than or equal to an

1/8 inch and the corrosive brines’ usages based on the low R2 value of 0.3829. It should be

noted that the IA agency had significantly higher corrosive brines usage rates. Overall,

there may be no correlations between deicing agent usages and tape test results based on

inconsistencies between assumptions and results when considering the corrosive brines

usage rates and tape test results, an insignificant R2 value when considering corrosive solids

usage rates and tape test results, a weak correlation that is obscured by the influence of

other variables, or more data may be required to assess these correlations.

5.6 Correlations Between Deicing Agent Usage and IC Analysis Results

Reported deicing agent usage values from the deicing agent survey and average

chloride concentration results from the IC analyses (only the chloride concentrations were

considered because this is the only ion from the IC analyses that is also contained in deicing

agents) were compared to assess if these two data types may have a cause (deicing agent

usage) and effect (chloride concentrations) relationship regarding environmental

conditions of UWS bridges and chloride concentrations of the rust patinas. Table 5.5 shows

a summary of the deicing agent usage values in terms of corrosive solids per lane mile and

corrosive brines per lane mile for each agency that was considered along with the average

chloride concentrations from each corresponding cluster. Figure 5.8 and Figure 5.9 shows

134

a scatter plot of the corrosive solids’ usages and corrosive brines’ usages, respectively

versus the average chloride concentrations for each agency/cluster.

Table 5.5 Summary of Deicing Agent Usage and Average Chloride Concentrations for

Each Agency/Cluster

Figure 5.8 Scatter Plot of Corrosive Solids’ Usages Versus Average Chloride


Agency/Cluster

Corrosive

Solids

(tons/lane

mile)

Corrosive

Brines

(gal./lane

mile)

Average

Chloride

(ppm)

Colorado 7.53 498.73 12009

Connecticut 20.37 141.13 1816

Iowa 5.71 1,167.35 4825

Minnesota 8.10 151.19 6229

New Hampshire 24.69 41.21 1923

Ohio 17.44 247.59 3537

R² = 0.5292

0

2000

4000

6000

8000

10000

12000

14000

0.00 5.00 10.00 15.00 20.00 25.00 30.00

Av

erag

e C

hlo

ride

(ppm

)

Corrosive Solids (tons/lane mile)

135

Figure 5.9 Scatter Plot of Corrosive Brines’ Usages Versus Average Chloride


It was assumed that as both of the corrosive solids and corrosive brines usages

increased, the average chloride concentration would also increase because of the

assumption that a higher concentration of chloride ions coincided with greater deicing

agent usages. Figure 5.8 shows a decreasing linear trendline for the data, which is opposite

of these assumptions. There is a slight correlation between the average chloride

concentrations and the corrosive solids’ usages based on the R2 value of 0.5292. Figure 5.9

shows an increasing linear trendline for the data, which matches the previously mentioned

assumptions; however, there is little to no correlation between the average chloride

concentrations and the corrosive brines’ usages based on the low R2 value of 0.0918. It

should be noted that the IA agency had significantly higher corrosive brines usage rates

than the other agencies. Furthermore, the CO cluster had significantly higher average

chloride concentrations than the other clusters. Overall, there may be no correlations

between deicing agent usages and chloride concentrations based on inconsistencies

R² = 0.0918

0

2000

4000

6000

8000

10000

12000

14000

0.00 200.00 400.00 600.00 800.00 1,000.00 1,200.00 1,400.00

Av

erag

e C

hlo

ride

(ppm

)

Corrosive Brines (gal./lane mile)

136

between assumptions and results in terms of corrosive solids usage rates and average

chloride concentrations, an insignificant R2 value in terms of corrosive brines usage rates

and average chloride concentrations, a weak correlation that is obscured by the influence

of other variables, or more data may be required to assess these correlations.

5.7 Correlations Between IC Analysis Results and Tape Test Results

The IC analysis and tape test results were compared to assess data correlations

relevant to UWS bridge performance. It was assumed that higher concentrations of ions

would coincide with poorer performance of rust patinas based on prior knowledge

regarding the effects that chloride, nitrate, and sulfate ions have on patina performance.

Chloride ions typically originate from deicing agents and salt spray from the ocean, nitrate

ions typically come from rainwater that originated from organic matter found in soil, and

sulfate ions typically come from air pollutants. From past research discussed in Chapter 2,

chloride has been found to be the most problematic ion affecting rust patina performance

of UWS, sulfate typically negatively impacts UWS performance within the initial stages

of patina formation, and nitrate was found to have minor effects on UWS performance.

Similarly, tape test results with higher average percent area of rust particles greater

than or equal to an 1/8 inch were also assumed to coincide with poorer performance of the

rust patina. Therefore, each cluster’s IC analysis results were compared with each cluster’s

tape test results to evaluate trends of UWS bridge performance between clusters.

Furthermore, each field bridge’s IC analysis results were compared with each field bridge’s

tape test results to evaluate any trends in UWS performance between individual bridges.

The IC analysis results and tape test results were also compared for each standard sample

area location to evaluate any trends in UWS performance between different locations of

rust patinas on girders. It should be noted that only available data between both the IC

137

analyses and tape tests were compared. Refer to Section 4.4.2 for IC analysis data that was

not available to be included in the data set and, therefore was not compared with the tape

test data.

5.7.1 Correlations Between Cluster IC Analysis Results and Tape Test Results

The average concentrations of chloride, nitrate, and sulfate ions from each cluster’s

IC analysis results were compared with the average percent area of rust particles greater

than or equal to an 1/8 inch from each cluster’s tape test results to assess if there was a

cause (ion concentrations) and effect (tape test results) relationship regarding UWS bridge

performance between clusters. Table 5.6 shows a summary of the average chloride, nitrate,

and sulfate concentrations along with the average percent area of rust particles greater than

or equal to an 1/8 inch of each cluster. Figure 5.10, Figure 5.11, and Figure 5.12 show

scatter plots of the average chloride concentrations, nitrate concentrations, and sulfate

concentrations, respectively versus the average percent area of rust particles greater than

or equal to an 1/8 inch for each cluster.

138

Table 5.6 Summary of Average Chloride, Nitrate, and Sulfate Concentrations and

Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch of Each

Cluster

Figure 5.10 Scatter Plot of Average Chloride Concentration Versus Average Percent Area

of Rust Particles Greater than or Equal to an 1/8 inch of Each Cluster

Cluster

Average

Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulphate

(ppm)

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

CO 12009 1264 3545 0.30

CT 1816 377 952 7.81

IA 4825 273 1517 3.54

MN 6229 315 1569 11.16

NC 583 361 2745 6.64

NH 1923 311 396 1.97

OH 3537 341 2630 8.17

R² = 0.1448

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0 2000 4000 6000 8000 10000 12000 14000

Av

erag

e P

erce

nt A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)

Average Chloride (ppm)

139

Figure 5.11 Scatter Plot of Average Nitrate Concentration Versus Average Percent Area


Figure 5.12 Scatter Plot of Average Sulfate Concentration Versus Average Percent Area


R² = 0.3333

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0 200 400 600 800 1000 1200 1400

Av

erag

e P

erce

nt A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)

Average Nitrate (ppm)

R² = 0.0203

0.00

2.00

4.00

6.00

8.00

10.00

12.00

0 1000 2000 3000 4000

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)

Average Sulfate (ppm)

140

It was assumed that higher concentrations of ions would coincide with higher

average percent area of rust particles greater than or equal to an 1/8 inch and may indicate

poorer performance of rust patinas. However, Figure 5.10, Figure 5.11, and Figure 5.12

each show decreasing linear trendlines, which is opposite of this assumption. There is also

little to no correlation between the average percent area of rust and average concentrations

of ions based on the low R2 values of 0.1448, 0.3333, and 0.0203; respectively. It should

be noted that the CO cluster had significantly higher average ion concentrations than the

other clusters. Overall, there may be no correlations between tape test results and IC

analysis results in terms of cluster bridges, there may be a weak correlation that is obscured

by the influence of other variables, or more data may be required to assess these

correlations.

5.7.2 Correlations Between Field Bridge IC Analysis Results and Tape Test

Results

The average concentrations of chloride, nitrate, and sulfate ions from each field

bridge’s IC analysis results were compared with the average percent area of rust particles

greater than or equal to an 1/8 inch from each field bridge’s tape test results to assess if

there was a cause (ion concentrations) and effect (tape test results) relationship regarding

UWS bridge performance between field bridges. Table 5.7 shows a summary of the average

chloride, nitrate, and sulfate concentrations along with the average percent area of rust

particles greater than or equal to an 1/8 inch of each field bridge. Figure 5.13, Figure 5.14,

and Figure 5.15 show scatter plots of the average chloride concentrations, nitrate

concentrations, and sulfate concentrations, respectively versus the average percent area of

rust particles greater than or equal to an 1/8 inch for each field bridge.

141


Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch of Each Field

Bridge

Field BridgeAverage Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulfate

(ppm)

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

CO E-16-JX 13437 1582 5094 0.01

CO E-16-JZ 10438 913 1841 0.63

CT 3830 1668 393 1253 6.29

CT 4382 2658 406 809 12.15

CT 5796 1052 329 776 4.71

IA 004111 4300 154 781 3.07

IA 041331 5409 346 1843 5.98

IA 042711 4813 330 1994 1.61

MN 04019 4650 220 1634 1.95

MN 19811 4425 456 2192 13.93

MN 62861 9216 246 898 15.28

NC 190083 601 405 4396 1.72

NC 1290057 563 312 944 11.15

NH 017201120011300 1923 311 396 1.97

OH 7700105 93 8 44 5.59

OH 7701977 97 9 77 11.56

OH 7701993 115 10 101 7.36

142


of Rust Particles Greater than or Equal to an 1/8 inch of Each Field Bridge



R² = 0.0521

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 5000 10000 15000

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)


R² = 0.1507

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 500 1000 1500 2000

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)


143



Figure 5.13, Figure 5.14, and Figure 5.15 each have decreasing linear trendlines

which portray opposite results of what was assumed in terms of correlations between the

average ion concentrations and average percent area of rust particles greater than or equal

to an 1/8 inch. It was assumed that higher concentrations of ions would coincide with higher

percent areas of rust particles and may indicate poorer performance of rust patinas. Figure

5.13, Figure 5.14, and Figure 5.15 also show little to no correlation in the data based on the

low R2 values of 0.0483, 0.0642, and 0.0001; respectively. It should be noted that bridges

CO E-16-JX and CO E-16-JZ had significantly higher average ion concentrations than the

other field bridges. Overall, there may be no correlations between tape test results and IC

analysis results in terms of individual field bridges, there may be a weak correlation that is

obscured by the influence of other variables, or more data may be required to assess these

correlations.

R² = 0.1807

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

0 1000 2000 3000 4000 5000 6000

Aver

age

Per

cen

t A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)


144

5.7.3 Correlations Between Sample Location IC Analysis Results and Tape Test

Results

The average concentrations of chloride, nitrate, and sulfate ions and the average

percent area of rust particles greater than or equal to an 1/8 inch were compared for each

standard sample area to assess if there was a cause (ion concentrations) and effect (tape test

results) relationship regarding UWS bridge performance between different locations of rust

patinas on girders. Table 5.8 shows a summary of the average chloride, nitrate, and sulfate

concentrations along with the average percent area of rust particles greater than or equal to

an 1/8 inch of each standard sample area location. Figure 5.16, Figure 5.17, and Figure

5.18 show scatter plots of the average chloride concentrations, nitrate concentrations, and

sulfate concentrations, respectively versus the average percent area of rust particles greater

than or equal to an 1/8 inch for each standard sample area location.

145


Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch of Each

Standard Sample Area Location

Standard

Sample

Area

Location

Average

Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulfate

(ppm)

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

1 4755 381 2148 10.88

2 2287 276 1231 8.45

3 4364 399 2749 12.50

4 2363 354 1283 2.85

5 1473 270 646 2.47

6 4768 347 2931 6.93

7 5356 380 2103 11.08

8 2906 243 1686 5.67

9 4879 413 2406 11.54

10 2390 266 964 2.22

11 1410 221 452 2.14

12 4847 401 2265 7.98

146


of Rust Particles Greater than or Equal to an 1/8 inch of Each Standard Sample Area

Location



Location

R² = 0.6871

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 1000 2000 3000 4000 5000 6000

Av

erag

e P

erce

nt A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)


R² = 0.5731

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 100 200 300 400 500

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)


147



Location

The R2 values of 0.6871, 0.5731, and 0.6285 of Figure 5.16, Figure 5.17, and Figure

5.18; respectively each showcase relatively strong correlations between average ion

concentrations and average percent area of rust particles greater than or equal to an 1/8 inch

in terms of standard sample area location as compared to the other data correlations that

were assessed in this chapter. The increasing trendlines shown in each scatter plot also

agree with the assumption that higher concentrations of ions coincide with higher percent

areas of rust particles greater than or equal to an 1/8 inch and may indicate poorer

performance of rust patinas. More data related to average percent areas of rust particles and

average ion concentrations of ions should be assessed to draw conclusions regarding UWS

performance in terms of standard sample area location.

R² = 0.6285

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 1000 2000 3000 4000

Aver

age

Per

cen

t A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)


148

5.8 Correlations Between Tape Test Results and Condition Ratings of Field

Bridges

Correlations between the average percent area of rust particles greater than or equal

to an 1/8 inch and reported condition ratings (SCR from the NBI database and CS of girders

from inspection reports) of each field bridge were assessed. It was assumed that assessing

data trends between UWS bridge condition ratings and tape test results would validate

whether or not there was agreement between these methods used for evaluating UWS

bridge performance. The CS rating of the girders for each field bridge was normalized by

using a weighted girder condition state (WGCS) rating based on the formula WGCS =

CS1/100*1 + CS2/100*2 + CS3/100*3 + CS4/100*4. A WGCS rating of 1.00

corresponded with the best rating while a WGCS rating of 4.00 corresponded with the

worst rating. Table 5.9 shows a summary of the SCR, WGCS ratings, and average percent

area of rust particles greater than or equal to an 1/8 inch for each field bridge that was

evaluated. Figure 5.19 shows a scatter plot of the SCR versus the average percent area of

rust particles greater than or equal to an 1/8 inch for each field bridge.

149

Table 5.9 Summary of Average Percent Area of Rust Particles Greater than or Equal to

an 1/8 inch, SCR, and Weighted Girder CS Ratings for Each Field Bridge

Field Bridge SCRWGCS

Rating

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

CO E-16-JW 8 1.32 0.13

CO E-16-JX 8 1.04 0.01

CO E-16-JZ 7 1.00 0.56

CT 3830 6 1.12 6.82

CT 4382 6 2.01 12.23

CT 5796 7 1.00 5.40

IA 004111 8 1.00 3.07

IA 041331 7 1.00 5.52

IA 042711 8 1.00 1.63

MN 04019 5 1.08 2.58

MN 19811 7 1.28 13.93

MN 62861 6 1.50 15.28

NC 190083 5 1.00 1.75

NC 1290057 8 1.00 11.15

NC 1290058 8 1.00 17.40

NH 017201120011300 6 1.05 1.97

NH 11101120017900 8 2.00 5.58

NH 017701460003700 8 1.00 13.56

OH 7700105 6 1.00 5.59

OH 7701977 8 1.00 11.56

OH 7701993 8 1.00 7.36

150

Figure 5.19 Scatter Plot of SCR Versus Average Percent Area of Rust Particles Greater

than or Equal to an 1/8 inch of Each Field Bridge

It was assumed that as the SCR decreased, the average percent area of rust particles

greater than or equal to an 1/8 inch would increase because of the assumption that a higher

average percent area of rust particles greater than or equal to an 1/8 inch coincided with

poorer UWS performance. Figure 5.19 shows the opposite of this assumption with an

increasing linear trendline; however, there is little to no correlation between the tape test

results and SCR when looking at the very low R2 value of 0.0145. The reason for little to

no correlation between the tape test results and reported SCR may be due to the fact that

the SCR is a rating of the entire bridge’s superstructure, whereas the tape test evaluates

performance of the girders’ rust patina at specific locations. So, a lower SCR rating due to

joint or bearing issues may not be representative of UWS performance. These

inconsistencies may be why there do not appear to be correlations between the tape test

results and SCR. Another reason for little to no correlation between the tape test results

and reported SCR may be due to the subjective nature of inspecting and rating bridges.

R² = 0.0145

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

0 2 4 6 8 10

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)

SCR

151

Different bridge inspectors may have different perspectives regarding the conditions of

bridges. The processes and criteria for inspecting bridges may also vary between states.

To attempt to eliminate ambiguity of the reported SCR of each field bridge while

assessing correlations between reported UWS bridge performance and the tape test results,

the WGCS ratings from inspection reports were used. When looking at Figure 5.20, which

shows a scatter plot of SCR versus the WGCS rating of each field bridge it can be seen that

there is little to no correlation between the SCR and WGCS ratings based on the

significantly low R2 value of 0.0092. Therefore, using the WGCS rating to assess

correlations between UWS bridge condition ratings and tape test results may provide better

agreements between these two methods used for evaluating UWS bridge performance.

Figure 5.21 shows a scatter plot of the WGCS rating versus the average percent area of rust

particles greater than or equal to an 1/8 inch of each field bridge.

Figure 5.20 Scatter Plot of SCR Versus WGCS Rating of Each Field Bridge

R² = 0.0092

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0 2 4 6 8

WG

CS

Rat

ing

SCR

152

Figure 5.21 Scatter Plot of WGCS Rating Versus Average Percent Area of Rust Particles

Greater than or Equal to an 1/8 inch of Each Field Bridge

It was assumed that as the WGCS rating decreased, the average percent area of rust

particles greater than or equal to an 1/8 inch would also decrease because of the assumption

that a lower average percent area of rust particles greater than or equal to an 1/8 inch

coincided with better UWS performance. Figure 5.21 shows an agreement with this

assumption based on the decreasing linear trendline; however, there is little to no

correlation between the tape test results and WGCS rating when looking at the low R2 value

of 0.049. The reason for little to no correlation between the tape test results and WGCS

ratings may also be due to the subjective nature of inspecting and rating bridges as

mentioned previously. Furthermore, the girder CS rating is a rating of the entire length of

all the girders for a bridge, whereas the tape test results were obtained from 12 small areas

on just two girders. So, a higher WGCS rating due to corrosion issues at some locations on

the girders may not be representative of UWS performance indicated by tape test results

from samples taken at other locations along the girders. These inconsistencies may be why

R² = 0.049

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

1.002.003.004.00

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)

WGCS Rating

153

correlations between the tape test results and WGCS ratings did not showcase a strong

agreement. Lastly, most of the bridges had a WGCS of 1.00 (a somewhat default rating)

and these bridges exhibited a wide range of average percent area of rust particles greater

than or equal to an 1/8 inch; if these bridges are removed from the dataset, the R2 value in

Figure # increases to 0.2158.

