Correlation Study on the Falling Weight Deflectometer and Light Weight Deflectometer for the Local

Correlation Study on the Falling Weight Deflectometer and Light Weight Deflectometer

for the Local Pavement Systems

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Ahmadudin Burhani

August 2016

© 2016 Ahmadudin Burhani. All Rights Reserved.

2

This thesis titled



by

AHMADUDIN BURHANI

has been approved for

the Department of Civil Engineering

and the Russ College of Engineering and Technology by

Shad M. Sargand

Russ Professor of Civil Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology

3

ABSTRACT

BURHANI, AHMADUDIN, M.S., August 2016, Civil Engineering



Director ofThesis: Shad M. Sargand

The Falling Weight Deflectometer (FWD) and Light Weight Deflectometer (LWD)

are essential nondestructive devices used for structural evaluation and characterization of

pavement layer systems. This study evaluated the performances of both devices in 99

different test sites grouped into five clusters located in eight counties in Ohio. The

structural adequacy of the local roads in Ohio was assessed by conducting field tests using

deflectometry and backcalculation techniques. A field research program consisting of a

series of FWD and LWD tests was undertaken at the same locations to investigate local

pavement performances. The deflection data obtained from test results corresponding to

pavement material properties were used to estimate: in-situ stiffness layer moduli, effective

structural numbers, and a range of structural coefficients for different materials utilized to

widen, construct, and rehabilitate county roads in Ohio. AASHTO 1993 Guide for Design

of Pavement Structures and computer software, Modulus 6.0, Evercalc 5.0 were chosen to

perform the backcalculation analysis.

Specifically, this study investigated the feasibility and potential use of the Prima

100 LWD as in-situ testing device on the local roads. Although the FWD device could be

used for the evaluation of the county roads, the cost of the equipment is prohibitive for

most local agencies. The Prima 100 LWD on the other hand proved to be reasonable and

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effective alternative. However, the application of Prima 100 LWD requires a

methodological correlation with respect to benchmark test. Comparisons were made

through comprehensive regression analyses using the SPSS software. Center and radial

offset sensor deflections as well as backcalculated layer moduli, layer coefficients, and the

effective structural numbers were compared. The correlation results for the layer

coefficients and subgrade modulus across all test sites were improved by the Rohde

method. The results demonstrated consistent relationship between both devices on the

evaluation for the asphalt and concrete surfaces. However, lower relationship for sensor

deflections was reported for aggregate overlay, full depth grinding, and soft soil surfaces.

In the course of this study, a modified relationship between deflection basin

parameter and pavement response was devised. This promising relationship is the Area

Under Pavement Profile (AUPP) which can be used to predict tensile strain at the bottom

of the asphalt concrete layer. The statistical analyses showed the proposed procedure

appears to be a new valid parameter for the pavement evaluation using LWD sensor

deflections.

In the final analysis, the Prima 100 LWD proved to be an effective and

economically viable test procedure for asphalt and concrete surfaces for the evaluation of

local pavement systems.

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DEDICATION

I dedicate this work to my family for giving me support and encouragement throughout

my career

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ACKNOWLEDGMENTS

First of all, I would like to express sincere appreciation and gratitude to my

academic advisor Professor Shad M. Sargand for his continuous support and guidance

throughout my entire research. Your devotion, encouragement, and advice helped me in

realizing my potential and I appreciated any single minute spent on this adventure.

Next, I specifically would like to extend my appreciation and thanks to the rest of

my thesis committee: Dr. Teruhisa Masada, Dr. Issam Khoury, and Dr. Tatiana Savin for

agreeing to be my committee member and for their supportive comments. Also, I give my

deepest thanks to Mr. Roger Green and Mr. Benjamin Jordan for their continuous

cooperation and assistance during my research. Without their supports, this thesis may not

be completed.

Finally, I also would like to thank all my colleagues and civil engineering family in

Ohio University for making my study a memorable adventure here in Athens. I further give

my deepest gratitude and thanks to my family who always encouraged, supported and loved

me. Without their help, I would be unable to accomplish my goals.

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TABLE OF CONTENTS

Page

Abstract ............................................................................................................................... 3

Dedication ........................................................................................................................... 5

Acknowledgments............................................................................................................... 6

List of Tables .................................................................................................................... 10

List of Figures ................................................................................................................... 12

Chapter 1 Introduction ...................................................................................................... 18

1.1 Overview ................................................................................................................18

1.2 Research Goal and Objectives .............................................................................23

1.3 Outline of Thesis .................................................................................................24

Chapter 2 Literature Review ............................................................................................. 26

2.1 Introduction .........................................................................................................26

2.2 The Falling Weight Deflectometer (FWD) .........................................................26

2.2.1 Dynatest Model 8000 FWD ........................................................................ 29

2.2.2 KUAB America .......................................................................................... 32

2.2.3 Carl Bro FWD ............................................................................................. 32

2.2.4 JILS FWD ................................................................................................... 32

2.3 The Light Weight Deflectometer (LWD) ............................................................33

2.3.1 Prima 100 LWD .......................................................................................... 35

2.3.2 The LWD Principle of Operation ............................................................... 36

2.4 Existing Correlations between FWD and LWD ..................................................38

8

2.5 Determination of Pavement Responses Using Deflection Basin Parameter .......42

2.6 Backcalculation of Layer Moduli ........................................................................45

2.6.1 Overview of Backcalculation Software ...................................................... 48

2.6.2 Modulus Program........................................................................................ 50

2.6.3 Evercalc Program ........................................................................................ 50

Chapter 3 Evaluation of Pavement Condition Using FWD and LWD Measurements ..... 53

3.1 Field Testing ........................................................................................................53

3.2 Quantifying Pavement Condition Using FWD Deflections ................................58

3.2.1 FWD Results ............................................................................................... 61

3.3 Quantifying Pavement Condition Using LWD Deflections ................................62

3.3.1 LWD Results ............................................................................................... 64

3.4 Backcalculation Methodology and Pavement Layer Moduli ..............................66

3.4.1 AASHTO Method (Section 5.4.5, FWD) ................................................... 67

3.4.2 Determining Layer Coefficients from AASHTO 5.4.5 Equations ............. 70

3.4.3 AASHTO Method (Section 2.3.5, LWD) ................................................... 74

3.4.4 Rohde’s [1994] Method of Determination of Pavement Structural Number

and Subgrade Modulus from FWD Testing. ............................................... 79

3.4.5 Pavement Layer Moduli .............................................................................. 86

Chapter 4 Results and Discussion ..................................................................................... 94

4.1 Introduction .........................................................................................................94

4.2 Regression Analysis ............................................................................................94

4.3 Comparison FWD and LWD Sensor Deflections ...............................................96

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4.3.1 Deflections at the Center of Loading plate, (D0) ........................................ 96

4.3.2 Deflections at Radial Offset Distance r = 300mm, (D1) ............................. 99

4.3.3 Deflections at Radial Offset Distance r = 600mm, (D2) ........................... 100

4.4 Area Under Pavement Profile (Deflection Basin Parameter) ............................103

4.5 Comparison of Backcalculated Layer Moduli ..................................................106

4.5.1 Comparison of Subgrade Moduli .............................................................. 111

4.6 Comparison of Layer Coefficients ....................................................................114

4.7 Comparison of Effective Structural Numbers ...................................................117

Chapter 5 Conclusion and Recommendations ................................................................ 121

5.1 Summary ...........................................................................................................121

5.2 Conclusion .........................................................................................................121

5.3 Recommendations .............................................................................................125

References ....................................................................................................................... 127

Appendix A: Pavement Layer Thicknesses and Material Properties by County. ........... 132

Appendix B: Typical FWD and LWD Deflection Basins .............................................. 143

Appendix C: AASHTO 5.4.5 Procedure Outputs Using FWD Sensor Deflections ....... 148

Appendix D: Summary of Backcalculated Layer Moduli from FWD and LWD Testing

......................................................................................................................................... 152

Appendix E: FWD and LWD Sensor Deflections .......................................................... 156

Appendix F: Effective Structural Numbers of AASHTO Equations and The Rohde Method

......................................................................................................................................... 161

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LIST OF TABLES

Page

Table 2.1: Sensor Spacing of the FWD Device (FHWA, 2009 & Dynatest, 1995) ......... 28

Table 2.2: Physical Characteristics of Typical LWD Devices (Mooney & Miller, 2009) 35

Table 2.3: Regression Analysis Between FWD & LWD Moduli, (Shafiee, et al., 2013) 42

Table 2.4: Typical Poisson’s Ratio Values, (ASTM D5858, 2003) ................................. 46

Table 2.5: Existing Backcalculation Software (Adapted from Appea et al., 2003) .......... 49

Table 3.1: Ohio County Roads by Cluster and Construction Material Used .................... 55

Table 3.2: Prima 100 LWD Sensor Deflection Measurements for Cluster # 3 ................ 65

Table 3.3: Representation of Backcalculation Procedure (Murillo & Bejarano, 2013) .... 67

Table 3.4: Calculated Layer Coefficients Range Based on Material Types, AASHTO 5.4.5

........................................................................................................................................... 72


LWD ................................................................................................................................. 78

Table 3.6: Coefficient for Structural Number versus SIP Relationships, (ROHDE, 1994).

........................................................................................................................................... 82

Table 3.7: Coefficient for E versus SIS Relationship, (Rohde, 1994) .............................. 83

Table 3.8: Effective Structural Numbers and Subgrade Modulus from Rohde Procedure83

Table 3.9: Calculated Layer Coefficients Range Based on Material Types, Rohde [1994]

Method .............................................................................................................................. 85

Table 4.1: Statistical Analysis Model Summary of FWD vs. LWD Sensor Deflections (D2).

......................................................................................................................................... 100

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Table 4.2: Statistical Analysis, Model Summary of FWD & LWD Procedures. ........... 108

Table 4.3: Summary of Regression Analysis of FWD versus LWD Generated from

Developed Models .......................................................................................................... 120

Table A1: Layer Thicknesses and Material Properties, Defiance County ...................... 132

Table A2: Layer Thicknesses and Material Properties, Harrison County ...................... 135

Table A3: Layer Thicknesses and Material Properties, Carroll County ......................... 136

Table A4: Layer Thicknesses and Material Properties, Auglaize County ...................... 137

Table A5: Layer Thicknesses and Material Properties, Mercer County ......................... 138

Table A6: Layer Thicknesses and Material Properties, Champaign County .................. 139

Table A7: Layer Thicknesses and Material Properties, Madison County ...................... 140

Table A8: Layer Thicknesses and Material Properties, Muskingum County ................. 141

Table C1: AASHTO 5.4.5 Equations Outputs Calculated from FWD Sensor Deflection

Using 11.8-in. (300mm) Plate. ........................................................................................ 148

Table D1: Summary of Averaged Backcalculated Layer Moduli Computed from FWD

Sensor Deflections Using 11.8-in. (300mm) Plate, Modulus 6.0 Software. ................... 152

Table D2: Summary of Averaged Backcalculated Layer Moduli Computed from LWD

Sensor Deflections Using 11.8-in. (300mm) Plate, Evercalc 5.0 Software. ................... 154

Table E1: Normalized/Extrapolated to 9000 Pounds Sensor Deflections (D0, D1, and D2) at

Radial Offset Distance 0, 12, 24 inches from the Center of the Load. ........................... 156

Table E2: Deleted Outliers/ Abnormal Sensor Deflections Obtained from FWD and LWD

Testing............................................................................................................................. 160

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LIST OF FIGURES

Page

Figure 2.1: Haversine Loading Applied by FWD in Defiance, Section C146-Krouse Road

........................................................................................................................................... 27

Figure 2.2: Falling Weight Deflectometer Schematic (Ferne & Langdale, 2010) ............ 28

Figure 2.3: Dynatest Model 8000 FWD (LRTC 2000 & Nazzal, 2003) .......................... 31

Figure 2.4: Schematic of Prima 100 with Additional Geophones, (Senseney, 2010) ...... 36

Figure 2.5: Typical Time History Data from LWD Test (Mooney & Miller, 2009) ........ 37

Figure 2.6: Best Fit Model of Fleming (2000) & Nazzal (2003) ...................................... 40

Figure 2.7: Area Under Pavement Profile (Adopted from Thompson, 1989) .................. 44

Figure 2.8: Backcalculation Flowchart (Lytton, 1989) ..................................................... 47

Figure 2.9: Relationship Between Deflection and Modulus (Tawfiq, 2003) .................... 51

Figure 3.1: Ohio Counties Map (Adapted from ORIL, 2015) .......................................... 53

Figure 3.2: Typical Pavement Surface Deflection Basins Based on Load Levels,

Champaign County, and Section Pisgah Road (C236-3) .................................................. 59

Figure 3.3: Coring and Obtaining Samples, form One of Tested Section ........................ 60

Figure 3.4: FWD Deflection Basins, Various Loads, Cluster # 3, Section of Southland Road

(Aug-C3-15), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate ............. 61

Figure 3.5: FWD Deflection Basins, Various Loads, Meter Road (CAR-T269-2),

Aggregate Overlay Surface Layer, Carroll County, 11.8-in. (300-mm) Plate .................. 62

Figure 3.6: Conducting Tests on Pavement Surface Sections in Defiance ....................... 63

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Figure 3.7: Example of a LWD Output from Field Testing, Auglaize County, Section of

Minster Fort Recovery Road, (Aug-C30-16) .................................................................... 64

Figure 3.8: LWD Deflection Basins, Same Loads, Cluster # 3, Southland Road (Aug-C3-

15), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate ............................ 66

Figure 3.9: Box Plot of Layer Coefficients for Each Widening/Construction Treatment,

Layer Type Based on AASHTO 5.4.5. ............................................................................. 73

Figure 3.10: Chart for Estimating Structural Layer Coefficient of Asphalt Concrete

(AASHTO, 1993) .............................................................................................................. 76

Figure 3.11: Used Chart for Cement-Treated Base Materials, (AASHTO, 1993). .......... 77


Layer Type Based on AASHTO 2.3.5. ............................................................................. 79

Figure 3.13: Box Plot Showing Layer Coefficients for Each Widening/Construction

Treatment as Determined Using Rohde [1994] Procedure ............................................... 86

Figure 3.14: Evercalc 5.0 General File Data Entry Screen for Pisgah Road, Champaign

County. .............................................................................................................................. 88

Figure 3.15: Evercalc 5.0 LWD Deflection File screen for Pisgah Road, Champaign

County. .............................................................................................................................. 88

Figure 3.16: Evercalc 5.0 LWD Deflection Basin for Pisgah Road, Champaign County 89

Figure 3.17: Main Window of Modulus 6.0 (Liu and Scullion, 2001) ............................. 90

Figure 3.18: Backcalculation Routine Window, Krouse Road, Defiance County. ........... 90

Figure 3.19: Box Plot Showing Backcalculated Layer Moduli for Each Widening

Treatment as Determined Using Modulus 6.0 Software, FWD Testing. .......................... 91

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Treatment as Determined Using Evercalc 5.0 Software, LWD Testing. .......................... 92

Figure 4.1: Comparison Between FWD and LWD Deflections at the Center of Loading

Plate, (D0) .......................................................................................................................... 97

Figure 4.2: DFWD vs. dLWD Correlation, Comparison to, (Horak et al., 2008) .................. 98

Figure 4.3: Comparison of FWD and LWD Deflections at r = 300mm from the Center of

Loading Plate, (D1) ........................................................................................................... 99

Figure 4.4: Comparison of FWD and LWD Deflections at r = 600mm from the Center of

Loading Plate, (D2) ......................................................................................................... 102

Figure 4.5: AUPP (LWD 3 Sensors) Modified Deflection Basin Parameter ................. 103

Figure 4.6: AUPP Comparison of FWD and FWD across All Sites .............................. 104

Figure 4.7: Backcalculated Layer Moduli of Pavement Layers Based on FWD and LWD

Measurements ................................................................................................................. 107

Figure 4.8: Regression Analysis Fitting Linear Trendline to Data Points ...................... 109

Figure 4.9: EFWD vs. ELWD, Comparison to Steinert et al. (2005), Nazzal (2003), and

Fleming et al. (2000) ....................................................................................................... 111

Figure 4.10: FWD Measured Modulus of the Subgrade. Values Indicated are Minimum;

Mean; and Maximum Respectively. (1 ksi = 6.89 MPa) ................................................ 112

Figure 4.11: LWD Measured Modulus of the Subgrade. Values Indicated are Minimum;

Mean; and Maximum Respectively. (1 ksi = 6.89 MPa) ................................................ 113

Figure 4.12: Rohde Method Measured Modulus of the Subgrade. Values Indicated are

Minimum; Mean; and Maximum Respectively. (1 ksi = 6.89 MPa) .............................. 113

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Figure 4.13: Regression Analysis Fitting Linear Trendline to All Layer Coefficients

Obtained from AASHTO 5.4.5-FWD & AASHTO 2.3.5-LWD Methods ..................... 114


Obtained from AASHTO 2.3.5-LWD and Rohde Method ............................................. 115

Figure 4.15: FWD vs LWD Layer Coefficient Models, Comparison to Rohde Method 116

Figure 4.16: Effective Structural Numbers Based on County Roads, AASHTO Equations

versus Rohde Method of Defiance County ..................................................................... 117

Figure 4.17: Regression Model of Effective Structural Numbers Obtained from, the

AASHTO Equations and the Rohde Method .................................................................. 118

Figure B1: Deflection Basins for Three Loads, Cluster # 2, Section of Minster Recovery

Road (Aug-C30-16), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate.

......................................................................................................................................... 143

Figure B2: LWD Deflection Basins Same Loads, Meter Road (CAR-T269-2), Aggregate

Overlay Surface Layer, Carroll County, 11.8-in. (300-mm) Plate. ................................ 143

Figure B3: FWD Deflection Basins for Three Loads, Cluster # 2, Section of East Shelby

Road (Aug-C71-8), HMA Surface layer, Auglaize County, 11.8-in. (300-mm) Plate. .. 144

Figure B4: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Blank Pike

Road (Aug-C160-12), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate.