5.9 Summary of Correlations

When looking at all preliminary correlations that were evaluated in Chapter 5, the

weakest correlations were the bridge maintenance manual ratings versus tape test results

(R2 value of 0.1436), bridge washing practices versus IC analysis results (R2 values of

0.0593, 0.3302, and 0.0739 for chloride, nitrate, and sulfate, respectively), deicing agent

usage rates versus tape test results (R2 values of 0.1592 and 0.3829 for corrosive solids and

corrosive brines, respectively), corrosive brines’ usage rates versus average chloride

concentrations (R2 value of 0.0918), tape test results versus IC analysis results in terms of

cluster (R2 values of 0.1448, 0.3333, and 0.0203 for chloride, nitrate, and sulfate,

respectively), as well as individual field bridges (R2 values of 0.0521, 0.1507, and 0.1807

for chloride, nitrate, and sulfate respectively), and tape test results versus bridge condition

ratings (R2 values of 0.0145 and 0.049 for SCR and WGCS rating, respectively). For each

of these preliminary correlations there may be no correlations; however, there may be a

weak correlation that is obscured by the influence of other variables or more data may be

required to assess each of them.

The strongest preliminary correlation that was evaluated was between the tape test

results and IC analysis results in terms of standard sample area location (R2 values of

0.6871, 0.5731, and 0.6285 for chloride, nitrate, and sulfate, respectively). This correlation

matched assumptions regarding higher concentrations of ions coinciding with higher

154

percent areas of rust particles greater than or equal to an 1/8 inch and may indicate poorer

performance of rust patinas. One reason as to why this correlation was stronger than

correlations between tape test results and IC analysis results in terms of clusters or

individual field bridges may be due to the CO bridges. These bridges had significantly

higher average ion concentrations. Furthermore, the CO bridges’ data were not included in

the average tape test and IC analysis results for the standard sample area locations because

of the different standard sample area locations used for these bridges (refer to Table 4.2).

Data from Phase 2 tape test results and IC analysis results should be included with Phase

3 data and assessed to further evaluate UWS performance in terms of standard sample area

location due to this being the strongest preliminary correlation that was assessed.

Table 5.10 shows a summary of the average chloride, nitrate, and sulfate

concentrations along with the average percent area of rust particles greater than or equal to

an 1/8 inch sorted from highest to lowest of each standard sample area location. Figure

5.22, Figure 5.23, and Figure 5.24 show scatter plots of the average chloride

concentrations, nitrate concentrations, and sulfate concentrations, respectively versus the

average percent area of rust particles greater than or equal to an 1/8 inch for each standard

sample area location, which are listed next to each data point. Looking at the data in this

way will make it easier to evaluate the performance of standard sample area locations based

on the tape test results and their corresponding relations to the IC analysis results.

155


Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch Sorted from

Highest to Lowest of Each Standard Sample Area Location

Figure 5.22 Scatter Plot of Each Standard Sample Area Location Listed for Each

Corresponding Average Chloride Concentration Versus Average Percent Area of Rust

Particles Greater than or Equal to an 1/8 inch

Standard

Sample

Area

Location

Average

Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulfate

(ppm)

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

3 4364 399 2749 12.50

9 4879 413 2406 11.54

7 5356 380 2103 11.08

1 4755 381 2148 10.88

2 2287 276 1231 8.45

12 4847 401 2265 7.98

6 4768 347 2931 6.93

8 2906 243 1686 5.67

4 2363 354 1283 2.85

5 1473 270 646 2.47

10 2390 266 964 2.22

11 1410 221 452 2.14

1

2

3

45

6

7

8

9

1011

12

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 1000 2000 3000 4000 5000 6000

Av

erag

e P

erce

nt A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)


156


Corresponding Average Nitrate Concentration Versus Average Percent Area of Rust



Corresponding Average Sulfate Concentration Versus Average Percent Area of Rust


12

3

45

6

7

8

9

1011

12

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 100 200 300 400 500

Aver

age

Per

cen

t A

rea

of

Ru

st

Par

ticl

es ≥

1/8

" (%

)


12

3

45

6

7

8

9

1011

12

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0 1000 2000 3000 4000

Av

erag

e P

erce

nt A

rea

of

Rust

P

arti

cles

≥ 1

/8"

(%)


157

Other notably strong correlations include bridge washing practices versus tape test

results (R2 value of 0.4634), and corrosive solids’ usage rates versus average concentrations

of chloride (R2 value of 0.5292). Although each of these correlations were relatively strong

in comparison to the other preliminary correlations, both of these scatter plots showed data

trends that were opposite of what was assumed. Overall, there may be no correlations

between these data types, there may be a weak correlation that is obscured by the influence

of other variables, or more data may be required to assess each of them.

158

Chapter 6

CONCLUSIONS

6.1 Summary

Field evaluations of 21 UWS bridges were performed in Phase 3 of this work, which

was the focus of this thesis, in order to obtain additional data relevant to data collected

from field evaluations of 13 UWS bridges conducted in Phase 2 of this research project.

The field evaluations carried out in Phase 3 also provided opportunities to update and refine

LTBPP UWS bridge field evaluation protocols developed in Phase 1 of this research

project. The main data types that were collected from field evaluations and that were

discussed in this thesis include tape test data and IC analysis data of rust samples.

Qualitative condition assessments of each UWS field bridge from Phase 3 were also

performed. Furthermore, work carried out in Phase 3 included conducting a survey

regarding maintenance practices (including a review of bridge maintenance manuals and

bridge washing practices) of UWS bridges and deicing agent usage for each agency in the

U.S.

6.2 Overview of Results

The results from the field evaluations (tape tests and IC analyses), survey (UWS

bridge maintenance practices and deicing agent usages), and UWS bridge condition ratings

(qualitative condition rating, SCR, and weighted girder CS rating) were compared to

identify any preliminary correlations between possible cause and effect relationships as

well as to assess whether or not there was agreement between methods used for evaluating

UWS bridge performance. It was assumed that more extensive bridge maintenance

159

practices and lower deicing agent usage rates (causes) would result in better UWS bridge

condition ratings, a lower average percent area of rust particles greater than or equal to an

1/8 inch from tape test results, and lower average ion concentrations from IC analysis

results. It was intended that evaluating these assumed correlations would further quantify

and understand the performance of UWS bridges in a variety of circumstances and

conditions. Table 6.1, Table 6.2, and Table 6.3 provide summaries of the data correlations

that were presented in Chapter 5 in terms of whether the correlation was positive or

negative, the strength of the correlation indicated by the R2 value, and the corresponding

scatter plot (Figure) of the correlation.

Table 6.1 Summary of Chapter 5 Cause and Effect Correlations

Chloride Nitrate Sulfate

Negative

R2 = 0.1436

Figure 5.1

Positive

R2 = 0.4634

Figure 5.2

Negative

R2 = 0.0593

Figure 5.3

Negative

R2 = 0.3302

Figure 5.4

Negative

R2 = 0.0739

Figure 5.5

Corrosive

Solids

Positive

R2 = 0.1592

Figure 5.6

Negative

R2 = 0.5292

Figure 5.8

Corrosive

Brines

Negative

R2 = 0.3829

Figure 5.7

Positive

R2 = 0.0918

Figure 5.9

Deicing Agent

Usage

IC Analysis ResultsTape Test

Results

Manual Rating

Washing Rating

160

Table 6.2 Summary of Chapter 5 IC Analysis and Tape Test Correlations

ClusterField

Bridge

Standard

Sample Area

Location

Cluster

Negative

R2 = 0.1448

Figure 5.10

Field Bridge

Negative

R2 = 0.0521

Figure 5.13

Standard

Sample Area

Location

Positive

R2 = 0.6871

Figure 5.16

Cluster

Negative

R2 = 0.3333

Figure 5.11

Field Bridge

Negative

R2 = 0.1507

Figure 5.14

Standard

Sample Area

Location

Positive

R2 = 0.5731

Figure 5.17

Cluster

Negative

R2 = 0.0203

Figure 5.12

Field Bridge

Negative

R2 = 0.1807

Figure 5.15

Standard

Sample Area

Location

Positive

R2 = 0.6285

Figure 5.18

Tape Test Results

IC

Analysis

Results

Chloride

Nitrate

Sulfate

161

Table 6.3 Summary of Chapter 5 Methods to Assess UWS Performance Correlations

Table 6.4 provides a summary of each cluster that was evaluated in Phase 3 along

with the cluster’s classification, maintenance and deicing agent usage practices (where

higher numbers in the “maintenance manual rating” and “washing practices rating”

columns indicate better practices; see Section 5.2 and Section 5.3, respectively), as well as

each field bridge that was evaluated in Phase 3 along with measures of performance

(qualitative condition, SCR, WGCS rating, and average percent area of rust particles

greater than or equal to an 1/8 inch from tape test results) and environmental conditions

(average chloride, nitrate and sulfate concentrations from IC analysis results). There was

no reported IC analysis data for bridges CO E-16-JZ, NC 1290058, NH 11101120017900,

and NH 017701460003700 as samples from these bridges were not yet processed at the

time of this writing due to the COVID-19 pandemic causing the University of Delaware’s

labs to be shut down during the semester in which this thesis was completed. There was

also no data obtained from the survey for the NC cluster in terms of bridge maintenance

manuals, bridge washing practices, and deicing agent usages.

SCR WGCS

Tape

Test

Results

Positive

R2 = 0.0145

Figure 5.19

Negative

R2 = 0.049

Figure 5.21

162

Table 6.4 Summary of Cluster and Field Bridge Data Types

ClusterCluster

Classification

Maintenance

Manual

Rating

Washing

Practices

Rating

Corrosive

Solids

(tons/lane

mile)

Corrosive

Brines

(gal./lane

mile)

Field Bridge1 Qualitative

ConditionSCR

WGCS

Rating

Average

Percent Area

of Rust Particles

≥ 1/8" (%)

Average

Chloride

(ppm)

Average

Nitrate

(ppm)

Average

Sulfate

(ppm)

CO E-16-JW good 8 1.32 0.13 13437 1582 5094

CO E-16-JX good 8 1.04 0.01 10438 913 1841

CO E-16-JZ good 7 1.00 0.56 — — —

CT 3830 good 6 1.12 6.82 1668 393 1253

CT 4382 good 6 2.01 12.23 2658 406 809

CT 5796 good 7 1.00 5.40 1052 329 776

IA 004111 poor 8 1.00 3.07 4300 154 781

IA 041331 poor 7 1.00 5.52 5409 346 1843

IA 042711 poor 8 1.00 1.63 4813 330 1994

MN 04019 poor 5 1.08 2.58 4650 220 1634

MN 19811 poor 7 1.28 13.93 4425 456 2192

MN 62861 poor 6 1.50 15.28 9216 246 898

good coastal — — — — NC 190083 good 5 1.00 1.75 601 405 4396

— — — — NC 1290057 good 8 1.00 11.15 563 312 944

— — — — NC 1290058 good 8 1.00 17.40 — — —

NH 017201120011300 good 6 1.05 1.97 1923 311 396

NH 11101120017900 poor 8 2.00 5.58 — — —

NH 017701460003700 poor 8 1.00 13.56 — — —

OH 7700105 poor 6 1.00 5.59 93 8 44

OH 7701977 poor 8 1.00 11.56 97 9 77

OH 7701993 poor 8 1.00 7.36 115 10 101

1 - Green highlighted bridges denote Reference bridges

247.59

inferior coastal

498.73

141.13

1167.35

151.19

41.21

5

0

0

2

7

4

4

5

4

17.44

7.53

20.37

5.71

8.10

24.69

CO

CT

IA

MN

NC

good

deicing

inferior

deicing & coastal

inferior

deicing

inferior

deicing

good

deicing & coastal

5

4

2NH

OHgood

deicing

163

From an overview of the data presented in Table 6.4, it was difficult to form

conclusions based on inconsistencies in terms of assumed cause and effect relationships

and assumed agreements between methods used for evaluating UWS bridge performance.

Simply looking at the performance of reference bridges (highlighted in green in Table 6.4,

whose stated performance in inspection reports coincided with the cluster classifications)

in comparison to the qualitative conditions of the reference bridges, it can be seen that there

is not much agreement between these assessments of UWS bridge performance. All three

of the field bridges in the NC cluster were considered reference bridges and there was no

reference bridge included in the NH cluster because the original reference bridge (NH

017700960015300) was not evaluated due to it being painted as discussed in Section 3.1.5.

The CO, MN, and IA clusters were the only clusters that had agreement between these

parameters. The CT cluster was considered to have an inferior performing UWS reference

bridge in a severe deicing and coastal environment; however, the qualitative condition of

the reference bridge (along with the other two field bridges) was determined to be good.

The NC cluster was split between being classified as a good and inferior performing cluster

in a severe coastal environment; however, all UWS bridges were determined to have good

qualitative conditions. The NH cluster was classified as having a good performing UWS

reference bridge in a severe deicing and coastal environment; yet two out of the three field

bridges that were assessed (one of these two bridges included the replacement bridge, NH

011101120017900) were determined to have poor qualitative conditions. Finally, the OH

cluster was classified as having a good performing UWS reference bridge in a severe

deicing environment, but the reference bridge (along with the other two field bridges) was

determined to be in a poor qualitative condition.

It should be noted that comparisons between cluster classifications and field bridge

data may have inconsistencies due to the fact that cluster classifications consider the entire

164

UWS bridge inventory within the agency associated with that cluster, while field bridge

data considers individual bridges. Furthermore, correlations between other pairs of data

types in Table 6.4 may be obscured by the influence of additional variables, or more data

points may be required to assess these correlations. The data presented in Table 6.4 only

summarizes data collected from Phase 3 of this research project. Compiling all data

collected thus far (Phase 2 and Phase 3) may provide better understandings of preliminary

correlations discussed in this thesis. Doing so may allow clusters to be more easily

compared in terms of environmental conditions, maintenance practices, and UWS

performance in order to draw conclusions regarding environments and maintenance

practices that are and are not suitable for UWS bridges.

6.3 Main Takeaways

One of the main takeaways from this thesis involved the correlation found between

the tape test results and IC analysis results in terms of standard sample area location.

Section 5.7.3 shows the correlations between the average percent area of rust particles

greater than or equal to an 1/8 inch and the average chloride, average nitrate, and average

sulfate concentrations for each standard sample area location. The higher values from the

tape test results tended to correspond with the higher values of each ion that was assessed,

and vice versa.

The discussion of the tape test results and IC analysis results in terms of standard

sample area location in Section 4.4.1.3 and Section 4.4.2.3, respectively were very similar

in regard to the locations that performed poor (in terms of tape test results) and had higher

ion concentrations (in terms of IC analysis results) as well as the locations that performed

good (in terms of tape test results) and had lower ion concentrations (in terms of IC analysis

results). Standard sample area locations 1, 3, 7, and 9 each had higher average percent areas

165

of rust particles greater than or equal to an 1/8 inch and average ion concentrations than

the other locations. On the other hand, standard sample area locations 5 and 11 both had

lower average percent areas of rust particles greater than or equal to an 1/8 inch and average

ion concentrations than the other locations. Standard sample area locations 1, 3, 7, and 9

were each located on the tops of the bottom flanges of interior bridge girders while standard

sample area locations 5 and 11 were both located on the web of the exterior portion of the

fascia girder. Refer to Table 3.6 for descriptions of each standard sample area location.

Figure 6.1 shows a bar graph of the average chloride concentrations and percent area of

rust particles greater than or equal to an 1/8 inch.

Figure 6.1 Graph of Average Chloride Concentrations and Percent Area of Rust Particles

Greater than or Equal to an 1/8 inch

Similarly, the Raman et al. (1988) study discussed in Section 2.3.1.5 mentions that

interior locations of girders were found to perform the worse in terms of the observed flaky

4755

2287

4364

2363

1473

4768

5356

2906

4879

2390

1410

484710.88

8.45

12.50

2.852.47

6.93

11.08

5.67

11.54

2.22 2.14

7.98

0

2

4

6

8

10

12

14

0

1000

2000

3000

4000

5000

6000

1 2 3 4 5 6 7 8 9 10 11 12

Per

cent A

rea

(%)

Co

nce

ntr

atio

n (

ppm

)


Average Chloride(ppm)

Average Percent Areaof Rust Particles ≥ 1/8" (%)

166

or “sheet-type” rust that formed at these locations. This same observation was made in the

qualitative assessment of field bridges from Phase 3 shown in Table 4.1 and discussed in

Section 4.1 where large and small rust flakes were most commonly found at interior

locations. Raman et al. (1988) also mentions that chloride and salt accumulation was higher

at interior locations. Interior portions of the bridge girders are more susceptible to retaining

moisture being that they are not exposed to sunlight that can allow the steel to dry. This

means that they may experience a longer TOW and be susceptible to trapping higher

concentrations of chloride because they are subject to chloride laden moisture from road

spray but not the rinsing action of rainwater. The agreement between the correlation of tape

test results and IC analysis results in terms of standard sample area location presented in

this thesis and the Raman et al. (1988) study are significant in providing justification for

locations of UWS girders on bridges that typically perform worse. This may provide

evidence for the effectiveness of the clear tape adhesion test being used to evaluate the

performance of UWS.

6.4 Future Work

Referring back to Section 6.3, Raman et al. (1988) also found a correlation between

the sizes of observed rust flakes formed on steel surfaces at interior, sheltered locations and

chloride contents in the rust patinas (refer to Section 2.3.1.5). Similarly, Crampton et al.

(2013) notes the size of rust flakes and their correlation to patina performance when using

the NCHRP guidelines (Albrecht and Naeemi, 1984) (refer to Section 2.2.1). Crampton et

al. (2013) found that UWS surfaces with higher concentrations of chloride in the oxide

layer were found to have developed larger, thicker rust flakes in the patina. Similar to these

two studies, future work could include a digital image processing method to assess the size

of rust flakes from sample area photographs collected in Phase 3 field evaluations (e.g.

167

refer back to example sample area photos in Section 3.2.3.2.3). The sizes of rust flakes may

then be compared with tape test results and IC analysis results to evaluate correlations

between the size of rust flakes, chloride contents, and patina performance similar to Raman

et al. (1988) and Crampton et al. (2013). This may provide more insight regarding

evaluating UWS performance in terms of chloride concentrations, the clear tape adhesion

test, and visual inspection of rust patinas.

Future work for this research project also includes assessing the effectiveness of

new inspection methods that were used in Phase 3 to evaluate UWS bridge performance.

One of these new inspection methods includes rust color analyses from photos of rust

patinas collected at each sample area that was evaluated. This will hopefully provide

insight regarding UWS bridge performance similar to the Hara et al. (2006) and Crampton

et al. (2013) studies, which included using the 5 indices developed by the Japan Iron and

Steel Federation (JISF) and Japan Association of Steel Bridge Construction (JASBC) and

NCHRP guidelines (Albrecht and Naeemi, 1984), respectively to evaluate conditions of

UWS.

XRD analyses of rust samples collected from each field bridge will be used to

identify and quantify ratios of different corrosion products of rust patinas. Evaluating

preliminary correlations between this information and other data types, such as bridge

maintenance practices, environmental conditions, and UWS bridge performance measures

may provide insight regarding methods for assessing UWS performance. If correlations

exist, XRD analysis may pose as an effective method for predicting long-term performance

of UWS bridges. Refer to Section 2.2.4 and Section 2.3 regarding some background

information about XRD analysis and corrosion products, respectively.

Another new inspection method involved using dry-film thickness measurements

to measure the thickness of rust patinas at each sample area that was evaluated. The

168

effectiveness of these measurements determining characteristics of the rust patina may be

evaluated as well as used to hopefully predict corrosion rates based on dry-film

measurements being taken again at the same sample area locations at periodic intervals.

Refer to Appendix C for dry-film thickness measurements of field bridges evaluated in

Phase 3.

Finally, ultrasonic thickness measurements will similarly be used to determine

characteristics of the rust patina and hopefully predict corrosion rates by taking

measurements again at the same sample area locations at periodic intervals. Refer to

Appendix C for ultrasonic thickness measurements of field bridges evaluated in Phase 3.

Assessing these UWS bridge inspection methods will further develop field inspection

protocols for future UWS bridge evaluations.

Other future work includes compiling all data collected thus far in the GIS database

that was created in Phase 2. Preliminary correlations from Phase 3 discussed in this thesis

as well as preliminary correlations found in Phase 2 of this research project will then be

further evaluated by a multivariate statistical analysis. This will allow for comparisons to

be made between clusters with similar environmental conditions and maintenance

practices, but differing UWS bridge conditions. Doing so may allow for better

understanding of correlations associated with UWS bridge performance and provide

quantitative guidelines to define environments that cause undesirable rates of corrosion in

UWS bridges. This will in turn provide updates to generic language used in TA 5140.22

regarding unsuitable environments for UWS bridges to be used.

169

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173

Appendix A

CLUSTER BRIDGE CHARACTERISTICS

Table A.1 CO Cluster Bridges

Number CombinationStructure

Number

Corssing

Type

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 12 E-16-JZ Highway 183000 H 19.69 H 9E, 3F L 62.6 L 0.045 L 28 7

2 20 E-16-JW Highway 62500 L 16.40 L 9E, 3F L 62.6 L 0.045 L 31 8

3 20 E-16-JX Highway 84000 L 16.40 L 9E, 3F L 62.6 L 0.045 L 31 8

4 20 E-16-JY Highway 84000 L 16.40 L 9E, 3F L 62.6 L 0.045 L 31 8

5 24 E-16-JT Highway — L* 16.40 L 9E, 3F L 62.6 L 0.045 L 33 3

6 24 E-16-JU Highway — L* 16.40 L 9E, 3F L 62.6 L 0.045 L 33 3

7 28 E-16-JV Highway 62500 L 26.25 H 9E, 3F L 62.6 L 0.045 L 31 7

8 W E-16-KB Water — — — — 9E, 3F L 62.6 L 0.045 L 33 8

9 W E-16-KC Water — — — — 9E, 3F L 62.6 L 0.045 L 32 7

10 W E-16-KD Water — — — — 9E, 3F L 62.6 L 0.045 L 33 7

95200 18.28 9.0E, 3.0F 62.6 0.045 31.6 7

50245 3.72 0.0E, 0.0F 0.0 0.000 1.6 2

183000 26.25 9E, 3F 62.6 0.045 33.0 8

62500 16.40 9E, 3F 62.6 0.045 28.0 3

84000 16.40 9E, 3F 62.6 0.045 31.5 7

— = data not provided

* = assumed category value because not provided

Mean

Standard Deviation

Max

Min

Median

174

Table A.2 CT Cluster Bridges

Table A.3 IA Cluster Bridges


NumberCorssing Type

Distance

to Coast

(miles)

Dist.

Cat.

(H/L)

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 3 2928 Highway 3.8 L 37400 H 16.17 L 4F, 8G, 0H H 26.3 L 0.315 H 38 6

2 13 5843 Highway 1.4 L 153700 H 17.09 H 5F, 7G, 0H L 47.4 H 0.315 H 26 6

3 13 5555 Highway 1.8 L 161900 H 16.57 H 5F, 7G, 0H L 47.4 H 0.315 H 28 6

4 15 4382 Highway 2.9 L 48400 H 16.57 H 5F, 7G, 0H L 31.9 L 0.315 H 32 6

5 21 5796 Highway 0.6 L 11600 L 15.68 L 5F, 7G, 0H L 47.4 H 0.315 H 26 7

6 23,W 3831 Highway-Water 2.2 L 4200 L 15.91 L 5F, 7G, 0H L 31.9 L 0.315 H 37 7

7 28,R 14 Highway-Rail 0.0 L — L* 26.67 H 4F, 7G, 1H H 36.1 L 0.251 L 13 7

8 29 4295 Highway 1.0 L 11200 L 29.99 H 5F, 7G, 0H L 47.4 H 0.315 H 34 7

9 29 5844D Highway 1.1 L — L* 16.24 H 5F, 7G, 0H L 47.4 H 0.315 H 26 6

10 29 5558 Highway 3.7 L — L* 16.50 H 5F, 7G, 0H L 41.2 H 0.315 H 26 7

11 29 5844B Highway 1.2 L — L* 18.67 H 5F, 7G, 0H L 47.4 H 0.315 H 25 6

12 31 3830 Highway 2.2 L 19400 L 16.24 L 5F, 7G, 0H L 31.9 L 0.315 H 37 6

13 31,W 3832 Highway-Water 1.2 L 4200 L 16.67 H 5F, 7G, 0H L 31.9 L 0.315 H 37 7

14 31,W 4383 Highway-Water 4.5 L 9000 L 16.40 H 5F, 7G, 0H L 31.9 L 0.315 H 32 6

15 53 5230 Highway 7.9 H 13400 L 15.91 L 5F, 7G, 0H L 41.2 H 0.315 H 31 7

16 53 3588 Highway 6.4 H — L* 13.91 L 5F, 7G, 0H L 41.2 H 0.315 H 44 7

17 54 5307 Highway 18.8 H 5300 L 14.93 L 5F, 7G, 0H L 38.7 H 0.127 L 29 8

18 55,R 3912 Highway-Rail 7.3 H — L* 14.67 L 5F, 7G, 0H L 35.7 L 0.315 H 42 5

19 61 4276 Highway 11.7 H 16500 L 16.40 H 5F, 7G, 0H L 41.2 H 0.315 H 35 7

20 62 5308 Highway 18.9 H 1020 L 16.67 H 5F, 7G, 0H L 38.7 H 0.127 L 29 7


7.5 47453 17.34 4.7F, 7.2G, 0.0H 38.3 0.273 23.5 7

7.4 44392 3.81 0.5F, 0.4G, 0.2H 12.3 0.071 11.9 1

30.4 161900 29.99 6F, 8G, 2H 100.1 0.315 78.0 9

0.0 900 13.91 3F, 6G, 0H 23.6 0.127 2.0 3

5.4 33800 16.40 5F, 7G, 0H 36.1 0.315 24.0 7


Standard Deviation

Max

Min

Median

Mean



Number

Corssing

Type

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 8 041331 Highway 88500 H 17.16 L 3F, 9G L 34.8 L 0.066 L 11 7

2 8 042061 Highway 36200 H 16.50 L 3F, 9G L 23.8 L 0.066 L 7 9

3 8 609280 Highway 65700 H 16.34 L 3F, 9G L 23.8 L 0.066 L 10 7

4 8 042571 Highway 93110 H 17.16 L 3F, 9G L 34.8 L 0.066 L 6 7

5 8 042611 Highway 77610 H 16.83 L 3F, 9G L 34.8 L 1.066 L 10 9

6 16 608565 Highway 94500 H 19.00 H 3F, 9G L 34.8 L 0.066 L 10 9

7 16 042491 Highway 85700 H 18.67 H 3F, 9G L 34.8 L 0.066 L 10 7

8 16 042711 Highway 78360 H 24.02 H 3F, 9G L 34.8 L 0.066 L 10 8

9 16 004111 Highway 73990 H 18.18 H 3F, 9G L 34.8 L 0.066 L 12 8

10 24 606775 Highway 5900 L 17.16 L 3F, 9G L 34.8 L 0.066 L 16 7

11 24 601935 Highway 18400 L 16.77 L 3F, 9G L 34.8 L 0.066 L 13 8

12 32 015225 Highway 12400 L 17.91 H 3F, 9G L 28.2 L 0.066 L 41 7

13 32 601770 Highway 18400 L 20.18 H 3F, 9G L 34.8 L 0.066 L 16 8

14 32 607285 Highway 7200 L 18.77 H 3F, 9G L 34.8 L 0.066 L 13 9

41394 18.19 3.0F, 9.0G 30.7 0.066 9.9 8

32612 2.01 0.0F, 0.0G 5.4 0.000 5.1 1

108300 24.02 3F, 9G 34.8 0.066 41.0 9

2030 16.34 3F, 9G 21.7 0.066 2.0 7

27500 17.54 3F, 9G 34.8 0.066 9.5 8

Mean

Standard Deviation

Max

Min

Median

175

Table A.4 MN Cluster Bridges

Table A.5 NC Cluster Bridges


NumberCorssing Type

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 2 04019 Highway 9700 H 16.11 L 2F, 9G, 1H, 0I H 53.1 H 0.030 L 34 5



4 3 600200 Highway 17500 H 16.57 L 2F, 9G, 1H, 0I H 34.2 L 0.070 H 43 7

5 6 27049 Highway 10300 H 16.70 L 4F, 8G, 0H, 0I L 56.0 H 0.057 L 24 7



8 8 19811 Highway 53000 H 16.31 L 4F, 8G, 0H, 0I L 44.3 L 0.057 L 35 7

9 8 19866 Highway 53000 H 16.99 L 4F, 8G, 0H, 0I L 44.3 L 0.057 L 34 7

10 12,R,W 69102 Highway-Water-Rail 66900 H 18.11 H 0F, 10G, 2H, 0I H 42.3 L 0.037 L 30 6

11 14 27796 Highway 135600 H 19.39 H 4F, 8G, 0H, 0I L 56.0 H 0.057 L 35 7

12 14 27047 Highway 111000 H 39.90 H 4F, 8G, 0H, 0I L 56.0 H 0.057 L 25 7

13 14 27727B Highway 109300 H 18.50 H 4F, 8G, 0H, 0I L 56.0 H 0.057 L 35 5

14 15 07018 Highway 13700 H 18.11 H 2F, 10G, 0H, 0I L 42.6 L 0.105 H 36 7

15 18 04021 Highway 3450 L 16.31 L 2F, 9G, 1H, 0I H 47.9 H 0.030 L 38 6

16 22,R 62532 Highway-Rail 2050 L 16.21 L 4F, 8G, 0H, 0I L 52.9 H 0.057 L 33 7

17 22 62870 Highway 3400 L 16.60 L 4F, 8G, 0H, 0I L 52.9 H 0.057 L 40 7

18 23 07017 Highway 7600 L 16.60 L 2F, 10G, 0H, 0I L 42.6 L 0.105 H 36 7

19 30,R 62526 Highway-Rail 300 L 25.30 H 3F, 9G, 0H, 0I L 52.9 H 0.057 L 40 5

20 30,R 62531 Highway-Rail 1025 L 20.31 H 4F, 8G, 0H, 0I L 52.9 H 0.057 L 36 8

29425 18.67 2.2F, 8.9G, 0.9H, 0.0I 45.3 0.065 31.3 7

51512 5.45 0.9F, 1.3G, 1.3H, 0.2I 7.8 0.025 8.9 1

303000 39.90 1F, 5G, 4H, 2I 88.1 0.105 48.0 9

35 16.11 5F, 7G, 0H, 0I 25.7 0.030 2.0 0

7800 16.65 2F, 10G, 0H, 0I 45.5 0.057 34.0 7

Mean

Standard Deviation

Max

Min

Median


Number

Corssing

Type

Distance

to Coast

(miles)

Dist.

Cat.

(H/L)

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 3 1900831 Rail 4.2 L — — — — 1F, 8G, 3H L 2.3 L 0.328 L 33 5

2 3 190084 Rail 4.2 L — — — — 1F, 8G, 3H L 2.3 L 0.328 L 33 7


4 3 470382 Highway 32.2 H 2000 L 16.31 L 3F, 9G, 0H L 3.0 H 0.328 L 30 7

5 19 1290044 Highway 5.3 H 2500 L 17.42 H 0F, 9G, 3H H 2.4 H 0.717 L 29 7

6 19 1290045 Highway 5.3 H 2500 L 17.16 H 0F, 9G, 3H H 2.4 H 0.717 L 29 8

7 32 12900582 Highway 2.1 L 23000 H 16.40 L 0F, 9G, 3H H 2.3 L 0.717 L 28 8



10 38 1290042 Highway 4.7 H 29000 H 17.59 H 0F, 9G, 3H H 2.4 H 0.717 L 30 7

9.4 14350 16.67 0.8F, 8.8G, 2.4H 2.5 0.561 29.8 7

12.0 13139 0.65 1.2F, 0.4G, 1.3H 0.3 0.201 1.9 1

32.2 29000 17.59 3F, 9G, 3H 3.0 0.717 33.0 8

2.1 1800 15.75 0F, 8G, 0H 2.3 0.328 28.0 5

4.4 12750 16.50 0F, 9G, 3H 2.4 0.717 29.5 7


Mean

Standard Deviation

Max

Min

Median1 = Inferior Reference Bridge

2 = Good Reference Bridge

176

Table A.6 NH Cluster Bridges

Table A.7 OH Cluster Bridges

Number Combination Structure Number Corssing Type

Distance

to Coast

(miles)

Dist.

Cat.

(H/L)

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)SCR

1 3 019700810009300 Highway 3.2 L 51318 H 16.40 L 1F, 4G, 6H, 1I H 59.2 L 0.750 H 38 7





6 19 022200590009600 Highway 12.3 L — L* 14.50 L 3F, 8G, 1H, 0I H 59.2 L 0.259 H 35 7

7 19 022200510009400 Highway 12.6 L — L* 14.21 L 3F, 8G, 1H, 0I H 59.2 L 0.259 H 35 8

8 32,W 022800870006300 Highway-Water 5.7 L — L* 17.95 H 5F, 6G, 1H, 0I L 57.3 L 0.180 L 22 9

9 32 021900760009600 Highway 14.4 L 18571 L 16.73 H 4F, 7G, 1H, 0I L 59.2 L 0.154 L 31 7

10 38 012800920005900 Highway 24.9 H 68103 H 16.50 L 4F, 7G, 1H, 0I L 65.1 H 0.180 L 37 7

11 38 017700960015300 Highway 19.5 H 71000 H 16.31 L 5F, 7G, 0H, 0I L 68.2 H 0.180 L 26 8

12 40 017701340007300 Highway 17.6 H 75000 H 16.50 L 5F, 7G, 0H, 0I L 55.4 L 0.180 L 16 8

13 46 005201560013800 Highway 30.5 H 41000 H 23.29 H 4F, 8G, 0H, 0I L 65.1 H 0.154 L 35 7

14 46 012800680009900 Highway 26.8 H 64000 H 17.13 H 4F, 7G, 1H, 0I L 65.1 H 0.154 L 37 7

15 46,R,W 016101230007300 Highway-Water-Rail 23.9 H 77400 H 20.57 H 4F, 7G, 1H, 0I L 65.1 H 0.180 L 25 7

16 54 017201120011300 Highway 28.0 H 3900 L 14.57 L 5F, 7G, 0H, 0I L 68.2 H 0.180 L 36 6



19 56,W 017701000011200 Highway-Water 19.3 H — L* 10.17 L 5F, 7G, 0H, 0I L 55.4 L 0.180 L 17 8

20 56,W 017701010011200 Highway-Water 19.3 H — L* 10.17 L 5F, 7G, 0H, 0I L 55.4 L 0.180 L 17 8

21 58 016101280012700 Highway 21.8 H — L* 17.72 H 3F, 8G, 1H, 0I H 65.1 H 0.180 L 36 8

22 58 003700950007000 Highway 19.2 H — L* 16.96 H 3F, 8G, 1H, 0I H 65.1 H 0.154 L 28 8

23 64 017701460003700 Highway 17.6 H 21000 L 17.42 H 5F, 7G, 0H, 0I L 55.4 L 0.180 L 20 8

24 64 017701510005700 Highway 17.1 H 26000 L 18.73 H 5F, 7G, 0H, 0I L 55.4 L 0.180 L 20 8

25 W 006502010002400 Water 0.1 L — — — — 1F, 4G, 6H, 1I H 59.2 L 0.750 H 29 7

26 W 006501310012300 Water 0.2 L — — — — 1F, 5G, 6H, 0I H 59.2 L 0.750 H 37 8

27 W 006501280012200 Water 0.3 L — — — — 1F, 5G, 6H, 0I H 59.2 L 0.750 H 38 7

28 W 006501230012600 Water 0.6 L — — — — 1F, 6G, 5H, 0I H 59.2 L 0.750 H 35 7

18.8 36748 16.33 3.8F, 6.9G, 1.2H, 0.1I 59.6 0.258 20.0 8

17.0 33592 2.77 1.2F, 1.1G, 1.6H, 0.3I 8.5 0.176 12.5 1

67.5 246700 23.29 5F, 9G, 6H, 2I 80.8 0.750 59.0 9

0.0 100 10.17 0F, 4G, 0H, 0I 41.6 0.102 2.0 4

15.3 27950 16.50 4F, 7G, 1H, 0I 59.2 0.180 18.0 8


Mean

Standard Deviation

Max

Min

Median



NumberCorssing Type

ADT

Under

Structure

ADT

Cat.

(H/L)

Verticle

Under-

Clearance

(ft.)

Vert.

Cat.

(H/L)

Relative

Humidity

Humidity

Cat.

(H/L)

Snow

(in.)

Snow

Cat.

(H/L)

Chloride

(mg/L)

Chloride

Cat.