......................................................................................................................................... 144

Figure B5: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Kossuth Loop

(Aug-C216A-3), Full depth Grindings layer, Auglaize County, and 11.8-in. (300-mm)

Plate................................................................................................................................. 145

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Figure B6: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Fairground

(Aug-FG-18), Full Depth Grindings Layer, Auglaize County, 11.8-in. (300-mm) Plate.

......................................................................................................................................... 145

Figure B7: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Neptune

Mendon Road (MER-C161C-7), HMA Surface Layer, Mercer County, 11.8-in. (300-mm)

Plate................................................................................................................................. 146

Figure B8: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Harris Road

(MER-C175B-8), HMA Surface Layer, Mercer County, 11.8-in. (300-mm) Plate. ...... 146

Figure B9: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Dutton Road

(MER-C230A-3), HMA Surface Layer, Mercer County, 11.8-in. (300-mm) Plate. ...... 147

Figure B10: LWD Deflection Basins Same Loads, Cluster # 2, Kossuth Loop (Aug-

C216A-3), Full Depth Grindings Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate

......................................................................................................................................... 147

Figure F1: Effective Structural Numbers Based on County Roads, AASHTO Equations

versus Rohde Method of Auglaize County. .................................................................... 161


versus Rohde Method of Mercer County. ....................................................................... 161


versus Rohde Method of Madison County. .................................................................... 162


versus Rohde Method of Champaign County. ................................................................ 162

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versus Rohde Method of Muskingum County. ............................................................... 163


versus Rohde Method of Carroll County. ....................................................................... 163


versus Rohde Method of Harrison County. .................................................................... 164

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CHAPTER 1 INTRODUCTION

1.1 Overview

A local road considered herein as low volume road which has approximately an

average daily traffic (ADT) of less than 400 vehicles; design speed typically less than

50mph (80kph), and corresponding geometry (Keller & Sherar, 2003). A majority of local

or low volume roads are experiencing growth in the annual average daily traffic due to

increasing residential and commercial development (Sargand et al., 2016). Many county

roads that fall under the low volume category still carry important levels of heavy vehicle

traffic. As traffic grows, pavements have to be widened and/or strengthened in an effort to

sustain the geometrics and structural integrity of the roadway. From a road way point of

view there are numerous reasons such as economics, sustainability, and availability that

many local engineers recommend and prefer to reuse the existing materials from the

roadway or any available material such as recycled asphalt, recycled concrete, fly ash, and

so forth.

In addition, various construction methods such as full depth reclamation (which is

an effective recycling procedure for low volume roads), white-topping, fabric

reinforcement, and roller compacted concrete are used to strengthen or widen pavement.

These methods are the keys to ensure that a local road meets the needs of the user, and is

essential for community and infrastructure development. However, the load carrying

capacity of these materials/methods techniques are unknown in Ohio (Sargand et al., 2016).

Also, without structural inputs parameters, the thickness design of widening is not possible,

19

resulting in premature failure when placed too thin or an overly conservative design when

placed too thick.

Therefore, research was undertaken to develop structural input parameters for the

pavement design/analysis based on AASHTO 1993 Guide for Design of Pavement

Structures for the local road network, to ensure durability and adequately serve its users.

The research evaluated structural condition of pavements using nondestructive test (NDT)

technology. Also, evaluation of structural condition is one of the most important factors in

pavements construction (AASHTO, 1993; Huang, 2004; Nazzal, 2003). Load carrying

capacity for a pavement is highly related to pavement layer and pavement subgrade moduli.

As a result, evaluating the local pavement conditions utilized to assess the structural

adequacy of pavements and determining used materials properties must be considered

significant in pavements construction. The current investigation of in-situ strength of

various construction/widening methods utilized on local roads and evaluation of structural

properties of pavements systems are based on field measurement using field tests to analyze

and interpret structural properties of rural pavement performance.

In 2015, a proposal for the Ohio Department of Transportation, Ohio Research

Initiative for Locals (ORIL) program was tasked to establish and verify a low cost, non-

destructive, repeatable methodology to characterize the load carrying capacity of materials

used in road construction when established values are unavailable. The research was

included field investigations to provide resilient moduli or a range of structural coefficients

for different materials utilized to widen, rehabilitate, or construct roads on Ohio's low-

volume road pavement system. The results of the research can be used by local officials to

20

enhance their knowledge and understanding of the potential structural integrity of

considered materials for use in roadway construction, maintenance, and improvement

projects. This can also lead to a more efficient design and greater confidence in the load

carrying capacity of the local roads. In addition, it can establish a rational basis for material

selection to correlate with the readily available cost data, which will aid locals in managing

budgets and ensuring the fiscal integrity of local pavement preservation programs, (ORIL,

2015).

The ORIL (2015) was tasked to investigate a total of 99 different test sites grouped

into five clusters, located in eight different counties (Defiance, Champaign, Mercer,

Auglaize, Muskingum, Madison, Carroll, and Harrison) around the state of Ohio were used

in the study. Field testing techniques for evaluation of paved and unpaved low volume

roads were investigated. The field components included traveling across Ohio to perform

site investigations, collecting deflection data, coring and measuring pavement layer

thicknesses, and collecting samples for performing laboratory experiments. The following

field tests were conducted to analyze and interpret local pavement performance:

1. Falling Weight Deflectometer (FWD)

2. Dynamic Cone Pentrometer (DCP)

3. Light Weight Deflectometer (LWD)

4. Portable Seismic Property Analyzer (PSPA)

This thesis work investigated the use of the Falling Weight Deflectometer (FWD)

and Light Weight Deflectometer (LWD) on the low-volume roads. In order to physically

investigate low-volume roads layer system, the Dynamic Cone penetration (DCP) was

21

employed to determine material properties and layer thickness. The Portable Seismic

Property Analyzer (PSPA) was used to evaluate low-volume road surface layers, but its

results were covered in another thesis.

Also, this study further documented the results from all the test sites. Both the FWD

and LWD were employed to measure deflection at the same spot of each single location.

A minimum of three (3) locations at each site were included in this evaluation in order to

develop better widening, rehabilitation, and construction strategies for each county road

based on material properties. The results was used to investigate the utilization of the LWD

(a lower cost technique to evaluate pavement condition) with respect to conventional

benchmark test, the FWD technology.

The FWD test (a commercially available nondestructive technique) utilizes radial

offset surface deflection measurements to evaluate pavement layer condition and

backcalculate layer moduli (Mooney et al., 2015). It is significant to determine the

relationships between FWD and LWD in order to provide the county engineers a low cost

alternative to the FWD for pavement layer analysis. In selecting the best correlation, it is

important to consider statistical analysis of the deflection data obtain from the sensors

measurements. Herein, regression analyses were used to determine the best fitting trendline

to the models corresponding to sensor deflection data. Also, the Statistical Package for the

Social Sciences (SPSS) was performed to determine whether the LWD is a valid structural

test for local pavement systems. Resultantly, statistical analyses demonstrate best

correlations between FWD and LWD. Several site and material specific relationship of

composite moduli between FWD and LWD have been conducted (Mooney et al., 2015).

22

However, Horak et al. (2008) and Mooney et al. 2015) are the only two studies that

compares radial offset deflection data.

Also, upon demonstration of close relationships between FWD and LWD sensor

deflections, the author would be interested to investigate/modify the Area under Pavement

Profile (AUPP), proposed by Hill and Thompson (1988). This modification at radial offset

distances 0, 12, 24 inches (0, 300, and 600mm) from the load center, now appears to be a

new valid parameter in the pavement evaluation using LWD investigation.

The AASHTO (1993), a guide for design of pavement structures, allows the use of

measured deflections to evaluate pavements conditions. AASHTO section 5.4.5 equations

were used to calculate effective structural numbers and layer coefficients using FWD

measurement, and AASHTO section 2.3.5 procedure were used for the LWD

measurements. These procedures are further processed to confirm by the Rohde method

(explained in chapter three) using FWD measurements.

Also, the deflection data are then used to evaluate the pavement stiffness in terms

of layer modulus. This layer modulus obtained from FWD and LWD measurements is

termed as backcalculated layer modulus. Numerous commercial software are available in

order to analysis nondestructive testing data to obtain backcalculated layer modulus. Two

independent software applications, MODULUS 6.0 and EVERCALC 5.0, were used in this

study. Due to feasibility and sensors adjustment capability of Evercalc 5.0 with LWD

deflection data, the Evercalc 5.0 was used to analyze LWD data. Also, the Modulus 6.0 is

capable of producing reliable results from FWD deflection data. Thus, Modulus 6.0 was

chosen in this study to investigate pavement condition (Al-Jhayyish, 2014). Lastly, the

23

backcalculated layer moduli have a significant input in the determination of effective

structural number (SNeff), and have also been used to determine the remaining life of the

pavement performance, therefore, the role of layer modulus is highly important in the local

pavement evaluation within this study.

1.2 Research Goal and Objectives

This thesis has two main goals: The first goal is to determine the structural

adequacy of the low-volume road pavement systems using nondestructive test (NDT)

technology. This is achieved by conducting field tests on local pavement systems. To this

end, the obtained deflection data from nondestructive tests conducted with the FWD and

the LWD based on the material properties was used to estimate layer moduli, effective

structural number. Thereafter, a range of structural coefficients for different materials

utilized to widen/construct low-volume road pavement system was calculated.