(H/L)

Age

(years)

SC

R

1 1 7700105 Highway 47308 H 15.09 L 0F, 11G, 1H H 43.4 H 0.101 H 9 6




5 2 7701853 Highway 10281 H 15.49 L 0F, 10G, 2H H 49.2 H 0.070 L 45 8

6 2,W 7701799 Highway-Water 92927 H 16.01 L 0F, 11G, 1H H 49.2 H 0.100 L 32 5

7 2 7702043 Highway 10032 H 15.26 L 0F, 10G, 2H H 49.2 H 0.100 L 39 8

8 3 7808186 Highway 10108 H 15.16 L 1F, 11G, 0H H 40.1 L 0.101 H 45 6

9 4 7801246 Highway 8970 H 15.42 L 1F, 11G, 0H H 40.1 L 0.100 L 43 5

10 6 7606524 Highway 32165 H 16.08 L 2F, 10G, 0H L 49.2 H 0.100 L 31 7

11 6 7803788 Highway 8469 H 15.32 L 2F, 10G, 0H L 57.2 H 0.100 L 46 5

12 18 7701802 Highway 5161 L 15.26 L 0F, 11G, 1H H 49.2 H 0.100 L 32 7

13 18,W 7701810 Highway-Water 5394 L 15.26 L 0F, 10G, 2H H 49.2 H 0.100 L 32 7

14 24 7805934 Highway 7832 L 15.16 L 2F, 10G, 0H L 40.1 L 0.100 L 21 5

15 25 7700148 Highway 2931 L 17.85 H 1F, 11G, 0H H 43.4 H 0.101 H 31 6

16 25,W,R 7708645 Highway-Water-Rail 5508 L 23.00 H 0F, 10G, 2H H 49.2 H 0.101 H 37 7

17289 16.04 0.6F, 10.4G, 0.9H 43.1 0.100 43.1 8

22909 1.97 0.8F, 0.5G, 0.9H 12.7 0.002 12.7 1

99400 23.00 0F, 10G, 2H 95.3 0.101 95.3 9

295 15.09 2F, 10G, 0H 23.8 0.070 23.8 4

7832 15.37 0F, 11G, 1H 40.1 0.100 40.1 8

Standard Deviation

Max

Min

Median

Mean

177

Appendix B

PARAMETRIC COMBINATIONS

Table B.1 Deicing Cluster Parametric Combinations

CombinationCrossing

Type

ADT

Under

Structure

Vertical

Under-

Clearance

Relative

HumiditySnow Chloride

1 Highway High Low High High High

2 Highway High Low High High Low

3 Highway High Low High Low High

4 Highway High Low High Low Low

5 Highway High Low Low High High

6 Highway High Low Low High Low

7 Highway High Low Low Low High

8 Highway High Low Low Low Low

9 Highway High High High High High

10 Highway High High High High Low

11 Highway High High High Low High

12 Highway High High High Low Low

13 Highway High High Low High High

14 Highway High High Low High Low

15 Highway High High Low Low High

16 Highway High High Low Low Low

17 Highway Low Low High High High

18 Highway Low Low High High Low

19 Highway Low Low High Low High

20 Highway Low Low High Low Low

21 Highway Low Low Low High High

22 Highway Low Low Low High Low

23 Highway Low Low Low Low High

24 Highway Low Low Low Low Low

25 Highway Low High High High High

26 Highway Low High High High Low

27 Highway Low High High Low High

28 Highway Low High High Low Low

29 Highway Low High Low High High

30 Highway Low High Low High Low

31 Highway Low High Low Low High

32 Highway Low High Low Low Low

R Railway NA NA NA NA NA

W Waterway NA NA NA NA NA

178

Table B.2 Deicing + Coastal Cluster Parametric Combinations

CombinationCrossing

Type

Distance

to

Coast

ADT

Under

Structure

Vertical

Under-

Clearance

Relative

HumiditySnow Chloride

1 Highway Low High Low High High High

2 Highway Low High Low High High Low

3 Highway Low High Low High Low High

4 Highway Low High Low High Low Low

5 Highway Low High Low Low High High

6 Highway Low High Low Low High Low

7 Highway Low High Low Low Low High

8 Highway Low High Low Low Low Low

9 Highway Low High High High High High

10 Highway Low High High High High Low

11 Highway Low High High High Low High

12 Highway Low High High High Low Low

13 Highway Low High High Low High High

14 Highway Low High High Low High Low

15 Highway Low High High Low Low High

16 Highway Low High High Low Low Low

17 Highway Low Low Low High High High

18 Highway Low Low Low High High Low

19 Highway Low Low Low High Low High

20 Highway Low Low Low High Low Low

21 Highway Low Low Low Low High High

22 Highway Low Low Low Low High Low

23 Highway Low Low Low Low Low High

24 Highway Low Low Low Low Low Low

25 Highway Low Low High High High High

26 Highway Low Low High High High Low

27 Highway Low Low High High Low High

28 Highway Low Low High High Low Low

29 Highway Low Low High Low High High

30 Highway Low Low High Low High Low

31 Highway Low Low High Low Low High

32 Highway Low Low High Low Low Low

33 Highway High High Low High High High

34 Highway High High Low High High Low

35 Highway High High Low High Low High

36 Highway High High Low High Low Low

37 Highway High High Low Low High High

38 Highway High High Low Low High Low

39 Highway High High Low Low Low High

40 Highway High High Low Low Low Low

41 Highway High High High High High High

42 Highway High High High High High Low

43 Highway High High High High Low High

44 Highway High High High High Low Low

45 Highway High High High Low High High

46 Highway High High High Low High Low

47 Highway High High High Low Low High

48 Highway High High High Low Low Low

49 Highway High Low Low High High High

50 Highway High Low Low High High Low

51 Highway High Low Low High Low High

52 Highway High Low Low High Low Low

53 Highway High Low Low Low High High

54 Highway High Low Low Low High Low

55 Highway High Low Low Low Low High

56 Highway High Low Low Low Low Low

57 Highway High Low High High High High

58 Highway High Low High High High Low

59 Highway High Low High High Low High

60 Highway High Low High High Low Low

61 Highway High Low High Low High High

62 Highway High Low High Low High Low

63 Highway High Low High Low Low High

64 Highway High Low High Low Low Low

R Railway NA NA NA NA NA NA

W Waterway NA NA NA NA NA NA

179

Table B.3 Coastal Cluster Parametric Combinations

CombinationDistance

to CoastHumidity Chloride

1 Low High High

2 Low High Low

3 Low Low High

4 Low Low Low

5 High High High

6 High High Low

7 High Low High

8 High Low Low

180

Appendix C

FIELD DATA ENTRY SHEETS

181

Bridge: CO E-16-JWDate: 10/17/19

Span #1

st FB

(A)

2nd

B

(B)WL BFB

Facing

TrafficBackside

Right

LaneShoulder Distance (ft)

Nearest

joint designationX (ft) Y (in.) Z (in.)

Average of 9

Readings

(microns)

Standard

Deviation

Example 1 X X X X 10 10 Left abutment 10 0.1 0.1 100 30

1 1 X X X X 22.645 54.5 Abutment 2 99.5 1.5 18 42.1 13.4

2 1 X X X X 21.135 54.5 Abutment 2 99.5 4 0 16.6 2.5

3 1 X X X X 20.729 51.697 Abutment 2 102.303 75 0 31.6 29.7

4 1 X X X X 21.927 41.468 Abutment 2 112.532 1.5 18 33.6 6.1

5 1 X X X X 20.208 41.052 Abutment 2 112.948 4 0 20.9 4.9

6 1 X X X X 19.791 38.093 Abutment 2 115.907 75 0 25.2 7.1

7 1 X X X X 20.75 37.791 Abutment 2 116.209 1.5 18 32.2 21.8

8 1 X X X X 19.083 37.791 Abutment 2 116.209 4 0 34.7 44.2

9 1 X X X X 18.916 36.25 Abutment 2 117.75 75 0 39.8 14.9

10 1 X X X X 21.583 48.75 Abutment 2 105.25 1.5 18 24.3 6.6

11 1 X X X X 20.083 47.666 Abutment 2 106.334 4 0 28.8 6

12 1 X X X X 19.666 49.666 Abutment 2 104.334 75 0 49.3 44

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

1A X X X 21.927 41.468 Abutment 2 112.532 1.5 18

Most

Corrosive

Ultrasonic

Thickness

1B X X X 21.583 48.75 Abutment 2 105.25 1.5 18

Dry Film Thickness

Tape Samples, Rust Samples, Ultrasonic

Component

Structural Component

Test

Area

ID

Orientation Above (Rail)

Evaluating

Structural ComponentOrientation Above (Rail)

0.318

Note: Very odd/different bridges. Flanges have virtually no patina, only some mill scale and some pitting. Could flanges be regular steel? Webs have very fine, light patina. Splice plates clearly are WS. No chance of getting

samples from flanges. Very difficult to get on web also.

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

0.307

1st FB - fascia beam facing traffic first; 2nd beam - 2nd beam in the traffic direction

WL - Web near bottom flange; W - Web; BFT - Bottom flange top side; BFB - Bottom flange bottom side; TFB - Top flange bottom side

Element Identifier Examples: 1A - Fascia beam, Span 1; 2B - Second beam (interior beam adjacent to exterior beam) Span 2

182

Bridge: CO E-16-JXDate: 10/16/19

Span #1

st FB

(A)

2nd

B

(B)WL BFB

Facing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation


1 1 X X X X 20.937 41.604 Abutment 2 147.066 1.5 24 87.8 21

2 1 X X X X 18.937 41.604 Abutment 2 147.066 4 0 73.4 47.6

3 1 X X X X 18.802 38.166 Abutment 2 150.504 68 0 40.7 11.6

4 1 X X X X 21.822 60.083 Abutment 2 128.587 1.5 24 79.1 21.4

5 1 X X X X 19.822 60.083 Abutment 2 128.587 4 0 79.6 38.2

6 1 X X X X 19.687 55.75 Abutment 2 132.92 68 0 75.9 36.1

7 1 X X X X 19.083 38.416 Abutment 2 150.254 1.5 24 55.4 14.9

8 1 X X X X 21.083 35.916 Abutment 2 152.754 4 0 160.2 86.9

9 1 X X X X 18.916 37.625 Abutment 2 151.045 68 0 51 11.2

10 1 X X X X 21.833 55.791 Abutment 2 132.879 1.5 24 119.6 39.2

11 1 X X X X 19.791 55.25 Abutment 2 133.42 4 0 158.4 48.9

12 1 X X X X 19.666 52.416 Abutment 2 136.254 68 0 68.3 18.9

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

1A X X X 18.802 38.166 Abutment 2 150.504 68 0

Most

Corrosive

Ultrasonic

Thickness

1B X X X 21.833 55.791 Abutment 2 132.879 1.5 24




Dry Film Thickness


Component


Test

Area

ID


Evaluating


0.385

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

1.733

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

183

Bridge: CO E-16-JZDate: 10/15/19

Span #1

st FB

(A)

2nd

B

(B)WL BFB

Facing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation


1 2 X X X X 23.635 86.593 Pier 2 245.593 1.25 25 93.8 13.5

2 2 X X X X 21.625 86.593 Pier 2 245.593 4 0 68.9 34.6

3 2 X X X X 21.239 89.833 Pier 2 248.833 66 0 199.8 87.6

4 2 X X X X 23.666 102.968 Pier 2 261.968 1.25 25 74.8 21

5 2 X X X X 21.583 102.968 Pier 2 261.968 4 0 45.6 37.4

6 2 X X X X 21.166 76.666 Pier 3 270.584 66 0 181.3 85.3

7 2 X X X X 20.468 73.734 Pier 3 273.516 1.25 25 66.2 29.2

8 2 X X X X 20.39 72.848 Pier 3 274.402 4 0 106.8 70.1

9 2 X X X X 20.104 67.385 Pier 3 279.865 66 0 259.3 103.3

10 2 X X X X 20.421 87.453 Pier 3 259.797 1.25 25 91 35.1

11 2 X X X X 20.291 91.166 Pier 3 256.084 4 0 199.6 69

12 2 X X X X 20 86.697 Pier 3 260.553 66 0 259.3 55.4

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A X X X 23.666 102.968 Pier 2 261.968 1.25 25

Most

Corrosive

Ultrasonic

Thickness

2B X X X 20.421 87.453 Pier 3 259.797 1.25 25




Dry Film Thickness


Component


Test

Area

ID


Evaluating


0.373

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

0.361

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

184

Bridge: CT 3830Date: 5/7/19

Span # 1st FB 2nd B WL BFTFacing

TrafficBackside

Right


Nearest


With plastic

shim (261

microns thick)

(microns)

Standard

Deviation

Without plastic

shim (microns)

Standard

Deviation

Example 1 X X X X 10 10 Left abutment 10 0.1 0.1 100 30 100 30

1 1 X X X X 20.7 48.83 Abut. No. 1 48.83 16.5 1.5 640 247

2 1 X X X X 20.49 48.83 Abut.No. 1 48.83 11.5 21.5 578 169

3 1 X X X X 20.85 63.33 Abut.No. 1 63.33 16.5 1.5 708 80 221 68

4 1 X X X X 22.64 63.33 Abut.No. 1 63.33 11.5 21.5 602 38 144 25

5 1 X X X X 20.85 63.33 Abut.No. 1 63.33 5.5 1.5 767 132 256 43

6 1 X X X X 20.7 49.25 Abut.No. 1 49.25 5.5 1.5 950 282 385 138

7 1 X X X X 20.09 51.15 Abut.No. 1 51.15 17 1.5 1150.4 170 567.6 71

8 1 X X X X 21.86 51.15 Abut.No. 1 51.15 11.5 21.5 927.6 230 424.8 148

9 1 X X X X 20.25 62.01 Abut.No. 1 62.01 17 1.5 807.2 82 463.2 111

10 1 X X X X 21.86 62.01 Abut.No. 1 62.01 11.5 21.5 815.2 173 223.2 124

11 1 X X X X 20.16 62.01 Abut.No. 1 62.01 5 1.5 1046 122 547.6 173

12 1 X X X X 20.13 51.15 Abut. No. 1 51.15 5 1.5 1072.8 171 489.6 173

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

1E X X X 20.7 48.83 Abut. No. 1 48.83 16.5 1.5

Most

Corrosive

Ultrasonic

Thickness

1D X X X 20.13 51.15 Abut. No. 1 51.15 5 1.5

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

CoordinatesComponent

Vertical distance

from roadway/

ground (ft)

Beam

1.235

1.456




Dry Film Thickness


Component


Test

Area

ID

Orientation Above

Evaluating

Structural ComponentOrientation Above

Element

Identifier

185


Span #1

st FB

(A)

2nd

B

(B)WL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

X (ft)

[from Pier 1]Y (in.) Z (in.)

With plastic

shim (261

microns thick)

(microns)

Standard

Deviation

Without plastic

shim (microns)

Standard

Deviation


1 2 X X X X 17.75 56.67 Abut. 2 69.17 5.5 2.25 1191.2 198 879.8 375

2 2 X X X X 19.08 56.67 Abut. 2 69.17 11.5 18.25 831.6 114 235.2 42

3 2 X X X X 17.75 56.67 Abut. 2 69.17 18.75 2.25 761.2 117 348.4 143

4 2 X X X X 17.58 80.83 Abut. 2 45.01 6 2.25 1240 246 690 357

5 2 X X X X 18.92 80.83 Abut. 2 45.01 11.5 18.25 1150.4 206 556.8 205

6 2 X X X X 17.58 80.83 Abut. 2 45.01 18.75 2.25 818.8 89 307.2 87

7 2 X X X X 16.74 43.97 Pier 1 43.97 18.125 2 312 79 741 40

8 2 X X X X 18.07 43.97 Pier 1 43.97 12 18 707 113 252 104

9 2 X X X X 16.74 43.97 Pier 1 43.97 18.125 2 834 40 280 39

10 2 X X X X 16.69 59.84 Pier 1 59.84 5.875 2 656 36 264 65

11 2 X X X X 18.02 59.84 Pier 1 59.84 12 18 674 47 240 47

12 2 X X X X 16.69 59.84 Pier 1 59.84 18.125 2 772 148 412 177

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A X X X 16.74 43.97 Pier 1 43.97 18.125 2

Most

Corrosive

Ultrasonic

Thickness

2B X X X 17.58 80.83 Abut. 2 45.01 6 2.25




Dry Film Thickness


Component


Test

Area

ID

Orientation Above

Evaluating


Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

2.213

1.961

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

186


Span #1

st FB

(K)

2nd

B

(L)WL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

X (ft)

[from Pier 1]Y (in.) Z (in.)

With plastic

shim (261

microns thick)

(microns)

Standard

Deviation

Without plastic

shim (microns)

Standard

Deviation


1 1 X X X X 15.21 27.85 Left abut. 27.85 5.81 1.88 1131 271 796 243

2 1 X X X X 16.83 27.85 Left abut. 27.85 11.75 21.25 665 28.7 183.2 67.7

3 1 X X X X 15.21 27.85 Left abut. 27.85 18.25 1.88 826 86.7 342.4 115.2

4 1 X X X X 15.73 45.98 Left abut. 45.98 5.81 1.88 935.2 217.7 654.8 491.5

5 1 X X X X 17.33 45.98 Left abut. 45.98 11.75 21.25 554.4 29.3 380 56.9

6 1 X X X X 15.73 45.98 Left abut. 45.98 18.25 1.88 853.2 30.7 594.8 262.1

7 1 X X X X 16.21 25.16 Left abut. 25.16 18.00 2.13 840.4 106 356.4 128

8 1 X X X X 18.05 25.16 Left abut. 25.16 11.75 21.25 478.8 41.3 124 34

9 1 X X X X 16.21 25.16 Left abut. 25.16 5.75 2.13 827.2 54 294.8 129

10 1 X X X X 16.85 46.96 Left abut. 46.96 18.50 2.13 1178.4 156.9 786.8 337

11 1 X X X X 18.72 46.96 Left abut. 46.96 11.75 21.25 702.8 59.1 571.2 68.1

12 1 X X X X 16.86 46.96 Left abut. 46.96 5.75 2.13 1235.2 244.8 703.2 327

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

1K X X X 15.73 45.98 Left abut. 45.98 11.75 21.25

Most

Corrosive

Ultrasonic

Thickness

1L X X X 16.85 46.96 Left abut. 46.96 18.50 2.13

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

CoordinatesComponent

Vertical distance

from roadway/

ground (ft)

Beam

2.114

1.947




Dry Film Thickness


Component


Test

Area

ID

Orientation Above

Evaluating


Element

Identifier

187

Bridge: IA 004111Date: 5/14/19

Span #1

st FB

A

2nd

B

BWL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

(facing traffic)

X (ft) Y (in.) Z (in.)

Average of 9

Readings

(mills)

Standard

Deviation


1 2 X X X X 21 68closest face of

pier139 3 1.25 39.41 18.19


pier139 6 18 12.31 3.17


pier139 10 1.25 49.93 18.64


pier138 3 1.25 13.19 5.07


pier138 6 18 13.26 4.7


pier138 10 1.25 45.84 18.73


pier151 3 1.25 51.04 19.06


pier151 6 18 16.24 5.75


pier151 10 1.25 49.9 23.83


pier148 3 1.25 8.71 1.71


pier148 6 18 10.89 3.72


pier148 10 1.25 51.9 16.14

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A

(sample

6)

X X X 21 67closest face of

pier138 10 1.25 1.223,1.228,1.223

Most

Corrosive

Ultrasonic

Thickness

2A

(sample

4)

X X X 21 67closest face of

pier138 3 1.25 1.223, 1.225, 1.227,1.223,1.224




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

1.225

1.224

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

188

Bridge: IA 041331Date: 5/14/19

Span #1

st FB

A

2nd

B

BWL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

(facing traffic)


Average of 9

Readings

(mills)

Standard

Deviation


1 2 X X X X 17 85 East abutment 208 4 1.5 72.56 7.46












W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A

(sample

4)

X X X X 17 86 East abutment 207 4 1.5

Most

Corrosive

Ultrasonic

Thickness

2A

(sample

6)

X X X X 17 86 East abutment 207 12 1.5




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

1.458

1.444

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

189

Bridge: IA 042711Date: 5/14/2019

Span #1

st FB

G

2nd

B

FWL BFT

Facing

TrafficBackside Right Lane Shoulder Distance (ft)

Nearest

joint designation

(facing traffic)


Average of 9

Readings

(mills)

Standard

Deviation


1 2 X X X X 25 56Bearing of Pier

1171 12 1 50.49 17.05


1171 8 17 9.37 2.16


1171 4 1 60.6 11.45


1171 12 1 8.03 1.36


1171 8 17 8.8 2.88


1171 4 1 15.83 10.14


1162 12 1 9.06 2.89


1162 8 17 6.81 3.13


1162 4 1 51.28 13.68


1163 12 1 52.49 17.29


1163 8 17 40.47 5.8


1163 4 1 53.83 12.48

W BFT BFB TFBFacing

TrafficBackside Right Lane Shoulder Distance (ft)

Nearest


Typical

Ultrasonic

Thickness

2G

(sample

5)

X X X 25 56Bearing of Pier

18 17

Most

Corrosive

Ultrasonic

Thickness

2G

(sample

6)

X X X 25 56Bearing of Pier

14 1

note: the unit for dry film here is not micro, it should be mills




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

0.96

0.962

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

190

Bridge: MN 04019Date: 6/3/2019

Span # 1st

FB 2nd

B WL BFTFacing

TrafficBackside

Right


Nearest


Average of 9

Readings

(mills)

Standard

Deviation

1 2 X X X X 17 37from face pier

1111 4 1.25 38.49 11.25


1111 8 22 9.68 2.34

2A 2 X X X X 20 37from face pier

1111 8 28 5.34 2.22


1109 12 1 45.73 7.83


1109 8 22 18.59 7.96


1109 4 1 18.92 5.17

6A 2 X X X X 17 50from face pier

1124 4 1 29.99 25.16


1127 4 1 46.18 25.57


1127 8 44 10.4 1.38


1125 12 1 32.97 10.13


1125 8 22 11.32 3.32


1125 4 22 30.5 8.91

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness2A X X X 17 50

from face pier

1124 4 1

Most

Corrosive

Ultrasonic

Thickness

2B X X X 18 35from face pier

1109 12 1

Vertical distance

from roadway/

ground (ft)

1.110

1.123

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance




Component


Test

Area

ID

Orientation Above

Evaluating


Location

191


Span # 1st

FB 2nd

B WL BFTFacing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation

1 2 X X X X 16 26 Pier 1, bearing 84.5 12 1 16.21 7.54









10 2 X X X X 16 10 Pier 1, bearing 68.5 12 2 13.51 8.44*

11 2 X X X X 18 10 Pier 1, bearing 68.5 8 24 9.07 1.59*


W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness2B X X X 17 26 Pier 1, bearing 84.5 12 1

Most

Corrosive

Ultrasonic

Thickness

2B X X X 16 12 Pier 1, bearing 70.5 12 1


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance


Test

Area

ID

Orientation Above

Vertical distance

from roadway/

ground (ft)




Component

Evaluating


Location

0.979

0.992

Dry Film Thickness

192


Span # 1st

FB 2nd

B WL BFTFacing

TrafficBackside

Right


Nearest

joint

designation


Average of 9

Readings

(microns)

Standard

Deviation

1 1 X X X X 18 36 S. Abut. 36 15 2 NA NA

2 1 X X X X 20 36 S. Abut. 36 10 21 43.13 18.07

3 1 X X X X 18 36 S. Abut. 36 5 2 72.53 14.10

4 1 X X X X 18 36 S. Abut. 36 15 2 77.4 0

5 1 X X X X 20 36 S. Abut. 36 10 19 50.44 23.11

6 1 X X X X 18 36 S. Abut. 36 5 2 73.71 5.91

7 1 X X X X 19 24 S. Abut. 24 15 2 78.8 0

8 1 X X X X 20 24 S. Abut. 24 10 18 54.01 19.66

9 1 X X X X 19 24 S. Abut. 24 5 2 65.73 12.87

10 1 X X X X 19 24 S. Abut. 24 15 2 72.28 4.19

11 1 X X X X 20 24 S. Abut. 24 10 22** 66.24 12.6

12 1 X X X X 19 24 S. Abut. 24 5 2 71.07 14.19

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest

joint

designation


Typical

Ultrasonic

Thickness1B X X X 18 36 S. Abut. 36 15 2

Most

Corrosive

Ultrasonic

Thickness

1B X X X 19 24 S. Abut. 24 15 2




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical

distance from

roadway/

ground (ft)

1.320

1.318

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

ComponentVertical

distance from

roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

193

Bridge: NC 190083Date: 9/16/19

Span #1

st FB

(A)

2nd

B

(B)WL BFT

Facing

TrafficBackside

1/2

Point of

Span

1/4

Point of

Span

Distance (ft)Nearest


Average of 9

Readings

(microns)

Standard

Deviation


1 1 X X X X 6.17 9.25 Left abutment 9.25 4.88 9 135 35.7

2 1 X X X X 5.5 9.25 Left abutment 9.25 2.25 0.63 176.4 48.7





7 1 X X X X 13.25 18.17 Left abutment 18.17 4.88 9 117.3 34






W BFT BFB TFBFacing

TrafficBackside

1/2

Point of

Span

1/4

Point of

Span

Distance (ft)Nearest


Typical

Ultrasonic

Thickness

1A X X X 12.5 18.17 Left abutment 18.17 2.25 0.63

Most

Corrosive

Ultrasonic

Thickness

1B X X X 12.5 19.17 Left abutment 19.17 2.25 0.63




Dry Film Thickness


Component


Test

Area

ID


Evaluating


0.638

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

0.639

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

194

Bridge: NC 1290057Date: 9/18/19

Span #1

st FB

(D)

2nd

B

(C)WL BFT

Facing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation


1 2 X X X X 19.46 13.9First Pier from

Origin73.23 10.75 0.75 180.4 43.4


Origin73.23 7.38 15.38 121.1 27.8


Origin73.23 3.5 0.75 358.7 149.7


Origin86.85 10.75 1.38 138.7 40.6


Origin86.85 7.38 16 125.3 30.3


Origin86.85 3.5 1.38 273.1 75.7


Origin71 10.63 0.75 462 78.3


Origin71 7.38 15.75 166 76.2


Origin71 3.5 0.75 519.1 359.1


Origin85.42 10.88 1.38 617.6 181.7


Origin85.42 7.38 16.38 250.9 61


Origin85.42 3.25 1.38 399.1 93.4

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2D X X X 19.46 13.9First Pier from

Origin73.23 10.75 0.75

Most

Corrosive

Ultrasonic

Thickness

2C X X X 19.92 26.08First Pier from

Origin85.42 10.88 1.38




Dry Film Thickness


Component


Test

Area

ID

Orientation Above

Evaluating


1.353

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

0.719

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

195

Bridge: NC 1290058Date: 9/17/19

Span #1

st FB

(A)

2nd

B

(B)WL BFT

Facing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation


1 2 X X X X 16.67 6.83First pier from

right131.92 4 0.88 174.9 33.8


right131.92 7.75 17.88 163.8 44.9


right131.92 12.13 0.88 374 111.5


right131.08 4 0.88 368.9 79.4


right131.08 7.75 17.88 173.8 49.7


right131.08 12.13 0.88 371.3 85.1


right116.5 3.88 0.88 152.7 49.1


right116.5 7.75 17.88 123.6 33.4


right116.5 12.13 0.88 810 260.1


right117.58 3.88 0.88 490 83.5


right117.58 7.75 17.88 177.8 23.7


right117.58 12.13 0.88 746.7 192.6

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness2A X X X 17.11 22.25

First pier from

right116.5 3.88 0.88

Most

Corrosive

Ultrasonic

Thickness

2B X X X 17.24 21.17First pier from

right117.58 12.13 0.88

LocationCoordinates

Horizontal distance

Horizontal distance

Thickness (in.)

Location

Vertical distance

from roadway/

ground (ft)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

0.86




Dry Film Thickness


Component


Test

Area

ID

Orientation Above

Evaluating


0.86

196

Bridge: NH 017201120011300Date: 7/9/19

Span #1

st FB

A

2nd

B

BWL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

(facing traffic)


Average of 9

Readings

(microns)

Standard

Deviation


1 1 X X X X 15.47 26.19Right

abutment41.96 2.5 0.875 362.7 94.5

2 1 X X X X 16.51 26.19Right

abutment41.96 5.75 11.625 257.8 85.7

3 1 X X X X 15.47 26.19Right

abutment41.96 8.75 0.875 303.1 51.1

4 1 X X X X 15.91 15.41Right

abutment52.78 3 0.875 303.8 125.4

5 1 X X X X 16.96 15.41Right

abutment52.78 5.75 11.125 90.7 22.8

6 1 X X X X 15.91 15.41Right

abutment52.78 8.75 0.875 302.7 86

7 1 X X X X 14.85 25.46Right

abutment42.04 2.5 0.75 263.1 80.2

8 1 X X X X 15.81 25.46Right

abutment42.04 5.75 12.25 101.1 22.7

9 1 X X X X 14.85 25.46Right

abutment42.04 9.5 0.75 394.4 122.2

10 1 X X X X 15.4 14.85Right

abutment52.69 2.5 0.75 220.4 114.5

11 1 X X X X 16.42 14.85Right

abutment52.69 5.75 12.25 92.2 26.9

12 1 X X X X 15.4 14.85Right

abutment52.69 9.5 0.75 303.1 95.6

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness1A X X X 15.4 14.85

Right

abutment52.69 2.5 0.75

Most

Corrosive

Ultrasonic

Thickness

1B X X X 15.47 26.19Right

abutment41.96 8.75 0.875




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

0.772

0.785

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

197

Bridge: NH 1110112007900Date: 7/11/19

Span #1

st FB

A

2nd

B

BWL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

(facing traffic)


Average of 9

Readings

(microns)

Standard

Deviation


1 2 X X X X 18.42 60.23Right

abutment102.21 5 1 179.8 53.2

2 2 X X X X 20.5 60.23Right

abutment102.21 9.75 26 224.4 59.6

3 2 X X X X 18.42 60.23Right

abutment102.21 15.5 1 1861.8 141.6

4 2 X X X X 18.02 70.71Right

abutment91.07 15.5 1 1737.1 203.5

5 2 X X X X 18.02 70.71Right

abutment91.07 5 1 194 49.5

6 2 X X X X 20.1 70.71Right

abutment91.07 9.75 26 208 42

7 2 X X X X 18.08 59.35Right

abutment101.9 5 1.125 1980 180

8 2 X X X X 20.25 59.35Right

abutment101.9 9.875 27.125 492.4 236.6

9 2 X X X X 18.08 59.35Right

abutment101.9 15.25 1.125 >2000 -

10 2 X X X X 17.96 69.5Right

abutment91.9 5 1.125 >2000 -

11 2 X X X X 20.13 69.5Right

abutment91.9 9.875 1.125 339.1 142.3

12 2 X X X X 20.96 69.5Right

abutment91.9 15.25 1.125 >2000 -

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A X X X 18.02 70.71Right

abutment91.07 5 1

Most

Corrosive

Ultrasonic

Thickness

2B X X X 17.96 69.5Right

abutment91.9 5 1.125

0.975

0.939

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

198

Bridge: NH 1110112007900

Date: 7/11/19

W BFT BFB TFBFacing

TrafficBackside

Right

LaneShoulder

Distance

(ft)

Nearest

joint

designation


Depth (in.)

Severity

(Approximate

Surface Area

in.2)

2B X X X 18.08 59.35Right

abutment101.9 15.25 1.125 0.1

2B X X X 18.08 59.35Right

abutment101.9 15.25 1.125 0.11

Thickness (in.)

Severity

(Approximate

Surface Area

in.2)

Corrosion, Pitting, & Section Loss

AboveMax Length of

Corroded Area

(in.)

Max Width of

Corroded Area

(in.)

Corrosion

(If Applicable)

Horizontal distance

Section

Loss

(If Applicable)

Evaluating

Pitting

(If Applicable)


W - Web; BFT - Bottom flange top side; BFB - Bottom flange bottom side; TFB - Top flange bottom side

Element

Identifier

Component Vertical

distance

from

roadway/

ground (ft)

Structural ComponentOrientation

LocationCoordinates

199

Bridge: NH 017701460003700Date: 7/8/19

Span #1

st FB

G

2nd

B

FWL BFT

Facing

TrafficBackside

Right


Nearest

joint designation

(facing traffic)


Average of 9

Readings

(microns)

Standard

Deviation


1 1 X X X X 20.44 32.72Right

abutment32.72 12.5 1.38 198 41.4

2 1 X X X X 22.44 32.72Right

abutment32.72 9.25 25.38 260.7 73.4

3 1 X X X X 20.45 32.13Right

abutment32.13 4 1.38 946.7 343.5

4 1 X X X X 20.56 32.76Right

abutment32.76 12.5 1.5 1786.4 599.3

5 1 X X X X 22.56 32.76Right

abutment32.76 9.25 25.5 261.6 83.2

6 1 X X X X 20.43 32.04Right

abutment32.04 4.5 1.5 1505.1 615.2

7 1 X X X X 19.71 17.82Right

abutment17.82 13.5 1.5 906.9 674.9

8 1 X X X X 21.83 17.82Right

abutment17.82 9 25.5 206.4 58.4

9 1 X X X X 19.7 17.78Right

abutment17.78 4.38 1.5 575.3 344.1

10 1 X X X X 19.71 17.71Right

abutment17.71 13.5 1.38 153.3 19.8

11 1 X X X X 21.83 17.71Right

abutment17.71 9 25.38 198 44

12 1 X X X X 19.65 17.69Right

abutment17.69 4.5 1.5 447.6 217.7

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

1G X X X 19.71 17.71Right

abutment17.71 13.5 1.38

Most

Corrosive

Ultrasonic

Thickness

1F X X X 20.56 32.76Right

abutment32.76 12.5 1.5

1.339

1.371

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

200

Bridge: NH 017701460003700

Date: 7/8/19

W BFT BFB TFBFacing

TrafficBackside

Right

LaneShoulder

Distance

(ft)

Nearest

joint

designation


Depth (in.)

Severity

(Approximate

Surface Area

in.2)

Thickness (in.)

Severity

(Approximate

Surface Area

in.2)

1F X X X 20.56 32.76Right

abutment32.76 12.5 1.5 0.0625

1F X X X 20.43 32.04Right

abutment32.04 4.5 1.5 0.03125

Corrosion, Pitting, & Section Loss

AboveMax Length of

Corroded Area

(in.)

Max Width of

Corroded Area

(in.)

Corrosion

(If Applicable)

Horizontal distance

Section

Loss

(If Applicable)

Evaluating

Pitting

(If Applicable)


W - Web; BFT - Bottom flange top side; BFB - Bottom flange bottom side; TFB - Top flange bottom side

Element

Identifier

Component Vertical

distance

from

roadway/

ground (ft)

Structural ComponentOrientation

LocationCoordinates

201

Bridge: OH 7700105

Date: 6/19/2019

Span # 1st FB 2nd B WL BFTFacing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation

1 2 X X X X 16 24E. Abut.

Bearing24 3 1 237.6 115.2

2 2 X X X X 16 24E. Abut.

Bearing24 6 12 589.8 433.2

3 2 X X X X 16 24E. Abut.

Bearing24 9 1 1599.8 322.8

4 2 X X X X 15 29E. Abut.

Bearing29 3 1 272.9 62.5

5 2 X X X X 15 29E. Abut.

Bearing29 6 12 563.8 214.1

6 2 X X X X 15 29E. Abut.

Bearing29 9 1 1277.1 315.8

7 2 X X X X 16 24E. Abut.

Bearing24 3 1 1463.8 175.6

8 2 X X X X 16 24E. Abut.

Bearing24 6 12 735.3 288.6

9 2 X X X X 16 24E. Abut.

Bearing24 9 1 1381.1 302.9

10 2 X X X X 16 31E. Abut.

Bearing31 3 1 1403.3 286.9

11 2 X X X X 16 31E. Abut.

Bearing31 6 12 639.8 303

12 2 X X X X 16 31E. Abut.

Bearing31 9 1 1605.8 443.2

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness

2A X X X 16 24E. Abut.

Bearing24 3 1

Most

Corrosive

Ultrasonic

Thickness

2B X X X 16 24E. Abut.

Bearing24 9 1




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

0.871

0.902

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

202

Bridge: OH 7701977Date: 6/18/19

Span # 1st

FB 2nd

B WL BFTFacing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation

1 2 X X X X 17 31East Abut.

Bearing31 4 1 452.7 501.8


Bearing31 9 18 421.1 549.9


Bearing31 14 1 940.9 466.5


Bearing41 4 1 330 83.1


Bearing41 9 18 208.2 67.2


Bearing41 14 1 976.4 220.2


Bearing32 4 1 1032.4 320.7


Bearing32 9 18 656.9 622.4


Bearing32 14 1 798.2 372.5


Bearing42 4 1 1373.6 414.2


Bearing42 9 18 348.7 94.2


Bearing42 14 1 1478.9 245.6

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness2A X X X 17 41

East Abut.

Bearing41 4 1

Most

Corrosive

Ultrasonic

Thickness

2B X X X 18 32East Abut.

Bearing32 14 1

0.962

1.005

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

203

Bridge: OH 7701993Date: 6/19/19

Span #1

st FB

(D)

2nd

B

(C)WL BFT

Facing

TrafficBackside

Right


Nearest


Average of 9

Readings

(microns)

Standard

Deviation

1 2 X X X X 19 15S. Pier 1

Bearing15 9 1 245.6 45

2 2 X X X X 19 15S. Pier 1

Bearing15 6 12 117.1 28

3 2 X X X X 19 15S. Pier 1

Bearing15 3 1 803.8 335.1

4 2 X X X X 18 35S. Pier 1

Bearing35 10 1 333.8 71.8

5 2 X X X X 18 35S. Pier 1

Bearing35 6 12 237.8 73.9

6 2 X X X X 18 35S. Pier 1

Bearing35 3 1 556.4 248.9

7 2 X X X X 19 15S. Pier 1

Bearing15 9 1 706.4 226.3

8 2 X X X X 19 15S. Pier 1

Bearing15 6 12 238.7 67.3

9 2 X X X X 19 15S. Pier 1

Bearing15 3 1 518.4 208.1

10 2 X X X X 18 35S. Pier 1

Bearing35 10 1 480.4 143.3

11 2 X X X X 18 35S. Pier 1

Bearing35 6 12 282.7 66.7

12 2 X X X X 18 35S. Pier 1

Bearing35 3 1 628 239.1

W BFT BFB TFBFacing

TrafficBackside

Right


Nearest


Typical

Ultrasonic

Thickness2D X X X 18 35

S. Pier 1

Bearing35 10 1

Most

Corrosive

Ultrasonic

Thickness

2C X X X 19 15S. Pier 1

Bearing15 9 1




Component


Test

Area

ID

Orientation Above

Evaluating


Location

Vertical distance

from roadway/

ground (ft)

0.99

0.995

Dry Film Thickness


Thickness (in.)

Coordinates

Element

Identifier

Component

Vertical distance

from roadway/

ground (ft)

Beam

LocationCoordinates

Horizontal distance

Horizontal distance

204

Appendix D

MATLAB Script for Digital Image Processing of Tape Samples

%MATLAB script for image processing of tape samples

%% Program to determine the particle size distribution of Rust in a Tape Sample

% *** Notes ****

% Reads all pictures in a given folder and outputs the equivalent diameter of

% each individual particle in inches.

% Computes rust percentage using *optimized* graythresh.

% Computes the equivalent diameter of each particle by converting its area.

% Will ignore all particles below provided minimum threshold.

% Prints an rgb example image with green bounding boxes around the

% particles the program has captured (uncomment commands if this is to

% be done)

% Output is saved to excel file called Results.xlsx.