The second goal is to investigate the feasibility of employing the Light Weight

Deflectometer (LWD) as an in-situ testing device for the low-volume road pavements

which were earlier evaluated during the first goal activities. To accomplish this goal, a

comprehensive regression analysis was conducted between FWD and LWD sensor

deflections at various radial offset distances, developing a new method for evaluation of

Area under Pavement Profile (AUPP), the in-situ stiffness moduli, layer coefficients, and

effective structural numbers. The major objectives of this study are described below:

1. Evaluate low-volume road pavement conditions using non-destructive testing

devices, namely the FWD and LWD.

24

2. Analyze the deflection data to backcalculate layer moduli using various

backcalculation software.

3. Evaluate the analytical procedures to characterize the load carrying capacity of

materials used in road construction when established values are unavailable.

4. Compare/correlate FWD and LWD results for a single spot for at least three

different applied loading in each test location/section in order to find their

consistency.

5. Document and explain the differences in the results of FWD and LWD on the local

pavement evaluation methods.

6. Modifying a relationship (Area Under Pavement Profile) between deflection basin

parameter and pavement response to determine the tensile strain at the bottom of

an asphalt layer.

7. Perform statistical analysis to determine whether the LWD is a valid structural

testing device for low-volume road pavement systems.

8. Using the Rohde method to improve the correlation between FWD and LWD.

1.3 Outline of Thesis

This thesis is organized into five chapters and six appendices to effectively present

the data and information in the following format.

1. Chapter One is a brief introduction to the evaluation of the structural pavement

performance of low volume roads in Ohio. Also, this chapter further explains the

principal objectives of the research.

25

2. Chapter Two provides a literature review on the Falling Weight Deflectometer and

Light Weight Deflectometer. It also offers a short review of existing correlation

study between FWD/LWD, backcalculation methodologies of layer moduli, and

available commercial backcalculation programs.

3. Chapter Three includes the methodologies for the evaluation of pavement

condition based on material properties from the FWD and LWD deflection data.

This chapter considers in the AASHTO 1993 equations in order to determine

effective structural numbers, layer coefficients, and backcalculated layer moduli.

It also presents the Rohde method to determine effective structural numbers and

subgrade modulus.

4. Chapter Four presents the results and discussions of the correlation study between

FWD and LWD. This chapter also includes the statistical analysis (regression

models), which were conducted to ascertain the best correlations.

5. Chapter Five draws and summarizes the conclusions from the results and provides

recommendations for future studies on FWD and LWD.

26

CHAPTER 2 LITERATURE REVIEW

2.1 Introduction

Nondestructive testing methods for pavement evaluation was developed by

Waterways Experiment Station (WES) of the U.S Army Corps of Engineers in the mid-

1950’s (Grau & Alexander, 1994). The use of non-destructive testing for the evaluation of

pavement structural performance is increasing worldwide. Numerous studies have been

conducted in the past years to determine pavement structural capacity. Since its inception

in the 1960’s the Falling Weight Deflectometer (FWD) has become a nondestructive test

that plays a significant role in the pavement engineering. The Light Weight Deflectometer

(LWD), developed in early 1981, is another portable device for evaluating pavement layer

system (Mooney et al., 2015; Mooney & Miller 2009; Fleming et al. 2009; Siekmeier et al.

2009; Vennapus & White 2009). Since then, various methods have been developed using

FWD and LWD deflection data to investigate structural condition of pavement layers. This

chapter focuses on background of nondestructive devices, and common methodologies that

could be applied to their deflection data analyses.

2.2 The Falling Weight Deflectometer (FWD)

The Falling Weight Deflectometer (FWD) is a non-destructive test device that can

exert an impulse load into the pavement layer system. It mearues deflections at several

distances from the applied load on the pavement surfaces. The FWD has been broadly used

in pavement engineering to investigate pavement structural behaviors. It is a trailer or bed

mounted truck system. The FWD is able to load asphalt pavement or concrete surfaces in

a way that simulates real wheel loads in both magnitude and duration. As the name implies,

27

the FWD imparts a specified weight (usually 110 to 660 lbs (0.48 to 3.0 KN)) by raising

the weight hydraulically and then dropped it with a buffer system into a standard 11.8

inches (300 mm) diameter rigid steel loading plate for about to 20 to 35 miliseconds almost

the same load duration of a vehicle moving at 40 to 50 mph (see Figure 2.1 below (Ullidiz

& Stubsad, 1985)). Typically, three drops of 6000 lb (27 kN), 9000lb (40 kN), and 12000lb

(53 kN) were applied in the same location on an asphalt pavement surface to produce a

peak dynamic force of about 1500 lb (6.67 kN) to 27000 lb (120.0 kN) in 25-30

milliseconds, (Crovetti, J A Shahin & Touma, 2000).

Figure 2.1: Haversine Loading Applied by FWD in Defiance, Section C146-Krouse Road

Deflections induced by the FWD equipment are collected at the center of the

dropped weight and up to six other locations (a series of sensors each: -d1, d0, d1, d2, d3, d4,

and d5; located along the centerline of the trailer). These deflection sensors are located in

-2000

0

2000

4000

6000

8000

10000

12000

14000

0 10 20 30 40 50 60 70

Loa

d (l

b)

Time (milliseconds)

28

various radial distances from the applied load as shown in Table 2.1. (FHWA, 2009 &

Dynatest 1995).

Table 2.1: Sensor Spacing of the FWD Device (FHWA, 2009 & Dynatest, 1995)

Based on the radial distances shown in Table 2.1, the deflection measurements are

recorded by the data acquisition system typically located in the vehicle (Jordan, 2013). A

typical test schematic of FWD device mounted in the trailer system together with deflection

basin is indicated in Figure 2.2. The central sensor (d0), placed in the middle of plate

measures maximum deflection during testing. At the same time, the first sensor (d1) offset

12 inches away from central sensor and the rmaining series of sensors, measure deflections

at different points.

Figure 2.2: Falling Weight Deflectometer Schematic (Ferne & Langdale, 2010)

Sensor -D1 D0 D1 D2 D3 D4 D5 Offset Load Center (inches)

-12 0 12 24 36 48 60

29

Deflection data collected by a series of sensors indicated in Figure 2.2 are then

processed to estimate the pavement stiffness in terms of layer resilient modulus. This layer

modulus obtained from known FWD data is termed backcalculated modulus. A number of

commercial and non-commercial software are available for the analysis of FWD data to

determine this backcalculated layer modulus. The backcalculated modulus is not only used

in design but also to determine the layer coefficient and/or remaining life of the pavement

structures. Therefore, the role of this layer modulus is very important in pavement

engineering. This study focuses on the evaluation of the backcalculated layer modulus

using MODULUS 6.0 software and AASHTO 1993 guide for designing pavement

structures.

Moreover, FWD testing have several advantages. It can directly estimate the

Modulus of Subgrade Reaction (MR), it can precisely simulate traffic loading, it is easy and

can be operated by a single person, and it is quicke (can test up to 60 points per hour). Also,

the dropping loads vary from 1,500 to 27,000 lb (6.67 to 120 KN (Dynatest, 2009)).

Based upon available FWD device in the Ohio Department of Transportation

(ODOT) and among several FWD systems described in the literature review, the Dynatest

Model 8000 (a single-axle trailer-mounted FWD) was selected as the most applicable

device for the evaluation of local pavements condition during this research.

2.2.1 Dynatest Model 8000 FWD

The Dynatest FWD is a lightweight trailer-mounted device which has enjoyed the

long service record in the United States (Crovetti, J A Shahin & Touma, 2000). Figure 2.3

30

in below shows a view of this equipment. The Dynatest FWD consists of three main

components as describes below (Nazzal, 2003).

1. A Dynatest 8002E FWD Trailer.

2. A Dynatest System Processor.

3. A Hewlett-Packard HP-85B Laptop computer (Current system uses a windows

based laptop).

This device is equipped with a load cell to measure the applied force and seven to nine

geophones (velocity transducers) to measure the deflections up to 2mm. The Dynatest

FWD is further equipped with a standard 11.81 or 17.72 inches (300 or 450 mm) diameter

rigid or segmented loading plates, a rubberized pad, and a buffer system to help distribute

the load evenly (Dynatest 1995). The load is normally dropped from predetermined heights

ranging 2 to 20 inches (50 to 510 mm), (Nazzal, 2003). The load cell and seismic

deflection geophones (transducers) are both linked to sockets in a protective Trailer

Connection Box on the trailer. The transducers and the trailer connection box are connected

to a system processor (Dynatest, 1995).

31

Figure 2.3: Dynatest Model 8000 FWD (LRTC 2000 & Nazzal, 2003)

Figure 2.3 illustrates a FWD type developed by the Dynatest which is the original

commercial developer of the FWD technology, and is the world’s larger supplier of FWD

Equipment. The Dynatest FWD’s dynamic load capacity goes up to 54,000 lb (240.2 KN),

(Ahmed, 2010). A microprocessor based control and signal processing unit (the Dynatest

system processor), links the FWD trailer with the computer system. Also, this system

controls the FWD process, achieves scanning, modifying and further processing of the

geophone signals and monitors the status of the FWD unit to assure precise measurements.