% Excel sheet will contain 1 row per sample. The first column will contain

% the name of the tape file, the second row contains the total rust

% percentage, and the following columns include the rust percentage for

% those % particular sizes. For instance, the column labeled 0.03125

% (1/32) represents the percentage of particles with diameters in the

% range of 0-1/32". The next would represent 1/32"-1/16", 1/16"-1/8"

% and so on.

clearvars

clc; %clears command window

tic;

% ******* CHOOSE THE LOCATION OF DATA DUMP *******

myFolder = 'C:\Users\ruppjt\Desktop\Images';

% ***** SELECT MINIMUM THRESHOLD FOR DIAMETER (in.)(anything lower will be

% removed)

Min_Thresh = 0;

%Throw error message if aforementioned path incorrect

if ~isdir(myFolder)

errorMessage = sprintf('Error: The following folder does not exist:\n%s', myFolder);

uiwait(warndlg(errorMessage));

return;

end

%Reads all text files sequentially

filePattern = fullfile(myFolder, '*.jpg');

%Counts total number of text files in the folder

imgFiles = dir(filePattern);

%Throw error message if data folder is incorrect

if isempty(imgFiles)

errorMessage = sprintf('No text files in folder\n');

uiwait(warndlg(errorMessage));

return; %Stops further execution of Program

end

for k = 1:length(imgFiles)

205

baseFileName = imgFiles(k).name;

fullFileName = fullfile(myFolder, baseFileName);

%Displays name of file being read

fprintf(1, 'Now reading %s\n', fullFileName);

im1=imread(fullFileName); % Reading image

%% Evaluating Rust Density

%Computing threshold for converting original image (tape sample) to binary

% image

gl=graythresh(im1);

% Converting to binary image with *optimised* graythresh value as level

% (Threshold)

bwg=im2bw(im1,(gl+.25)/2);

% Get dimensions of the binary image. It should be same for both images

[A1,B1]=size(bwg);

% Finding the percentage of black (Rust in our case)

l2=find(bwg==0);

bldensity2=length(l2)/(A1*B1);

%% Capturing Individual Particle Areas

bwg=1-bwg; %inverting the binary image to make the background 0 and the rust

particles 1

[bwg_labeled,num]=bwlabel(bwg); %labeling each individual rust particle

example=label2rgb(bwg_labeled); %creating example image in rgb to show distinction

between particles

%finding the area of each particle (in pixels) using regionprops

area_vect=regionprops(bwg_labeled, 'Area');

final_areas=[area_vect(:).Area];

final_areas=sort(final_areas); %sorting for purposes of output

area_sum=sum(final_areas(:)); %used later in calculating percentages

%converting from pixels to inches

conversion_factor=0.438/40000;

final_areas=sqrt(4*(conversion_factor*final_areas)/pi);

%creating a minimum threshold

idx=1;

while idx<=length(final_areas)

if final_areas(idx)<Min_Thresh

final_areas(idx)=[];

else

idx=idx+1;

end

end

% Uncomment these commands for bounding boxes

% %printing green bounding boxes around each particle

% boxes=regionprops(bwg_labeled, 'BoundingBox').';

% final_boxes=[boxes(:).BoundingBox];

% final_boxes=reshape(final_boxes, [4,num]);

% %removing the boxes below the minimum threshold

% ix=1;

% finl_areas=[area_vect(:).Area];

% finl_areas=conversion_factor*finl_areas;

% while ix<=length(finl_areas)

% if finl_areas(ix)<Min_Thresh

% finl_areas(ix)=[];

% final_boxes(:,ix)=[];

% else

206

% ix=ix+1;

% end

% end

% hold on

% imshow(example);

% for indx= 1:length(final_boxes)

% rectangle('Position',final_boxes(:,indx),'EdgeColor','g');

% end

% hold off

%% Creating Histogram Table

%Takes any 1 row vector as an input.

%Produces two vectors, X and Y.

%X is a 1 row vector whose values represent a range of diameters,

% starting from the previous value and ending at the current one. i.e.

% 0.25 means 0.125-0.25.

%Y is a 1 row vector that contains the percentage of the total image

% occupied by all particles in that range of diameters.

X=0;

Y=0;

Am = final_areas;

Y(1)=0;

X_idx=1;

X(1)=1/32;

for q=1:length(Am)

if Am(q)<X(X_idx)

Y(X_idx)=Y(X_idx)+(pi*(Am(q)^2)/(4*conversion_factor));

else

while Am(q)>=X(X_idx)

X_idx=X_idx+1;

X(X_idx)=2*X(X_idx-1);

Y(X_idx)=0;

end

Y(X_idx)=(pi*(Am(q)^2)/(4*conversion_factor));

end

end

Y=100*Y/(A1*B1);

%% Writing output to excel file - Area/Frequency of each individual Particle

[ext,name] = fileparts(baseFileName);

name=str2num(name);

filename=strcat('Results','.xlsx'); %Creating excel file

A = {'Test Area ID', 'Density', '% of Area'}; %Creates Header Row

sheet=1;

xlRangea = 'A1';

xlswrite(filename,A,sheet,xlRangea);

B = X;

xlRangeb = 'C2';

xlswrite(filename,B,sheet,xlRangeb);

C = Y;

cellc=strcat('C', num2str(name+2));

xlRangec = cellc;

xlswrite(filename,C,sheet,xlRangec);

E = sum(Y);

cellc=strcat('B', num2str(name+2));

xlRangec = cellc;

xlswrite(filename,E,sheet,xlRangec);

D = {name};

celld=strcat('A', num2str(name+2));

xlRanged = celld;

xlswrite(filename,D,sheet,xlRanged);

207

end %Ends For Loop

%% Statistical Analysis

xlswrite(filename,{'=average(B3:B58)'},sheet,'B60');

xlswrite(filename,{'=STDEV.P(B3:B58)'},sheet,'B61');

xlswrite(filename,{'=B60/B61'},sheet,'B62');

xlswrite(filename,{'=max(B3:B58)'},sheet,'B63');

xlswrite(filename,{'=min(B3:B58)'},sheet,'B64');

xlswrite(filename,{'=median(B3:B58)'},sheet,'B65');

xlswrite(filename,{'Average'},sheet,'A60');

xlswrite(filename,{'Standard Deviation'},sheet,'A61');

xlswrite(filename,{'Coefficient of variation'},sheet,'A62');

xlswrite(filename,{'Maximum'},sheet,'A63');

xlswrite(filename,{'Minimum'},sheet,'A64');

xlswrite(filename,{'Median'},sheet,'A65');

%% Clearing temporary variables

%bar(X,Y);

%clearvars %Clears all variables

toc;

208

Appendix E

MAINTENANCE SURVEYS

E.1 Original Survey

Dear LTBP State Coordinator:

The University of Delaware’s Center for Innovative Bridge Engineering, in conjunction

with the Rutgers’ Center for Advanced Infrastructure and Transportation, is currently

conducting a research project evaluating the performance of uncoated weathering steel

(UWS) highway bridges in the United States as part of the FHWA Long Term Bridge

Performance Program (LTBPP). This research will ultimately be used to provide

guidance to bridge owners on appropriate uses of UWS. One task of this project is to

gather information on maintenance practices and the use of deicing agents. Your

assistance in providing information related to this task would be greatly appreciated.

We would like to request the following from you:

1. Electronic versions of any manuals, specifications, or procedures for

maintenance of your bridges. This would include information that is applicable

to all of your bridges, as well as any maintenance information that is specific to

UWS.

2. Information/data on bridge washing. Specifically, (1) does your state wash their

bridges, and (2) if so, please provide information on the frequency and

procedure for washing.

3. Information/data on the use of deicing salts and chemicals. Specifically, (1)

does your state use any salts or chemicals for deicing and snow removal, and

(2) if so, please provide information on the type and annual quantities of

materials dispersed. Please provide as much detail as existing data allows, e.g.,

tons-per-lane mile of roadway or average tons per county, versus total amounts,

if possible. Our goal here is to calculate a best estimate of the amount of

deicing agents that are dispersed on to the decks of your UWS bridges. Any

quantitative information that you can provide that would lead to this result is

appreciated.

209

Responses are requested via email to Tripp Shenton ([email protected]) by 1/11/18.

Any questions or comments regarding this request for information or the research project

can be directed to Jennifer McConnell at [email protected] / 302-668-6772. We

sincerely appreciate your participation in this effort.

Sincerely,

Jennifer McConnell

On behalf of Robert Zobel and the LTBPP team

E.2 Follow-Up Survey for Prior Participants

In the follow-up survey, the survey was customized based on the participants previous

responses. The template below indicates the possible questions that were asked, where

question numbers followed with a capital letter indicate that one of these options was

possibly asked.

Dear [State Coordinator],

Thank you for your assistance in completing our survey related to deicing agent use and

washing of weathering steel bridges within your agency.

In analyzing the data received from all agencies on bridge washing, we have developed

categorical responses to organize agencies in different categories. Based on the responses

you previously submitted, we have entered the following information for your agency.

Please review this information and either confirm we have correctly categorized your

agency or let us know of any corrections that should be made.

In terms of deicing agent usage:

1A. We have concluded based on the data you provided that you have an annual use

of [Value] tons of chloride-based solid deicing agents and [Value] gallons of

brine. We know that you also reported other deicing materials that are used by

your agency, however we are only concerned with deicing materials that pose

corrosive issues to weathering steel.

1B. You reported that your agency uses deicing agents, but did not provide quantities.

Can you please provide annual quantities of the following materials:

a. Tons of chloride compounds

b. Gallons of brine containing chloride (in addition to the chloride quantities

reported in Part a, i.e., the same quantity of chloride should only be

reported once)

c. Other (please specify quantity and type(s))

1C. We have compared the deicing agent usage per lane mile in the 12 state agencies

where it was possible for us to do so. The average amount of chloride-containing

solids was 17 tons / lane mile and the average amount of chloride-containing

brine was 65 gal / lane mile. Your agency's average deicing agent use of [Value]

tons of chloride-containing solids/lane mile and [Value] gallons of chloride-

mailto:[email protected]

mailto:[email protected]

210

containing brine / lane mile represents [Value (%)] and [Value (%)] of these

average values, respectively. The state agencies in this comparator group are CO,

CT, DE, IL, IA, NE, NH, NY, ND, OR, RI, and WI. Could you please either

confirm these values are reasonable from your perspective or provide an updated

estimate? We know that you also reported other deicing materials that are used by

your agency, however we are only concerened with deicing materials that pose

corrosive issues to weathering steel.

2. We have assumed that the quantity of brine is in addition to the quantity of solid

deicing agents. In other words, the chloride compounds used to make the brine are

not otherwise represented in the data. Is this correct?

3. Please provide your best estimate of the number of lane miles to which these

deicing agents are applied.

In terms of washing maintenance practices:

1. You reported that your agency washes bridges. Do you have different washing

practices for uncoated weathering steel bridges and other bridges? (Yes or No)

If you answered “yes” to the previous question, please answer the following questions

based on uncoated weathering steel bridges. If you answered “no” to the previous

question, please answer the following questions based on all bridges:

2. Approximately what percentage of your bridges do you wash? Please choose one

(<10%, 10%-50%, or >50%)

3. How frequently do you wash your bridges? Please choose one (more than once

per year, annually, every two years, less frequently, never)

4. Is there a specific time period in which your uncoated weathering steel bridges are

washed? Please choose one (Spring, none, other [please specify])

5. Do you wash the girders of your bridges? Please choose one (Yes (always),

Mostly (at least half of the time), Rarely (less than half of the time), or No

(never))

Your response to this survey is urgently requested by 9/16/19. Any questions or

comments regarding this request for information or the research project can be directed to

Jennifer McConnell at [email protected] / 302-668-6772. We sincerely appreciate your

participation in this effort.

Appreciatively,

Jennifer McConnell

On behalf of Robert Zobel and the LTBPP team

Jennifer McConnell

Associate Professor

Department of Civil and Environmental Engineering

University of Delaware

[email protected]

211

302-668-6772

E.3 Follow-Up Survey for Agencies with No Prior Response

Deicing Agent Use

1. Does your agency use any salts or chemicals for deicing or snow removal? (Yes

or No)

2. How many tons of chloride containing compounds does your agency use

annually?

3. How many gallons of brine containing chloride does your agency use annually?

(Note: Please report values in addition to any quantities reported in the previous

question. In other words, the same quantity of chloride should only be reported

once.)

4. How many lane miles are treated with these quantities of deicing agents?

5. Do you have more detailed information available regarding your agency's use of

deicing agents? If so, please upload this data with a file name clearly identifying

the name of the agency you represent.

6. Is there any other information related to your agency's use of deicing agents that

you believe would be helpful in our effort to estimate the quantity of chlorides

applied to specific bridges in your agency?

Bridge Washing Practices

1. Approximately what percentage of your UWS bridges does your agency wash?

(None, < 10%, 10-50%, > 50%)

Please answer the following questions considering your agencies practices with

uncoated weathering steel (UWS, i.e., weathering steel that has not been painted or

treated with any other coating system)

2. How frequently do you wash your UWS bridges? (More than once per year,

Annually, Every two years, Less Frequently)

3. Is there a specific time period in which your UWS bridges are washed? (No, Yes

[Spring], Yes [Other])

4. Do your washing practices for UWS bridges include the girders? (Yes [Always],

Mostly [at least half of the time], Rarely [less than half of the time], No [never])

5. Do you have different washing practices for UWS bridges and other bridges? (No

or Other [Please specify…])

Maintenance Manuals

1. Does your agency have a maintenance manual? (Yes or No)

If your agency has a maintenance manual please upload the manual here.

212

Appendix F

SURVEY DATA

Table F.1 Maintenance Manual Responses

Agency

Response

with

Manual

Response

with

Working

on

Manual

Response

with No

Manual

No

Response

Manual

from Other

Work1

Alabama — — X — X

Alaska — — — X —

Arizona X — — — —

Arkansas — X — — X

California X — — — —

Colorado X — — — —

Connecticut X — — — —

Delaware — — X — X

Florida X — — — —

Georgia — — — X X

Hawaii — — — X X

Idaho — — — X —

Illinois — X — — —

Indiana X — — — —

Iowa X — — — —

Kansas — — — X —

Kentucky — — — X —

Louisiana — — — X —

Maine X — — — —

Maryland — — X — X

Massachusetts — — — X —

Michigan — X — — X

Minnesota X — — — —

Mississippi — — — X —

Missouri X — — — —

Montana — — — X X

213

Table F.1 Continued

Agency

Response

with

Manual

Response

with

Working

on

Manual

Response

with No

Manual

No

Response

Manual

from Other

Work1

Nebraska — — X — X

Nevada — — — X X

New Hampshire X — — — —

New Jersey — — — X X

New Mexico X — — — —

New York X — — — —

North Carolina — — — X —

North Dakota X — — — —

Ohio X — — — —

Oklahoma — — — X —

Oregon — X — — —

Pennsylvania X — — — —

Peurto Rico — — — X —

Rhode Island — — X — —

South Carolina — — — X —

South Dakota — — X — X

Tennessee — — — X —

Texas X — — — —

Utah — — — X X

Vermont — — X — —

Virginia X — — — —

Washington X — — — —

Washington, DC — — X — —

West Virginia — — — X —

Wisconsin X — — — —

Wyoming X— — — —

Total 21 4 8 19 13

X = relevant information

— = no relevant information

1 manual obtained from other work, not via the survey (Shenton, 2016)

214

Table F.2 Washing Practices Responses

AgencyWash

(Y/N)1

Percent

(%)Frequency Time Period

Wash

Girders2

Difference

Between

UWS and

Other Bridges

(Y/N)1

Alabama N 0 — — — —

Arizona N 0 — — — —

Arkansas Y <10 Less frequently Spring No N

California N 0 — — — —

Colorado N 0 — — — —

Connecticut N 0 — — — —

Delaware N 0 — — — —

Florida N 0 — — — —

Illinois Y <10 Annually Other (by contract) Rarely N

Indiana Y >50 Annually Spring No N

Iowa Y <10 Less frequently Spring Rarely Y

Maine Y >50 Annually Spring No N

Maryland N 0 — — — —

Michigan Y <10 Annually Spring Rarely N

Minnesota Y >50 Annually Spring Typically N

Missouri Y 10-50 Annually Spring Rarely N

Montana N 0 — — — —

Nebraska Y <10 Annually — Rarely Y

New Hampshire Y 10-50 Every 2 years Spring Rarely N

New Mexico N 0 — — — —

New York Y 10-50 Every 2 years Spring No Y

North Dakota Y 10-50 Annually Spring Rarely N

Ohio Y 10-50 Annually Spring No N

Oregon Y — Every 2 years — — —

Pennsylvania Y 10-50 Less frequently — No Y

Rhode Island Y 10-50 Every 2 years Spring Yes N

South Dakota Y >50 Annually Spring No N

Texas Y — Less frequently — — —

Virginia Y <10 Annually Spring No N

Washington Y <10 Annually Spring Yes Y

Washington, DC Y — Less frequently — — —

Wisconsin Y 10-50 Every 2 years Spring Rarely N

Wyoming N 0 — — — —

2 Yes = always; Typically = at least half of the time; Rarely = less than half of the time; and No = never

1 Y = yes and N = no


215

Table F.3 Deicing Agent Usage Responses

Agency

Corrosive

Solids1

(tons)

Corrosive

Solids1/Lane

Mile

(tons/lane

mile)

Corrosive

Brines2

(gal.)

Corrosive

Brines2/Lane

Mile

(gal./lane

mile)

Number of

Lane Miles

Deicing Agenst

Applied to

Alabama3 26,260 0.9 357,000 12.2 29,273

Alaska3 6,203 0.5 1,169,000 99.4 11,766

Arizona 19,008 1.4 198,956 14.2 14,000

Arkansas — — — — —

California3 35,000 0.7 1,350,000 26.6 50,679

Colorado 173,243 7.5 11,470,846 498.7 23,000

Connecticut3 221,450 20.4 1,534,050 141.1 10,870

Delaware3 108,000 8.0 2,539,000 188.5 13,472

Florida 0 0.0 0 0.0 —

Georgia3 20,863 0.5 800,000 20.0 39,919

Hawaii — — — — —

Idaho3 116,828 9.5 9,335,189 757.7 12,320

Illinois6 550,230 12.1 1,963,470 43.0 45,617/25,801

Indiana 225,000 7.5 0 0.0 30,000

Iowa 140,000 5.7 28,600,000 1,167.3 24,500

Kansas3 96,000 3.8 3,918,000 154.9 25,300

Kentucky3 241,000 3.8 1,494,800 23.5 63,500

Louisiana — — — — —

Maine 140,000 17.9 550,000 70.5 7,800

Maryland4 210,193 12.2 — — 17,203

Massachusetts3 455,885 29.5 1,772,200 114.8 15,436

Michigan3 619,043 19.3 2,361,691 73.7 32,045

Minnesota 246,500 8.1 4,600,000 151.2 30,426

Mississippi — — — — —

Missouri3 145,000 1.9 3,896,000 50.2 77,570

Montana3 1,858 0.1 10,169,485 406.8 25,000

Nebraska3 104,729 4.5 1,040,104 44.9 23,168

Nevada — — — — —

New Hampshire3 231,257 24.7 386,011 41.2 9,366

New Jersey — — — — —

New Mexico 741 — 0 — —

New York 840,340 22.9 1,104,830 30.1 36,704

North Carolina — — — — —

North Dakota5 26,255 2.6 — 114.0 17,255

216

Table F.3 Continued

Agency

Corrosive

Solids1

(tons)

Corrosive

Solids1/Lane

Mile

(tons/lane

mile)

Corrosive

Brines2

(gal.)

Corrosive

Brines2/Lane

Mile

(gal./lane

mile)

Number of

Lane Miles

Deicing Agenst

Applied to

Ohio 759,826 17.4 10,787,372 247.6 43,570

Oklahoma — — — — —

Oregon 1,108 0.1 264,609 25.4 10,405

Pennsylvania 928,081 10.9 11,682,400 137.6 84,903

Puerto Rico — — — — —

Rhode Island3 154,000 48.4 14,800 4.6 3,185

South Carolina — — — — —

South Dakota 60,016 6.8 1,940,850 219.4 8,847

Tennessee — — — — —

Texas3 22,230 0.1 5,815,454 30.9 188,128

Utah3 260,105 10.6 273,422 11.2 24,500

Vermont3 173,365 26.6 2,853,974 438.3 6,511

Virginia — — — — —

Washington 82,000 2.3 2,364,000 67.5 35,000

Washington, DC — — — — —

West Virginia3 281,342 3.8 1,093,151 14.6 75,000

Wisconsin3 567,696 16.4 6,053,329 174.6 34,678

Wyoming7 222,668 11,793,705 770,000/40,000

7 Wyoming's reported lane miles is believed to be the cumulative number of lane miles that

were treated throughout the year rather than the number of lane miles that Wyoming

maintains given the extremely high value of lane miles for corrosive solids. Because of this,

Wyoming's data was not reported in quantities per lane mile.


1 corrosive solids include chloride containing chemicals such as, sodium chloride,

magnesium chloride, and calcium chloride

2 corrosive brines include brines containing chloride chemicals such as, sodium chloride brine,

magnesium chloride brine, calcium chloride brine, and prewetting brine

3 data obtained from Clear Roads State Winter Maintenance Data and Statistics

4 Maryland reported amounts of corrosive solids (sodium chloride) used to make corrosive

brine.

5 North Dakota reported 2.6 tons of corrosive solids/lane mile, however this value does not

match the calculated amount of 1.5 tons of corrosive solids/lane mile determined by dividing the

reported 26,255 tons of corrosive solids by the reported 17,255 lane miles. The reported value

of 2.6 tons of corrosive solids/lane mile was used in the data set.

6 Illinois' reported data had differences in the amount of lane miles that corrosive solids and

corrosive brines were applied to (ie. 45,617 lane miles for corrosive solids and 25,801 lane

miles for corrosive brines).

217

Table F.4 Deicing Agent Usage Statistics

Statistic

Corrosive

Solids1

(tons)

Corrosive

Solids1/Lane

Mile

(tons/lane

mile)

Corrosive

Brines2

(gal.)

Corrosive

Brines2/Lane

Mile

(gal./lane

mile)

Mean 218290 10.0 3933722 156.0

Standard Deviation 241643 10.6 5604680 238.6

Max 928081 48.4 28600000 1167.3

Min 0 0.0 0 0.0

Median 145000 7.5 1772200 69.01 corrosive solids include chloride containing chemicals such as, sodium

chloride, magnesium chloride, and calcium chloride2 corrosive brines include brines containing chloride chemicals such as,

sodium chloride brine, magnesium chloride brine, calcium chloride brine, and

prewetting brine

218

Appendix G

TAPE SAMPLE RESULTS

G.1 Tape Sample Images

225

G.2 Tape Test Results Data Tables

E-16-JW E-16-JX E-16-JZ Average

1 1.27 6.69 7.40 5.12

2 3.76 7.59 6.06 5.80

3 0.38 6.31 6.51 4.40

4 0.08 8.21 2.65 3.64

5 2.76 5.08 3.92 3.92

6 1.05 6.65 6.35 4.68

7 0.54 7.93 9.23 5.90

8 3.47 5.92 6.42 5.27

9 0.54 6.17 6.72 4.47

10 0.09 4.42 8.91 4.47

11 3.08 5.36 5.84 4.76

12 1.07 5.22 5.19 3.83

Average 1.51 6.30 6.27 4.69


1 0.65 0.00 0.60 0.41

2 0.00 0.00 0.00 0.00

3 0.20 0.00 0.44 0.21

4 0.00 0.00 0.19 0.06

5 0.00 0.00 0.00 0.00

6 0.31 0.00 1.09 0.47

7 0.10 0.00 1.64 0.58

8 0.00 0.00 0.00 0.00

9 0.10 0.09 0.97 0.39

10 0.00 0.00 1.49 0.50

11 0.00 0.00 0.00 0.00

12 0.18 0.00 0.28 0.15

Average 0.13 0.01 0.56 0.23


1 0.32 0.00 0.29 0.20

2 0.00 0.00 0.00 0.00

3 0.00 0.00 0.00 0.00

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 0.00 0.00 0.00 0.00

7 0.00 0.00 0.00 0.00

8 0.00 0.00 0.00 0.00

9 0.00 0.00 0.00 0.00

10 0.00 0.00 0.17 0.06

11 0.00 0.00 0.00 0.00

12 0.00 0.00 0.00 0.00

Average 0.03 0.00 0.04 0.02


1 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00

3 0.00 0.00 0.00 0.00

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 0.00 0.00 0.00 0.00

7 0.00 0.00 0.00 0.00

8 0.00 0.00 0.00 0.00

9 0.00 0.00 0.00 0.00

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 0.00 0.00 0.00 0.00

Average 0.00 0.00 0.00 0.00

CO

Standard Sample

Area Location ID

Density (%)

Standard Sample

Area Location ID

Percent Area ≥ 1/4" (%)

Standard Sample

Area Location ID


Standard Sample

Area Location ID


226

3830 4382 5796 Average

1 14.28 24.09 7.40 15.26

2 12.64 13.89 6.06 10.86

3 12.58 22.49 6.51 13.86

4 6.57 11.41 2.65 6.88

5 6.82 10.92 3.92 7.22

6 15.01 17.62 6.35 13.00

7 17.39 16.97 9.23 14.53

8 10.95 16.55 6.42 11.31

9 15.13 17.97 6.72 13.27

10 8.04 12.36 8.91 9.77

11 8.59 12.88 5.84 9.10

12 10.16 20.46 5.19 11.94

Average 11.51 16.47 6.27 11.42

3830 4382 5796 Average

1 11.30 18.88 8.31 12.83

2 5.70 9.67 0.04 5.13

3 8.78 15.47 4.81 9.69

4 3.20 7.70 8.98 6.63

5 1.21 6.67 3.32 3.73

6 12.58 13.89 8.07 11.51

7 12.62 15.11 9.52 12.42

8 4.29 13.11 0.05 5.81

9 10.84 13.30 4.89 9.68

10 3.53 8.70 9.54 7.26

11 2.93 9.05 0.07 4.02

12 4.85 15.27 7.16 9.09

Average 6.82 12.23 5.40 8.15

3830 4382 5796 Average

1 8.64 15.89 6.08 10.21

2 1.36 3.15 0.00 1.51

3 4.95 8.94 2.83 5.57

4 0.87 4.83 7.88 4.52

5 0.00 2.26 0.16 0.81

6 9.66 9.93 4.13 7.91

7 8.92 13.54 7.91 10.12

8 0.63 8.91 0.00 3.18

9 6.17 8.22 2.75 5.71

10 0.38 5.44 7.02 4.28

11 0.00 3.77 0.00 1.26

12 2.53 11.51 4.33 6.12

Average 3.68 8.03 3.59 5.10

3830 4382 5796 Average

1 5.40 8.58 2.57 5.52

2 0.00 0.00 0.00 0.00

3 0.78 2.80 0.00 1.19

4 0.00 0.00 5.41 1.80

5 0.00 0.00 0.00 0.00

6 3.87 3.32 0.00 2.40

7 3.02 10.37 3.91 5.77

8 0.00 0.69 0.00 0.23

9 0.00 2.02 1.13 1.05

10 0.00 0.00 2.95 0.98

11 0.00 0.45 0.00 0.15

12 0.00 5.67 0.59 2.09

Average 1.09 2.82 1.38 1.76

CT

Standard Sample

Area Location ID

Density (%)

Standard Sample

Area Location ID


Standard Sample

Area Location ID


Standard Sample

Area Location ID


227

004111 041331 042711 Average

1 2.93 10.77 3.60 5.77

2 10.50 9.59 10.55 10.21

3 9.32 16.12 7.34 10.93

4 4.75 2.33 2.78 3.29

5 8.30 5.94 5.32 6.52

6 7.71 3.65 4.85 5.40

7 2.29 5.92 4.38 4.20

8 10.72 20.04 10.51 13.75

9 5.39 13.25 7.53 8.73

10 3.38 2.30 2.14 2.61

11 7.65 4.34 2.87 4.95

12 10.43 6.47 4.84 7.25

Average 6.95 8.39 5.56 6.97

004111 041331 042711 Average

1 1.25 8.20 0.92 3.46

2 4.79 7.04 1.81 4.55

3 6.13 12.92 3.68 7.58

4 0.69 0.18 0.00 0.29

5 0.99 0.39 0.14 0.50

6 5.83 1.20 2.69 3.24

7 0.78 3.90 1.82 2.17

8 4.82 17.09 1.92 7.95

9 3.78 9.77 4.02 5.86

10 0.17 0.18 0.11 0.15

11 0.76 0.31 0.03 0.37

12 6.88 5.02 2.35 4.75

Average 3.07 5.52 1.63 3.41

004111 041331 042711 Average

1 0.51 6.00 0.14 2.22

2 1.42 4.83 0.00 2.08

3 4.78 10.56 1.90 5.75

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 4.09 0.44 2.21 2.25

7 0.12 3.00 0.95 1.36

8 1.00 15.43 0.00 5.48

9 2.29 6.28 2.12 3.56

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 4.51 4.45 1.18 3.38

Average 1.56 4.25 0.71 2.17

004111 041331 042711 Average

1 0.00 2.49 0.00 0.83

2 0.00 0.00 0.00 0.00

3 2.01 4.87 0.00 2.30

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 3.20 0.00 1.37 1.52

7 0.00 1.13 0.00 0.38

8 0.00 11.54 0.00 3.85

9 0.00 2.09 0.00 0.70

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 1.04 3.87 0.00 1.64

Average 0.52 2.17 0.11 0.93

IA

Standard Sample

Area Location ID

Density (%)

Standard Sample

Area Location ID


Standard Sample

Area Location ID


Standard Sample

Area Location ID


228

04019 19811 62861 Average

1 6.21 39.49 12.28 19.33

2 12.01 16.21 40.76 23.00

3 2.71 49.03 18.56 23.43

4 — 3.45 9.33 6.39

5 5.27 7.29 8.97 7.18

6 0.88 10.78 10.34 7.33

7 7.29 29.99 16.43 17.91

8 5.31 7.02 31.64 14.66

9 2.81 19.15 34.98 18.98

10 — 3.29 5.34 4.31

11 3.31 8.07 7.74 6.37

12 20.43 7.91 20.86 16.40

Average 6.62 16.81 18.10 14.27

04019 19811 62861 Average

1 3.58 37.62 10.29 17.16

2 8.24 12.04 36.48 18.92

3 1.62 46.95 14.69 21.08

4 — 1.29 8.56 4.93

5 0.48 1.01 8.27 3.25

6 0.53 8.98 8.74 6.08

7 3.41 27.35 14.79 15.18

8 0.32 6.28 26.20 10.93

9 1.31 15.51 29.57 15.46

10 — 1.45 3.99 2.72

11 0.43 2.00 6.48 2.97

12 5.87 6.70 15.36 9.31

Average 2.58 13.93 15.28 11.07

04019 19811 62861 Average

1 1.26 36.34 8.43 15.34

2 3.41 5.04 34.45 14.30

3 0.87 45.30 12.36 19.51

4 — 0.00 7.59 3.80

5 0.00 0.00 7.47 2.49

6 0.28 8.28 7.13 5.23

7 0.26 25.42 13.15 12.94

8 0.00 4.63 22.65 9.09

9 0.34 13.19 25.03 12.85

10 — 0.72 2.87 1.80

11 0.00 0.00 4.93 1.64

12 14.28 6.27 12.52 11.02

Average 2.07 12.10 13.21 9.54

04019 19811 62861 Average

1 0.00 34.78 6.63 13.80

2 0.00 0.00 31.25 10.42

3 0.00 43.89 8.12 17.34

4 — 0.00 6.75 3.37

5 0.00 0.00 5.36 1.79

6 0.00 7.85 3.62 3.82

7 0.00 23.62 10.99 11.54

8 0.00 0.73 15.35 5.36

9 0.00 7.84 20.08 9.31

10 — 0.00 1.19 0.59

11 0.00 0.00 3.32 1.11

12 12.50 5.70 7.07 8.43

Average 1.25 10.37 9.98 7.55

Standard Sample

Area Location ID

Density (%)

MN

Standard Sample

Area Location ID


Standard Sample

Area Location ID


Standard Sample

Area Location ID


229

190083 1290057 1290058 Average

1 11.18 25.75 40.62 25.85

2 10.15 10.34 10.42 10.30

3 14.20 40.35 43.85 32.80

4 5.40 5.27 9.04 6.57

5 5.96 3.77 6.88 5.54

6 8.77 21.87 44.14 24.93

7 9.83 29.00 29.67 22.83

8 10.04 10.38 10.37 10.26

9 9.27 29.11 48.00 28.79

10 6.23 3.94 8.60 6.26

11 7.28 4.55 5.01 5.62

12 10.44 31.93 32.18 24.85

Average 9.06 18.02 24.07 17.05

190083 1290057 1290058 Average

1 2.34 16.94 36.08 18.45

2 1.21 0.91 0.87 1.00

3 5.75 34.24 39.80 26.60

4 0.44 0.00 0.74 0.39

5 0.30 0.00 0.25 0.18

6 1.97 13.61 38.93 18.17

7 2.11 20.38 22.24 14.91

8 1.36 0.92 1.09 1.13

9 2.05 19.82 43.20 21.69

10 0.31 0.06 0.22 0.19

11 0.88 0.00 0.00 0.29

12 2.25 26.95 25.39 18.20

Average 1.75 11.15 17.40 10.10

190083 1290057 1290058 Average

1 0.12 11.93 33.44 15.17

2 0.00 0.00 0.00 0.00

3 1.68 30.59 37.20 23.16

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 0.00 8.30 35.70 14.67

7 0.14 15.79 19.73 11.89

8 0.00 0.00 0.00 0.00

9 0.00 12.34 41.59 17.98

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 0.00 24.04 22.45 15.50

Average 0.16 8.58 15.84 8.20

190083 1290057 1290058 Average

1 0.00 7.65 29.97 12.54

2 0.00 0.00 0.00 0.00

3 0.00 27.07 33.15 20.07

4 0.00 0.00 0.00 0.00

5 0.00 0.00 0.00 0.00

6 0.00 2.91 30.73 11.21

7 0.00 11.77 15.06 8.94

8 0.00 0.00 0.00 0.00

9 0.00 6.37 40.88 15.75

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 0.00 18.51 18.63 12.38

Average 0.00 6.19 14.03 6.74

Standard Sample

Area Location ID

Density (%)

NC

Standard Sample

Area Location ID

Standard Sample

Area Location ID



Standard Sample

Area Location ID


230

017201120011300 11101120017900 017701460003700 Average

1 8.88 1.83 5.30 5.34

2 3.36 25.42 16.67 15.15

3 5.64 2.62 4.52 4.26

4 5.58 5.03 4.11 4.90

5 3.33 8.58 9.69 7.20

6 5.41 2.73 24.65 10.93

7 9.80 2.39 51.65 21.28

8 4.74 28.52 16.10 16.45

9 8.02 2.38 60.36 23.59

10 5.60 5.35 3.02 4.66

11 3.65 12.31 10.31 8.76

12 5.02 2.77 4.98 4.26

Average 5.75 8.33 17.61 10.56

017201120011300 11101120017900 017701460003700 Average

1 4.04 0.00 2.82 2.29

2 0.00 23.99 6.52 10.17

3 1.77 0.39 3.28 1.81

4 2.89 1.01 1.93 1.95

5 0.00 5.48 4.39 3.29

6 2.57 0.16 21.77 8.17

7 4.46 0.21 49.74 18.14

8 0.00 27.19 6.31 11.17

9 3.10 0.20 57.43 20.24

10 2.68 1.04 1.19 1.64

11 0.04 7.18 5.84 4.36

12 2.09 0.08 1.44 1.20

Average 1.97 5.58 13.56 7.03

017201120011300 11101120017900 017701460003700 Average

1 1.51 0.00 1.28 0.93

2 0.00 21.95 0.40 7.45

3 0.48 0.31 2.54 1.11

4 1.27 0.00 0.56 0.61

5 0.00 3.32 1.22 1.51

6 1.56 0.00 20.54 7.37

7 0.91 0.00 48.66 16.52

8 0.00 26.07 0.54 8.87

9 0.86 0.00 55.29 18.72

10 0.56 0.00 0.14 0.24

11 0.00 1.66 1.73 1.13

12 0.35 0.00 0.57 0.30

Average 0.63 4.44 11.12 5.40

017201120011300 11101120017900 017701460003700 Average

1 0.75 0.00 0.00 0.25

2 0.00 16.00 0.00 5.33

3 0.00 0.00 1.16 0.39

4 0.00 0.00 0.00 0.00

5 0.00 0.70 0.00 0.23

6 0.65 0.00 17.44 6.03

7 0.00 0.00 46.26 15.42

8 0.00 22.11 0.00 7.37

9 0.00 0.00 54.12 18.04

10 0.00 0.00 0.00 0.00

11 0.00 0.00 0.00 0.00

12 0.00 0.00 0.00 0.00

Average 0.12 3.23 9.92 4.42

NH

Standard Sample

Area Location ID

Density (%)

Standard Sample

Area Location ID


Standard Sample

Area Location ID


Standard Sample

Area Location ID


231

Standard Sample

7700105 7701977 7701993 Average

1 3.21 36.29 14.72 18.07

2 16.11 24.15 9.89 16.72

3 5.92 19.79 19.73 15.15

4 4.73 8.03 7.21 6.66

5 13.98 10.53 4.74 9.75

6 14.17 4.29 14.05 10.84

7 2.53 30.46 17.91 16.97

8 5.26 15.82 14.39 11.82

9 9.62 36.66 17.07 21.12

10 3.99 9.49 7.25 6.91

11 10.42 11.89 8.10 10.14

12 5.39 9.50 16.24 10.38

Average 7.95 18.07 12.61 12.88

7700105 7701977 7701993 Average

1 0.60 27.30 11.67 13.19

2 14.53 16.21 1.19 10.64

3 2.80 10.75 17.18 10.24

4 1.73 1.66 2.52 1.97

5 12.00 0.19 0.03 4.08

6 11.54 0.38 12.44 8.12

7 0.18 28.33 14.05 14.19

8 4.31 10.06 1.67 5.35

9 7.41 33.35 14.41 18.39

10 1.65 2.38 3.68 2.57

11 8.50 0.50 0.10 3.03

12 1.87 7.63 9.41 6.30

Average 5.59 11.56 7.36 8.17

7700105 7701977 7701993 Average

1 0.00 23.76 9.94 11.23

2 13.21 7.61 0.00 6.94

3 2.08 7.74 14.26 8.03

4 0.93 0.00 0.44 0.46

5 9.74 0.00 0.00 3.25

6 10.03 0.00 10.12 6.72

7 0.00 26.40 12.23 12.88

8 3.41 2.84 0.11 2.12

9 6.38 30.58 11.92 16.30

10 0.62 0.00 1.72 0.78

11 6.73 0.00 0.00 2.24

12 0.48 5.62 6.61 4.23

Average 4.47 8.71 5.61 6.26

7700105 7701977 7701993 Average

1 0.00 20.34 6.65 8.99

2 7.92 0.00 0.00 2.64

3 1.42 3.02 4.57 3.00

4 0.00 0.00 0.00 0.00

5 6.44 0.00 0.00 2.15

6 8.86 0.00 2.54 3.80

7 0.00 21.90 8.55 10.15

8 1.11 0.00 0.00 0.37

9 4.92 23.81 5.47 11.40

10 0.00 0.00 0.00 0.00

11 2.53 0.00 0.00 0.84

12 0.00 1.57 2.90 1.49

Average 2.77 5.89 2.56 3.74

Density (%)