The application of the loading is remotely controlled by the operator (Nazzal, 2003).

In addition, many other manufacturers of impulse devices for the nondestructive

testing of pavement structures are available. A brief list of those manufacturers were

KUAB America, Carl Bro Group, and Foundation Mechanics Incorporated, who offers

FWD equipment through its JILS sections (Ahmed, 2010).

32

2.2.2 KUAB America

KUAB FWD manufactures a wide variety of FWDs which are capable of delivering

dynamic loads up to 66 kips (293.58 KN) and currently operates five FWD’s types. The

load is applied through a single or dual mass system, and the dynamic response of the

pavement system is measured in term of vertical deformation, or deflection, over a

seismometers area combined with LVDT’s through a mass-spring reference system. A

specific load plate is incorporated to produce a uniform pressure on the pavement surface

(Ahmed, 2010).

2.2.3 Carl Bro FWD

Carl Bro is another producer of FWD devices. Dynamic load capacity of this type

of FWD is about 56,200 lb (250 KN), (Alavi et al., 2008). A series of 9 to 12 velocity

transducers are used to evaluate the load and dynamic response. A single mass is used and

controlled hydraulically which reacts as rubber buffer system to supports the dropped

weights.

2.2.4 JILS FWD

Foundation Mechanics, based in California manufacture under its nameplate JILS

FWD’s that have seven to nine deflection sensors (velocity transducers) with a single

integrated response to determine the deflection. This type of FWD generates a minimum

load of 1,500 pounds (6.67 KN) to a maximum load capacity of 54,000 pounds (240.2 KN).

Unlike the Dynatest FWD, the JILS FWD utilizes two adjustable air bags for controlling

load direction, magnitude. The rise time is dependent on the mass, dropping height and

arresting spring properties (Ahmed, 2010).

33

2.3 The Light Weight Deflectometer (LWD)

A portable device, developed for in-situ testing by the Federal Highway Research

Institute, the Light Weight Deflectometer (LWD) first appeared in 1981 at Magdeburg,

Germany, (Amer, Elbaz, & Elhakim, 2014). The light weight deflectometer was invented

to estimate the in-situ layer modulus of soils. This portable hand device can be used for

structural evaluation of pavement layer systems. Resilient modulus, analogous to elastic

modulus is the main parameter for characterizing base, subbase, and subgrade materials for

pavement design in the United States, (Senseney, 2010). Additionally, the LWD consists

of a circular plate ( typically varies in diameter 6, 8, and 12 inches (150 , 200, and 300

mm)) resting on the ground to support an impulse load from a released weight, guide rode,

sliding drop weight, a locking release mechanism, housing, geophone sensors, and urethane

dampers. For safe operation, the sliding mass is supported with a transportation lock pin.

During LWD testing a drop weight slides down from variable height (typically 33.5

inches (850 mm)) and applies a dynamic force impulse to the circular steel load plate,

(Senseney, & Mooney, 2010). Three geophones, located at center underneath the plate and

different offsets from loading point measure deflections. The one mounted in the center of

the load plate measures a maximum deflections (d0) and two extra mounted on a support

bar resting on the surface, measure deflection at two additional fixed locations. Force

transducer mounted inside the housing measures the applied force (P) from the standard 22

lb (10 kg) or the optional 33 lb (15 kg) or 44 lb (20 kg) drop weight setups. In addition, the

LWD transfers an average contact stress of 14 to 29 psi (100 to 200 Kpa) on the pavement

surface, (a load pulse of 15 to 20 ms duration), (Tayabji, & E. Lukanen, 2000). According

34

to Senseney & Mooney (2010), the conventional LWD modulus (ELWD) is calculated in

Equation 2.1 as follows:

E Equation (2.1)

Where:

ELWD = conventional modulus

ν = Poisson’s ratio of soil

a = plate radius

A = contact stress distribution parameter (A = 2 for a uniform stress distribution, A=

π/2 for an inverse parabolic distribution, A = 8/3 for a parabolic distribution).

Moreover, there are three main types of LWD, which have been used in previous

research; the German Dynamic Plate (GDP), the Transport Research Laboratory

(prototype) Foundation Tester (TFT), and the Prima 100 LFWD, (Nazzal, 2003).

Table 2.2 provides a brief summary of the characteristics provided by five different

LWD manufacturers. Each device is unique in terms of its dropping weight and height,

impulse time, plate diameter and style, contact pressure, and sensors types, (Mooney &

Miller, 2009).

35

Table 2.2: Physical Characteristics of Typical LWD Devices (Mooney & Miller, 2009)

Manufacturer CSM Zorn Prima Loadman TFT

Plate style Solid Solid Annulus Solid Annulus

Plate diameter (mm)

200, 300 150, 200,

300 100, 200,

300 130, 200, 300

100, 150, 200 ,300

Plate mass (kg) 6.8, 8.3 15 12.0 6.0 Variable

Drop mass (kg) 10.0 10 10, 15, 20 10.0 10, 15, 20

Drop height (m) Variable 0.72 Variable 0.80 Variable

Damper Urethane Steel spring Rubber Rubber Rubber

Force measured Yes No Yes Yes Yes

Plate response sensor

Geophone Acceleromet

er Geophone Accelerometer Geophone

Impulse time (ms) 15 - 20 18 ± 2 15 - 20 25 - 30 15 - 25

Max load (KN) 8.8a 7.07a 1 - 15a 20a 1 - 15a

Contact stress User def. Uniform User def. Rigid User def.

Poisson's ratio User def. 0.50 User def. 0.50 User def. (a) Dependent Upon Drop Height and Damper

Table 2.2 demonstrates that although there are differences in the design and mode

of operation which can cause variations in the field measurement output, there are many

similarities in their mechanics of operation.

2.3.1 Prima 100 LWD

The first LWD model used in this thesis was the Prima with its plate manufactured

by Keros Technology and Carl Bro. both of Denmark, (Steinert et al., 2005). The Prima

100 made by Carl Bro. weighs, in total, approximately 57.2 lb (26 kg) and has varying

falling mass between 22, 33, and 44 lb (10, 15, and 20 kg) along with a varying drop height

0.4 to 33.5 inches (10 to 850 mm). This device has a load impulse of between 15-20

milliseconds and load range capacity of 225 to 3372 lb (1 to 15 KN) with its 11.8 inches

(300mm) bearing plate diameter, (Fleming, et al., 2000). Also the Prima 100 allows

36

collection of up to two deflections at a specified radial distance of 12 to 24 inches (300 to

600mm) from the center geophone. It measures both the impact force (P) from the falling

weight, and deflections as determined by integration from the velocity of the surface

(Christensen, 2003). The Prima 100 is shown in Figure 2.4.

Figure 2.4: Schematic of Prima 100 with Additional Geophones, (Senseney, 2010)

Furthermore, a personal digital assistant (PDA) device connected to the LWD

apparatus via wireless Bluetooth connection collects and saves measured load and

deflections. The collected deflections create a deflection basin profile and combined

surface modulus immediately after each reading.

2.3.2 The LWD Principle of Operation

The Light Weight Deflectometer is a portable device for repeated testing which can

be operated by a single person. It is a fast and less expensive test method. The relatively

37

small weight of LWD compared to FWD makes it more applicable for testing unbound

pavement layers. The lower contact stress allows the apparatus to sometimes bounce and

move immediately after impact of the weight (Von Quintus & Minchin, 2009). During

operation, it requires a flat surface to function properly and three seating drops are

performed to ensure close contact. Then another three drops were performed, and the

deflection corresponding to each blow and the soil’s dynamic modulus were calculated by

the data acquisition system. An important insight into the soil property can be obtained by

a typical output from acquisition system of LWD, which show time history data (see

Figure 2.5 in below)

Figure 2.5: Typical Time History Data from LWD Test (Mooney & Miller, 2009)

The LWD is, however, not ideal for thicker pavements because of low contact stress

and a limited depth of influence to the pavement layers. Also, it does not collect pavement

38

temperature in both thin and thick asphalt pavements; thus a further means of recording

temperature is needed (Icenogle & Kabir, 2013).

2.4 Existing Correlations between FWD and LWD

Numerical studies have been explored in the past to assess the FWD and LWD

measurements and to evaluate the effect of some relevant parameters. However, little

researches have been given to fully understand the correlation of LWD with different

instrument configurations such as FWD.

Only two published studies by Horak et al. (2008) and Mooney et al. (2015)

addressed the relationship between the FWD and LWD with additional geophones/sensors

(Mooney et al., 2015). The findings by Horak et al. (2008) on 3 to 4 inches (75 to 100mm)

thick layers of sand treated with emulsion between FWD and LWD sensor deflections with

various radial offset distances. His regression model at the center of loading plate yielded

a nonlinear model (see Equation 2.2) with a low correlation (R2 = 0.62).

D 0.3617 d . Equation 2.2

However, high relationships (R2 = 0.82 and R2 = 0.67) were found between FWD and LWD

sensor deflections at radial offset distance of r = 300 and r = 600 respectively. His

regression models are describe in Equations 2.3 and 2.4, respectively.