OH

Standard Sample

Area Location ID


Standard Sample

Area Location ID


Standard Sample

Area Location ID


232

G.3 Tape Test Results Standard Deviations

Table G.1 Tape Test Results Cluster Standard Deviations

ClusterDensity

(%)

Percent Area

≥ 1/8" (%)

Percent Area

≥ 1/4" (%)

Percent Area

≥ 1/2" (%)

CO 2.70 0.43 0.07 0.00

CT 4.63 4.83 4.21 2.62

IA 4.13 3.90 3.34 2.20

MN 12.46 12.11 11.74 11.10

NC 13.68 14.12 13.21 11.80

NH 13.05 13.20 13.09 12.44

OH 8.52 8.55 7.85 6.25

233

Table G.2 Tape Test Results Field Bridge Standard Deviations

Field BridgeDensity

(%)

Percent Area

≥ 1/8" (%)

Percent Area

≥ 1/4" (%)

Percent Area

≥ 1/2" (%)

CO E-16-JW 1.37 0.19 0.09 0.00

CO E-16-JX 1.19 0.02 0.00 0.00

CO E-16-JZ 1.84 0.60 0.09 0.00

CT 3830 3.56 4.14 3.78 1.90

CT 4382 4.34 3.79 4.28 3.58

CT 5796 2.65 3.77 3.13 1.87

IA 004111 3.09 2.53 1.89 1.05

IA 041331 5.68 5.63 4.87 3.40

IA 042711 2.85 1.41 0.92 0.40

MN 04019 5.75 2.70 4.41 3.95

MN 19811 14.99 15.37 15.35 15.26

MN 62861 11.74 10.18 9.44 8.53

NC 190083 2.52 1.48 0.48 0.00

NC 1290057 13.05 12.39 10.63 8.85

NC 1290058 17.15 18.53 17.55 16.02

NH 017201120011300 2.11 1.62 0.59 0.27

NH 11101120017900 9.26 9.65 9.24 7.51

NH 017701460003700 19.15 19.54 19.95 19.53

OH 7700105 4.73 5.01 4.61 3.37

OH 7701977 11.32 12.07 11.46 9.80

OH 7701993 4.95 6.42 5.76 3.08

234

Table G.3 Tape Test Results Standard Sample Area Location Standard Deviations

G.4 Tape Test Results Graphs

Figure G.1 Average Density of Rust Particles, by Cluster

Standard Sample

Area Location ID

Density

(%)

Percent Area

≥ 1/8" (%)

Percent Area

≥ 1/4" (%)

Percent Area

≥ 1/2" (%)

1 12.75 11.85 11.39 10.59

2 8.63 9.61 9.25 8.14

3 14.21 13.80 13.42 13.09

4 2.72 2.96 2.64 2.03

5 2.93 3.50 2.83 1.91

6 10.30 9.58 8.90 7.82

7 13.28 13.10 12.95 12.15

8 7.61 8.56 8.19 6.47

9 16.39 15.97 15.72 15.63

10 3.14 2.86 2.08 0.76

11 3.15 3.28 2.03 0.95

12 8.99 7.82 7.41 6.18

4.69

12.41

6.97

14.27

17.05

10.56

12.88

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

CO(good

deicing)

CT(inferior

deicing & coastal)

IA(inferiordeicing)

MN(inferiordeicing)

NC(good & inferior

coastal)

NH(good

deicing & coastal)

OH(good

deicing)

Per

cent A

rea

(%)

Cluster

235

Figure G.2 Average Percent Area of Rust Particles Greater than or Equal to an 1/8 inch,

by Cluster

Figure G.3 Average Percent Area of Rust Particles Greater than or Equal to a 1/4 inch, by

Cluster

0.23

8.15

3.41

11.0710.10

7.038.17

0.00

5.00

10.00

15.00

20.00

25.00

CO(good

deicing)

CT(inferior

deicing & coastal)

IA(inferior

deicing)

MN(inferior

deicing)

NC(good & inferior

coastal)

NH(good

deicing & coastal)

OH(good

deicing)

Per

cen

t A

rea

(%)

Cluster

0.02

5.10

2.17

9.54

8.20

5.406.26

0.00

5.00

10.00

15.00

20.00

25.00

CO(good

deicing)

CT(inferior

deicing & coastal)

IA(inferiordeicing)

MN(inferiordeicing)

NC(good & inferior

coastal)

NH(good

deicing & coastal)

OH(good

deicing)

Per

cent A

rea

(%)

Cluster

236


Cluster

Figure G.5 Average Density of Rust Particles, by Field Bridge

0.00

1.760.93

7.556.74

4.423.74

0.00

5.00

10.00

15.00

20.00

25.00

CO(good

deicing)

CT(inferior

deicing & coastal)

IA(inferiordeicing)

MN(inferiordeicing)

NC(good & inferior

coastal)

NH(good

deicing & coastal)

OH(good

deicing)

Per

cen

t A

rea

(%)

Cluster

1.51

6.30 6.27

11.51

16.47

9.246.95

8.395.56 6.62

16.8118.10

9.06

18.02

24.07

5.758.33

17.61

7.95

18.07

12.61

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

CO

E-1

6-JW

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NC 1

2900

58

NH 0

1720

1120

0113

00

NH 1

1101

1200

1790

0

NH 0

1770

1460

0037

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Per

cent A

rea

(%)

Field Bridge

237


by Field Bridge


Field Bridge

0.13 0.01 0.56

6.82

12.23

5.403.07

5.521.63 2.58

13.9315.28

1.75

11.15

17.40

1.975.58

13.56

5.59

11.56

7.36

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

CO

E-1

6-JW

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NC 1

2900

58

NH 0

1720

1120

0113

00

NH 1

1101

1200

1790

0

NH 0

1770

1460

0037

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Per

cen

t A

rea

(%)

Field Bridge

0.03 0.00 0.04

3.68

8.03

3.59

1.56

4.25

0.712.07

12.1013.21

0.16

8.58

15.84

0.63

4.44

11.12

4.47

8.71

5.61

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

CO

E-1

6-JW

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NC 1

2900

58

NH 0

1720

1120

0113

00

NH 1

1101

1200

1790

0

NH 0

1770

1460

0037

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Per

cen

t A

rea

(%)

Field Bridge

238


Field Bridge

Figure G.9 Average Density of Rust Particles, by Standard Sample Area Location

0.00 0.00 0.001.09

2.821.38 0.52

2.170.11

1.25

10.37 9.98

0.00

6.19

14.03

0.12

3.23

9.92

2.77

5.89

2.56

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

CO

E-1

6-JW

CO

E-1

6-JX

CO

E-1

6-JZ

CT 3

830

CT 4

382

CT 5

796

IA 0

0411

1

IA 0

4133

1

IA 0

4271

1

MN 0

4019

MN 1

9811

MN 6

2861

NC 1

9008

3

NC 1

2900

57

NC 1

2900

58

NH 0

1720

1120

0113

00

NH 1

1101

1200

1790

0

NH 0

1770

1460

0037

00

OH 7

7001

05

OH 7

7019

77

OH 7

7019

93

Per

cent A

rea

(%)

Field Bridge

15.13 14.31

16.78

6.247.69

12.35

16.42

13.00

19.11

6.007.53

12.80

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

1 2 3 4 5 6 7 8 9 10 11 12

Per

cent A

rea

(%)


239



Figure G.11 Average Percent Area of Rust Particles Greater than or Equal to a 1/4 inch,


11.23

8.40

12.83

2.56 2.51

9.22

12.83

7.06

15.22

2.40 2.51

8.14

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

1 2 3 4 5 6 7 8 9 10 11 12

Per

cen

t A

rea

(%)


9.18

5.38

10.52

1.43 1.34

7.36

10.95

4.79

12.52

1.15 1.05

6.76

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 2 3 4 5 6 7 8 9 10 11 12

Per

cent A

rea

(%)


240

Figure G.12 Average Percent Area of Rust Particles Greater than or Equal to a 1/2 inch,


6.99

3.07

7.38

0.71 0.69

4.80

8.70

2.86

9.37

0.24 0.35

4.34

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 2 3 4 5 6 7 8 9 10 11 12

Per

cent A

rea

(%)


241

Appendix H

ION CHROMATOGRAPHY ANALYSIS RESULTS

H.1 IC Analysis Results Data Tables


1 — 12680 3460 8070

2 — 11721 6323 9022

3 — 9243 — 9243

4 — 15663 853 8258

5 — 2261 194 1227

6 — — 14621 14621

7 — 16103 11644 13873

8 — 15404 11319 13361

9 — 17074 40054 28564

10 — 21771 2075 11923

11 — 2447 — 2447

12 — 23442 13838 18640

Average — 13437 10438 12009


1 — 1451 525 988

2 — 1326 688 1007

3 — 1710 — 1710

4 — 2313 303 1308

5 — 295 105 200

6 — — 1332 1332

7 — 1948 1296 1622

8 — 1793 1429 1611

9 — 1929 1599 1764

10 — 1901 679 1290

11 — 282 — 282

12 — 2458 1177 1817

Average — 1582 913 1264


1 — 7918 1147 4532

2 — 5930 1679 3804

3 — 3847 — 3847

4 — 3951 0 1976

5 — 1279 169 724

6 — — 1553 1553

7 — 10339 1620 5980

8 — 4316 2941 3629

9 — 3258 7064 5161

10 — 5394 512 2953

11 — 1505 — 1505

12 — 8295 1724 5009

Average — 5094 1841 3545

CO

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Chloride (ppm)

Nitrate (ppm)

Sulfate (ppm)

242

3830 4382 5796 Average

1 1916 3146 1560 2207

2 973 1593 247 938

3 2370 1707 1119 1732

4 2022 5229 2232 3161

5 327 1003 315 548

6 2045 1282 — 1663

7 — 4077 1589 2833

8 1059 — 246 653

9 1986 2767 1566 2106

10 1699 4882 — 3291

11 229 1352 234 605

12 3722 2204 1411 2446

Average 1668 2658 1052 1816

3830 4382 5796 Average

1 420 465 429 438

2 436 369 87 297

3 412 418 420 417

4 438 421 404 421

5 398 368 453 406

6 397 402 — 399

7 — 395 390 393

8 411 — 102 257

9 427 373 419 406

10 424 432 — 428

11 57 369 157 194

12 507 453 427 462

Average 393 406 329 377

3830 4382 5796 Average

1 2837 1507 1359 1901

2 918 399 116 478

3 2640 704 933 1426

4 517 853 1009 793

5 427 391 488 435

6 2555 1116 — 1835

7 — 1356 1547 1452

8 959 — 114 536

9 1955 630 937 1174

10 459 446 — 452

11 62 404 193 220

12 459 1097 1066 874

Average 1253 809 776 952

CT

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Chloride (ppm)

Nitrate (ppm)

Sulfate (ppm)

243

004111 041331 042711 Average

1 6328 5646 8406 6793

2 3589 5545 — 4567

3 4400 7710 6367 6159

4 3378 1538 455 1790

5 2493 2931 1555 2326

6 5889 9697 7072 7553

7 5319 — 9307 7313

8 3697 5311 3864 4291

9 5658 9130 7947 7578

10 3223 1431 424 1693

11 2297 3192 742 2077

12 5336 7368 6807 6504

Average 4300 5409 4813 4825

004111 041331 042711 Average

1 246 477 552 425

2 98 162 — 130

3 227 474 515 406

4 87 547 155 263

5 69 181 226 159

6 239 456 225 306

7 218 — 518 368

8 65 136 124 108

9 251 532 486 423

10 48 222 105 125

11 68 164 230 154

12 227 450 498 392

Average 154 346 330 273

004111 041331 042711 Average

1 987 1395 2635 1672

2 707 3403 — 2055

3 789 1785 2332 1635

4 1251 1936 1132 1440

5 45 640 1521 735

6 1032 2095 2776 1968

7 801 — 2546 1673

8 901 3009 2617 2176

9 964 2080 2568 1870

10 978 1688 919 1195

11 44 572 611 409

12 877 1666 2280 1608

Average 781 1843 1994 1517

IA

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Standard Sample

Area Location ID

Chloride (ppm)

Nitrate (ppm)

Sulfate (ppm)

244

04019 19811 62861 Average

1 798 6247 18795 8613

2 — 2924 6903 4913

3 4148 6517 8599 6421

4 — 1162 7047 4104

5 1960 563 6151 2891

6 6041 6563 6806 6470

7 6666 8709 13808 9728

8 7689 4357 6369 6138

9 4531 10566 12115 9070

10 — 703 8501 4602

11 2326 629 4833 2596

12 7693 4161 10670 7508

Average 4650 4425 9216 6229

04019 19811 62861 Average

1 60 424 303 263

2 — 163 269 216

3 95 462 260 272

4 — 1078 218 648

5 154 241 247 214

6 117 492 260 290

7 335 518 288 380

8 307 187 80 191

9 150 514 291 318

10 — 717 225 471

11 226 261 251 246

12 534 416 255 402

Average 220 456 246 315

04019 19811 62861 Average

1 350 1879 1132 1120

2 — 3549 1066 2307

3 1650 2636 895 1727

4 — 1875 603 1239

5 2479 267 1032 1259

6 2289 2872 640 1934

7 1288 2063 946 1432

8 1151 3635 890 1892

9 1906 3890 1187 2328

10 — 1463 557 1010

11 1285 278 884 816

12 2309 1896 939 1715

Average 1634 2192 898 1569

Sulfate (ppm)

MN

Standard Sample

Area Location ID

Chloride (ppm)

Nitrate (ppm)Standard Sample

Area Location ID

Standard Sample

Area Location ID

245

190083 1290057 1290058 Average

1 763 737 — 750

2 284 436 — 360

3 527 985 — 756

4 521 182 — 352

5 292 208 — 250

6 928 1092 — 1010

7 — 834 — 834

8 375 788 — 582

9 466 910 — 688

10 594 143 — 369

11 298 284 — 291

12 1146 607 — 876

Average 563 601 — 583

190083 1290057 1290058 Average

1 69 547 — 308

2 323 395 — 359

3 409 539 — 474

4 113 100 — 106

5 394 78 — 236

6 431 490 — 461

7 — 600 — 600

8 316 555 — 435

9 435 527 — 481

10 84 78 — 81

11 400 414 — 407

12 460 539 — 499

Average 312 405 — 361

190083 1290057 1290058 Average

1 1028 6260 — 3644

2 365 907 — 636

3 1570 7574 — 4572

4 549 1252 — 901

5 438 186 — 312

6 1620 7249 — 4434

7 — 7108 — 7108

8 411 6718 — 3565

9 1350 7617 — 4483

10 423 744 — 584

11 452 545 — 498

12 2182 6586 — 4384

Average 944 4396 — 2745

Standard Sample

Area Location ID

Chloride (ppm)

Nitrate (ppm)

Sulfate (ppm)

Standard Sample

Area Location ID

Standard Sample

Area Location ID

NC

246

017201120011300 11101120017900 017701460003700 Average

1 2332 — — 2332

2 158 — — 158

3 2731 — — 2731

4 3409 — — 3409

5 115 — — 115

6 2171 — — 2171

7 2498 — — 2498

8 136 — — 136

9 2865 — — 2865

10 3706 — — 3706

11 113 — — 113

12 2840 — — 2840

Average 1923 — — 1923

017201120011300 11101120017900 017701460003700 Average

1 410 — — 410

2 63 — — 63

3 425 — — 425

4 456 — — 456

5 64 — — 64

6 410 — — 410

7 435 — — 435

8 75 — — 75

9 435 — — 435

10 436 — — 436

11 123 — — 123

12 400 — — 400

Average 311 — — 311

017201120011300 11101120017900 017701460003700 Average

1 494 — — 494

2 76 — — 76

3 694 — — 694

4 578 — — 578

5 49 — — 49

6 533 — — 533

7 479 — — 479

8 85 — — 85

9 601 — — 601

10 446 — — 446

11 101 — — 101

12 612 — — 612

Average 396 — — 396

Chloride (ppm)

Nitrate (ppm)

Sulfate (ppm)

Standard Sample

Area Location ID

Standard Sample

Area Location ID

NH

Standard Sample

Area Location ID

247

Standard Sample

7700105 7701977 7701993 Average

1 4085 5375 5192 4884

2 3650 2343 1091 2361

3 5765 5399 7117 6094

4 1822 2430 1649 1967

5 2576 918 689 1394

6 7179 3464 6529 5724

7 3478 4528 3459 3822

8 3322 2026 1446 2264

9 4106 5263 3310 4226

10 1758 1728 2273 1920

11 2778 836 999 1538

12 7863 5805 5079 6249

Average 4032 3343 3236 3537

Standard Sample

7700105 7701977 7701993 Average

1 515 372 428 438

2 363 444 412 406

3 352 435 546 444

4 465 224 243 311

5 442 146 584 391

6 0 483 456 313

7 472 0 394 289

8 468 420 153 347

9 471 452 433 452

10 456 137 89 227

11 369 98 121 196

12 0 446 401 282

Average 365 305 355 341

Standard Sample

7700105 7701977 7701993 Average

1 2600 3885 3877 3454

2 1543 2284 669 1499

3 3481 4638 8916 5678

4 1462 2265 2678 2135

5 542 364 819 575

6 2898 5612 7750 5420

7 1962 2743 2394 2366

8 709 1740 671 1040

9 2500 3775 4129 3468

10 1131 1592 1687 1470

11 606 334 413 451

12 3276 4135 4594 4002

Average 1893 2781 3216 2630

OH

Sulfate (ppm)

Chloride (ppm)

Nitrate (ppm)

248

H.2 IC Analysis Results Standard Deviations

Table H.1 IC Analysis Results Cluster Standard Deviations

Table H.2 IC Analysis Results Field Bridge Standard Deviations

Cluster Chloride Nitrate Sulfate

CO 9363 704 2944

CT 1283 111 726

IA 2651 170 874

MN 4039 202 969

NC 305 181 2939

NH 1388 171 244

OH 2004 168 1996

Field Bridge Chloride Nitrate Sulfate

CO E-16-JX 6823 717 2842

CO E-16-JZ 11742 518 2030

CT 3830 993 115 1036

CT 4382 1497 35 406

CT 5796 734 149 519

IA 004111 1374 86 371

IA 041331 2886 169 851

IA 042711 3475 181 792

MN 04019 2555 150 689

MN 19811 3364 254 1193

MN 62861 4009 58 204

NC 190083 280 150 640

NC 1290057 338 201 3269

NH 017201120011300 1388 171 244

OH 7700105 1961 178 1048

OH 7701977 1855 171 1661

OH 7701993 2255 164 2797

249

Table H.3 IC Analysis Results Standard Sample Area Location Standard Deviations

Standard Sample

Area Location IDChloride Nitrate Sulfate

1 4562 152 1584

2 2154 142 1160

3 2657 123 2509

4 1904 260 684

5 1603 161 635

6 2843 154 2320

7 3721 160 1731

8 2425 166 1807

9 3578 107 1853

10 2295 207 501

11 1386 122 328

12 2912 142 1724

uncoated weathering steel bridge data collection …

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