39


The results by Mooney et al. (2015) on full depth reclamation of asphalt layers

using additives (Badlands, Carlsbad, and Mesa Verde), at the center of the loading plate, r

= 300 mm, and r = 600 mm with R2 = 0.71, R2 = 0.96, and R2 = 0.98 respectively were

found to be:

w 0.23d 0.26Equation 2.5



Similarly, Fleming (2000) conducted a correlation study between three main types

of LWD moduli with that of FWD, and the results of those tests proved that the evaluated

moduli of the Prima 100 LWD was well correlated with resilient modulus of FWD.

Equation 2.8 shows an example of well conducted results.

MFWD = 1.031 ELWD Equation (2.8)

The next study was accomplished by Nazzal (2003), see Figure 2.6. His regression

analysis for FWD and LWD results have proved that the best model to predict the FWD

40

backcalculated moduli, MFWD, in (MPa) from the LWD modulus, ELWD, in (MPa) is briefly

described in Equation 2.9 in below with R2 = 0.94, significance level < 99.9%, and standard

error = 33.1:

MFWD = 0.97 (ELWD) for 12.5 MPa < ELWD < 865 MPa Equation (2.9)

Figure 2.6: Best Fit Model of Fleming (2000) & Nazzal (2003)

As clearly indicated in Figure 2.6, Nazzal (2003) demonstrated a good correlation

between FWD and LWD, which generally agreed with those of Fleming (2000). Moreover,

FWD deflection normally correlate well with LWD deflections, but the back calculations

shows variation (Saadeh & Rhagavendra; Zhang; Mohammad 2007). The correlation

between LWD and FWD is known to vary with thickness. (Fleming and Lambert 2007).

41

Smaller contact stress, fewer geophones and shallow depth of influence of the LWD could

be the reason of variations as well (Nazzal 2007).

As back calculation procedure has an indispensable role in LWD modulus

measurement, such a bad evaluation of inputs results in erroneous layer moduli. In addition,

supporting layers can influence the surface layer (Von Quintus & Minchin 2009). Icenogle

(2013) found that the FWD and LWD deflections correlated well. However, the back-

calculated moduli of the surface layer between these two tests do not correlate. This is

because of the variations of the back-calculation software and the number of geophones

representing the deflection basin.

Conventional FWD moduli were found to be 2.5 to 3.3 times larger than LWD

moduli (Livneh & Goldberg, 2001). Variations of loading level/rate used in FWD and

LWD is the author’s reason. Furthermore, the LWD moduli depends on location, soil type,

pavement thickness, gradation, and moisture content. The stiffness moduli ratio between

the FWD and the LWD varied between 0.8 to1.21 with R2 = 0.5 to 0.9 (Fleming et al,

2007). According to Rahimzadeh (2004), the correlation between FWD and LWD was

found to be material thickness and type dependent. Table 2.3 shows a short summary of

aforesaid correlation equations obtained to relate FWD with LWD moduli in various

researches.

42

Table 2.3: Regression Analysis Between FWD & LWD Moduli, (Shafiee, et al., 2013)

Equation Layer

Description R-Square

(R2) Value LWD Model

Source

LWD(MPa) = 0.97FWD(MPa)

450-mm granular capping over silt

and caly 0.60 Prima 100

(Fleming et al., 2000)LWD(MPa) =

1.21FWD(MPa)

260-mm lime-cement treated caly subgrade

0.77 Prima 100

LWD(MPa) = 0.80 to 1.30FWD(MPa)

225-mm well-graded crush

stone granular subgrade

0.50 Prima 100


Granular subgrade

0.97 Prima 100 (Nazzal et al., 2004)


Thin asphalt layer

(≤ 127mm) 0.87 Prima 100

(Steirent et al., 2006) LWD(MPa) =

0.75FWD(MPa) Thicker asphalt

layer ( ≥ 178mm) 0.56 Prima 100

As indicated in Table 2.3, the coefficient of determination, R2, value is smaller for

thick and soft materials. This demonstrates that the Prima 100 LWD is an applicable device

for thin layer consisting of stiff materials (Shafiee et al., 2.13).

2.5 Determination of Pavement Responses Using Deflection Basin Parameter

According to Garg et al. (1998) and Kim et al. (2000), several pavement responses

were identified by the researchers as good performance indicators during the structural

evaluation of pavements. These included: (1) horizontal strain (tensile strain) at the bottom

of asphalt layer; (2) vertical compressive strain on the top of the base layer; and (3) vertical

compressive strain on the top of the subgrade.

43

In addition, many researchers have explored the relationships between deflection

basin parameters and pavement responses such as stresses and strains using FWD test (Kim

& Park, 2002). Also, Thompson (1989, 1995) proposed a relationship for full depth

pavements and aggregate base pavements using the Area under Pavement Profile (AUPP).

The AUPP is a FWD deflection basin shaped parameter. This dimensionless deflection

basin parameter definition is complimentary to the AREA parameter. Also it has been

widely used as a measure of pavement stiffness which means higher AUPP corresponds to

lower stiffness and vice versa (Gopalakrishnan & Kim, 2010; Rada et al., 2015; Tang et

al., 2012). The AUPP is described by Thompson (1989, 1995) in Equation 2.10 and

Figure 2.7 as follows:

AUPP 5D 2D 2D D Equation (2.10)

Where:

D0 = FWD sensor deflection at the center of the loading plate, mils

D1 = FWD sensor deflection 12 inches from the center of the loading plate, mils

D2 = FWD sensor deflection 24 inches from the center of the loading plate, mils,


44

Figure 2.7: Area Under Pavement Profile (Adopted from Thompson, 1989)

The tensile strain at the bottom of the asphalt layer (εAC), for full-depth asphalt is

computed from Equation 2.11.

Log ε 1.024 ∗ Log AUPP 1.001Equation (2.11)

For aggregate base pavements, the tensile strain can be predicted using Equation

2.12 as follows:

Log ε 0.821 ∗ Log AUPP 1.210Equation (2.12

45

It is worthy to mention that the geometric property of the deflection basin (AUPP)

is a significant parameter, which can be used to predict the horizontal strain (tensile strain)

at the bottom of the asphalt layer. The use of AUPP for predicting (εAC) is not affected by

the type of pavement and subgrade (Kim & Park, 2002).

Specifically, this study further investigated Thompson’s (1989, 1995) promising

relationship (AUPP) for the determination of the horizontal strain (tensile strain) at the

bottom of an asphalt layer using FWD and LWD sensor deflections at radial offset distance

0, 12, and 24, 36 inches and 0, 12, 24 inches from the center of the loading plate

respectively.

2.6 Backcalculation of Layer Moduli

Backcalculation is an analytical technique by which pavement layer moduli and

other stiffness properties are calculated, corresponding to the measured load and

deflections. The analysis may be conducted by the following methods: iteration, closed

form solution, database-searching, and simultaneous equations (using nonlinear regression

equations produced from layered elastic analysis output data), (ASTM D5858-96, 2003;

Alavi et al., 2008). Backcalculations using iteration method for calculating pavement layer

moduli and subgrade resilient modulus is the most widely accepted method based, on

pavement deflection profile or basins generated by FWD and LWD (Muench, et al., 2003;

Rahim & Geprge, 2003; Romanoschi & Metcalf, 1999). This method requires the initial

inputs such as assumed layer moduli that is often called (seed) modulus for the pavement,

number of layers, layer thicknesses, and Poisson’s ratio (ASTM D5858, 2003). This value

should be selected carefully for the subgrade layer. A typical range of Poisson’s ratio values

46

based on ASTM D5858 (2003) which may be used if other values are not available, are

describe in Table 2.4.

Table 2.4: Typical Poisson’s Ratio Values, (ASTM D5858, 2003)

Asphalt concrete 0.30 to 0.40

Portland cement concrete 0.10 to 0.20

Unbound granular bases 0.20 to 0.40*

Cohesive soil 0.25 to 0.45*

Cement-stabilized soil 0.10 to 0.30

Lime-stabilized soil 0.10 to 0.30

* Depending on Stress/Strain Level and Degree of Saturation.

After assuming the initial layer moduli, the surface deflections at radial offsets

(geophone location) can be computed by the mechanistic analysis based on seed modulus

and layer geometry. The computed deflections are then compared to the field measured

deflection values. The process is continued by changing or adjusting the layer moduli each

time, until a good match (within some tolerable error) between the computed and

theoretical (measured) deflections can be reached (FHWA, 1994). A basic schematic of the

backcalculation technique is shown in Figure 2.8 as follows:

47

Figure 2.8: Backcalculation Flowchart (Lytton, 1989)

The flowchart indicated above shows backcalculation technique. This flowchart is

further explained briefly according to Qin (2010) below:

1. Layer thicknesses and loads: The first and second boxes in the left hand side,

represent the layer thicknesses and applied load levels on the pavements surface

respectively. These values are the inputs and should be known in advance.

2. Measured deflections: FWD field measured sensor deflections.

3. Seed moduli: Input of the initial modulus in order to calculate theoretical sensor

deflections.

4. Deflection calculation: Use pavement response models such as stresses and strains

which can be used to compute theoretical sensor deflections.

5. Error check: Correlate between computed and measured deflections.

48

6. Search for new moduli: Iteratively search until the computed and measured

deflection are paired within tolerable error limit in order to find a new moduli of

the pavement layers.

7. Controls on the range of moduli: A range of modulus which can be define for each

pavement layer by backcalculation technique to avoid inconsistent pavement layer

moduli.

2.6.1 Overview of Backcalculation Software

Several well-known software for the evaluation of flexible pavements layer moduli

are available. After a literature review MODULUS 6.0, and EVERCALC 5.0 were selected

for this study. Since both are common and capable of producing reliable outputs. These

programs are based on linear layered theory for the basic structural model of the pavement

response. Table 2.5 shows a list of backcalculation software, (Appea, Brandon, & Jr, 2003).

49

Table 2.5: Existing Backcalculation Software (Adapted from Appea et al., 2003)

Software Pavement Type

Analysis

Method

Moduli Calculation

Method

Convergence Criteria

Forward Analysis

Method and Program

Stress and Strains*

EVERCALC 5.0

Flexible Static Bowl

Matching

Root Mean Square Error

Multilayered Linear Elastic,

WESLEA

User defines position

BOUSDEF Flexible

and Rigid

Static Bowl

Matching Absolute

Sum


Boussinesq theory

Does not Calculate

MODCOMP 5.0

Flexible and

Rigid Static

Bowl Matching


Multilayered Linear/Nonline

ar Elastic, CHEVLAY2

Forward Calculatio

n and User

defines positions

PEDD Flexible

and Rigid

Static

Determining

Equations and Bowl Matching

Minimum Absolute

Difference


ELSYM5

User Defines Position

MECHBACK

Flexible Static Bowl

Matching


Multilayered Linear Elastic, CHEVRON

Does not Calculate

UMPED Flexible

and Rigid

Static

Determining

Equations and Bowl Matching

Minimum Absolute

Difference

Multilayered Linear Elastic, CHEVRON

User Defines Position

ELMOD Flexible

and Rigid

Static Bowl

Matching


Odemark-Boussinesq

Method Fixed

MODULUS 6.0

Flexible and

Rigid Static

Bowl Matching



WESLEA No

*Fixed or User Defines Positions

50

From the ranges shown in Table 2.5, MODULUS 6.0 was selected based on the

reliability of its results to analyze the FWD data, and EVERCALC 5.0 was selected due to

its capability of sensors adjustments with LWD geophones to analyze LWD deflection

data, (Al-Jhayyish, 2014 & Tawfiq, 2003). These two software’s were used to estimate the

pavement layer moduli. A comparison of their backcalculated layer moduli were conducted

by the author in this thesis.

2.6.2 MODULUS Program

Modulus developed by the Texas Transportation Institution is the most commonly

used software for backcalculation pavement layer moduli, (Scullion et al., 1990; Uzan et

al., 1989). It can be applied to a two, three, and four-layer system, and is based on the linear

elastic theory. WESLEA, a layered elastic solution platform developed by US Army Corps

of Engineers covered in Modulus as a subroutine to perform the forward calculation for

building a database of calculated deflection basin (Tutumluer, Investigator, Pekcan, &

Ghaboussi, 2009). This database is matched with measured deflections using subroutine to

obtain the layer moduli in the pavement systems after several iterations. The latest version

of this program is Modulus 6.0. This newest version can be run for FWD data including

seven sensors easily, and it is able to analyze up to four unknown layer systems.

2.6.3 EVERCALC Program

Evercalc, developed by the Washington State Department of Transportation is also

a popular backcalculation program. It uses a WESLEA layered analysis program for

forward calculation and a modified Augmented Gauss-Newton algorithm for optimization

(Tutumluer et al., 2009). The optimization routine is applied to obtain a set of modulus

51

values which provide the best fit between measured and calculated deflections models,

basins, when given an initial estimate of elastic modulus and a limiting range of moduli.

(Tawfiq, 2003). Also as implied, a set of E values is assumed and the deflection at each

sensor is calculated and matched within a pre-specified root mean square (RMS) error

range. Each unknown E is varied independently, and a new set of deflections calculated for

each variation. For every layer and every sensor, the intercept Aji, and the slope Sji (shown

in Figure 2.9) are determined. For numerous deflections and layers, the solution is achieved

by developing a set of equations which define the slope and intercept for every deflection

and every unknown modulus (Tawfiq, 2003).

Log (deflectionj) = Aji + Sji (log Ei) Equation (2.13)

Figure 2.9: Relationship Between Deflection and Modulus (Tawfiq, 2003)

Evercalc can evaluate up to five layers, ten sensors, and twelve drops per station.

After estimating elastic moduli of pavement layers, it can determine the stresses and strains

52

at different locations. Also, it runs an inverse solution technique on FWD deflection data

to determine a set of layer moduli, (Evercalc User’s Guide, 2005). The deflection tolerance,

moduli tolerance, and the highest number of iterations can be defined before running the

program by the user. When one or more of these conditions are satisfied, the program will

terminate.

1. Deflection Tolerance:

RMS % ∑ 100 Equation (2.14)

Where:

RMS = root mean square error,

dci = calculated pavement surface deflection at sensor i,

dmi = measured pavement surface deflection at sensor i,

nd = number of deflection sensors used in the backcalculation process.

2. Moduli Tolerance: Expressed by the following equation, (EVERCALC User’s

Guide, 2005)

ε

Equation (2.15)

Where:

Eki and E (k+1) = the i-th layer moduli at the k-th and (k+1)-th iteration,

m = number of layers with unknown moduli.

53

CHAPTER 3 EVALUATION OF PAVEMENT CONDITION USING FWD AND

LWD MEASUREMENTS

3.1 Field Testing

This chapter discusses measured deflection data from the FWD and LWD devices.

The data consists of (1) FWD filed measured data, (2) LWD field measured data, (3) layer

thicknesses of the pavement, and (4) evaluation of materials properties. The Ohio Research

Institute for Transportation and the Environment (ORITE) arranged many trips to conduct

field tests for collecting deflection data, layer thicknesses, and materials properties around

the states of Ohio. The map of counties which were investigated during this thesis work is

shown in Figure 3.1.

Figure 3.1: Ohio Counties Map (Adapted from ORIL, 2015)

54

A total of 68 projects with 99 test sites, grouped into five clusters, in eight different

counties were provided for this study. These project sites were first grouped into: 32 test

sites in Defiance County (cluster 1), 16 test sites in Harrison and Carroll Counties (cluster

2), 13 test sites in Auglaize and Mercer Counties (cluster 3), 14 test sites in Champaign

and Madison Counties (cluster 4), and the remaining 24 test sites were included in

Muskingum County known as a cluster 5 (Sargand et al., 2016) .

Furthermore, the FWD tests were immediately followed by the LWD tests across

all test sites. The testing was conducted with thin asphalt layers 3-5 inches (75-127 mm)

underlying by cement treated base layer. Full depth reclamation mechanism was used to

stabilize 4-6 inches (100-150mm) base layers. At each test site a minimum of three

locations/sections were investigated. A total of three main mass drops (additional one

initial LWD seating drop) were performed for both devices at each test location. Also for

the FWD device, variable drop mass heights were used to achieve a target load. The target

loads for one, two, and three drops were 6000lb (26.68 KN), 9000 (40 KN), and 12000lb

(53.37 KN), respectively. However, the same target load 2000 – 3500 lb (8.89 – 15.56

KN) was used for all three main drops and one initial drop during LWD testing as

recommended by Mooney et al. (2015).

Consequently, three measurements in the same location were taken in order to

ensure proper reading and to enhance the accuracy of correlations between FWD and LWD

sensor deflections. The measurements of the three test drops of both devices were average

and considered herein. As results, the test sites and grouped clusters are presented in

Table 3.1.

55

Table 3.1: Ohio County Roads by Cluster and Construction Material Used

County Name Cluster # Total Test

Sites = (99) Material Type Tests Performed

Defiance 1 32

Fiber Cement

FWDa , LWDb , PSPAc, & DCPd

Cement FDR*

Asphalt FDR HMA**

Whitetopping Fabric Reinforced Stone

Full Depth Grinding

Harrison

2 16

Cement FDR


Permazine FDR HMA

Asphalt FDR

Carroll

Aggregate Overlay

HMA

Cement FDR

Auglaize 3 13

HMA


Full Depth Grinding

Partial Grinding

Mercer 70/30 asphalt/cement

HMA

Champaign

4 14

HMA


Mechanical FDR Cement FDR

Madison HMA

Cement FDR Geogrid

Muskingum 5 24

HMA


Motorpave Concrete Steel

PCC***

Surge & 411 Brick & 411 Asphalt FDR Lime FDR

Brick Fly Ash FDR

FDR* - Full Depth Reclamation; HMA** – Hot Mix Asphalt; PCC*** – Portland Cement Concrete FWDa – Falling Weight Deflectometer; LWDb – Lightweight Deflectometer PSPAc – Portable Seismic Pavement Analyzer, & DCPd – Dynamic Cone Penetration

56

To better illustrate a listed materials and performed tests, Some terms in the table

above, used in this study refer to the materials types and different procedures in

widening/construction of rural roads are listed below for the convenience of the reader

(Sargand et al., 2016).

1. Whitetopping: A reclamation approach in which the existing asphalt pavement is

overlaid with Portland Cement Concrete (PCC).

2. Surge: Stone which is the product of the primary crushing run. This stone is used

as base material for haul roads (a coarse, temporary road built to facilitate the

movement of materials and equipment) to protect very soft and wet soils.

3. 411: Also referred to as stabilized crushed aggregate (ODOT item 411, material

specification), includes coarse aggregate with a large amount of limestone fines.

This aggregate blend is used as an aggregate base and will harden after addition of

water and compaction due to the chemical cementation of the large stone combined

with line fines.

4. Surge/411: Stabilized crushed aggregate (411) mixed with surge stone that is

wetted and compacted.

5. Full Depth Reclamation (FDR): A reconstruction mechanism that pulverizes an

existing flexible pavement with the underlying materials to a predetermined depth.

Stabilizing agents such as cement, fly ash, lime or Permazine can be added to the

pulverized blend. After, this blend can be compacted with the underneath materials

in order to create a homogeneous layer as a base for a new pavement structure.

57

6. 70/30 asphalt/Cement: A mixture of 70% recycled asphalt grindings from milling

projects with 30% cement and water. This mixture is used in pavement structure

as base material.

7. Permazine: Is an enzyme rich material used for soil stabilization purpose. This

material is created by a natural fermentation mechanism and can be mixed with

soil and water to produce a cementitious effect that builds a solid base structure.

8. Permazine FDR: Adding Permazine with Full Depth Reclamation (FDR) in order

to stabilize the subgrade blend.

9. Motorpave: Usually referred to item 405 Bituminous Cold Mix Pavement. This

material is frequently placed in 2 inches lifts and covered with chip seal.

10. Mechanical FDR: A well compacted full depth reclamation without using of any

stabilization technique.

11. Lime FDR: Adding lime and water with full depth reclamation to stabilize the

subgrade blend.

12. Geogrid: A geosynthetic material that can be used to keep structural integrity in

soil structure in order to resist tensile stress in soil.

13. Full/Partial Depth Grindings: Using asphalt products which have been pulverized

from another bituminous surface project and recycled for reuse as surface layer or

an aggregate base.

14. Fly Ash FDR: Adding of fly ash and water with Full depth reclamation to stabilize

the subgrade of the asphalt pavement.

15. Fiber Cement: A concrete pavement reinforced with small fiber.

58

16. Fabric Reinforced Stone: Using fabric on top of natural subgrade with a compacted

overlaid aggregate base layer on top of the fabric. This mechanism is used to

enhance the tensile strength of the aggregate base material for protecting the

natural subgrade.

17. Aggregate Overlay: Using stone as a surface layer in pavement structure.

18. Asphalt FDR: An asphalt binder added to Full Depth Reclamation (FDR) to

stabilize the pulverized subgrade blend.

19. Asphalt Grindings FDR: Adding recycled asphalt grindings with full depth

reclamation to the asphalt subgrade blend.

20. Brick/411: Recycled bricks mixed with a 411 material and creates a blend that can

be wetted and compacted to bind materials together. This type of material is used

as an aggregate base.

21. Cement FDR: Adding cement and water to the full depth reclamation to stabilize

the subgrade blend.

22. Concrete/Steel: Recycled concrete and rebar from old buildings, bridges, and

pavement structures placed on top of the subgrade. This material can be used as

an aggregate base.

3.2 Quantifying Pavement Condition Using FWD Deflections

Falling Weight Deflectometer (FWD) testing was used to evaluate the 99 different

test sites. For all of these test sites, the nominal 11.8 inches (300mm) plates and three

loading drops each, approximately 6000lb (26.7KN), 9000lb (40KN), and 12000lb

(53.4KN) were used to measure pavement surface deflections. Every sensor has a subscript

59

indicates the distance in inches, from the center of applied load. A typical surface

deflections under variable loading conditions for Champaign section of Pisgah road is

shown in Figure 3.2.

Figure 3.2: Typical Pavement Surface Deflection Basins Based on Load Levels, Champaign County, and Section Pisgah Road (C236-3)

Moreover, a program called FWD-AREA to measure pavement condition was first

developed by the Washington State Department of Transportation (2005). The FWD-

AREA program computes a deflection basin that has a trapezoidal area developed in the

pavement system based on dynamic load, (Jordan, 2013). Previous studies show the

modulus of subgrade reaction (MR) is directly correlated with the deflection sensor spaced

from center load in about 24 inches (D24). (WSDOT, 2005). The Equation 3.1 below was

proposed for estimating MR:

D0

D1

D2

D3D4 D5

02468

1012141618202224262830

-60 -48 -36 -24 -12 0 12 24 36 48 60

Sen

sor

Def

lect

ion,

mil

s

Sensor Distance, inches

6000 lbf

9000 lbf

12000 lbf

FWD Load

60

M psi 9000 ∗ . Equation (3.1)

Where:

D24 = 24 inches from the center of the loading plate, mils.

MR = Modulus of subgrade reaction, psi.

A computer software, Modulus 6.0, was used to backcalculate pavement layer

moduli (typically, Surface, Base, Subbase, and Subgrade in this study). Thickness of the

layers, determined from prior Dynamic Cone Penetration (DCP) testing and the Poisson

ratio selected based on ASTM D5858 (2003) were the main inputs of the Modulus 6.0

software. Meanwhile, coring is a way to physically see and accurately measure different

bound layers, helps to determine the bond quality between pavement layers, and identify

the subgrade materials in asphalt pavement structure. Figure 3.3 shows DCP operation and

sample measurements which was performed in of the tested section.

Figure 3.3: Coring and Obtaining Samples, form One of Tested Section

61

Figure 3.3 indicates coring procedure which played a substantial role in the

pavement investigation during this study. This analysis involves layer’s thicknesses and

determination of the material properties. Evaluating pavement characteristics and other

predictors of pavement service cannot be done or seen visually without site investigation.

Therefore this study covered and investigated the aforementioned requirements for local

pavement system of the selected counties in Ohio. A complete coring summary along with

layer thicknesses is shown in Appendix A.

3.2.1 FWD Results

The results in Figure 3.4 demonstrate a typical shape of the deflection bowl for

structural analysis of the pavements. Basically, the upper deflection line define the first

dropped load, 6000lb (26.68 KN) in relation to the second and third dropped loads each,

9000lb (40 KN) and 12000lb (53.37 KN), respectively.

Figure 3.4: FWD Deflection Basins, Various Loads, Cluster # 3, Section of Southland Road (Aug-C3-15), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate

0

10

20

30

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)

Radial Distance (in)

6000 lb

9000 lb

12000 lb

62

Based on Figure 3.4, typically a semi bowl with little curvature shows minimum

deflection and the resulting stiff layer system in the structural analysis of the pavements

system. However, the semi bowl with high curvature around these loads indicates

maximum deflection and the resulting weak layer system (see Figure 3.5). The remaining

typical deflection basins of every location in cluster-3 for various test sites were provided,

and can be seen in Appendix B.

Figure 3.5: FWD Deflection Basins, Various Loads, Meter Road (CAR-T269-2),

Aggregate Overlay Surface Layer, Carroll County, 11.8-in. (300-mm) Plate

3.3 Quantifying Pavement Condition Using LWD Deflections

The second nondestructive device examining structural pavements performance

during this study was the Light Weight Deflectometer (LWD). The Prima 100 LWD, with

additional radial geophones was used to develop a deflection basin for backcalculation of

layer moduli. Three consecutive drops of sliding weight (a standard 10kg) and plate

0

15

30

45

60

75

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lb

9000 lb

12000 lb

63

diameter of 11.8 inches (300mm) were performed on leveled surface. Figure 3.6 shows

process of collecting field deflections using Prima 100 LWD.

Figure 3.6: Conducting Tests on Pavement Surface Sections in Defiance

As shown in Figure 3.6, two additional geophones/sensors were placed at radial

offset distance 12, and 24 inches from the center of the loading steel plate. Three drops (in

addition of one initial drop) in each location/section were performed and the deflection

basin was taken by geophones. The deflections and loads were measured and stored by a

personal digital assistant (PDA). The PDA device has a Bluetooth wireless connection to

the Prima 100 LWD apparatus. As previously stated, Evercalc 5.0 software was used to

backcalculate pavement layer moduli in this study. The LWD software integrates the

velocity transducer signal to determine peak deflection value. According to Shafiee et al.

(2013), usually under testing the maximum deflection does not occur at the same instant as

the peak load especially for lower stiffness materials as shown in Figure 3.7.

64

Figure 3.7: Example of a LWD Output from Field Testing, Auglaize County, Section of

Minster Fort Recovery Road, (Aug-C30-16)

3.3.1 LWD Results

Prima 100 LWD testing with radial geophones followed FWD testing, and three

repeat measurements were taken at the same locations/section along the 99 different test

sites as the FWD test. The Prima 100 LWD with additional geophones allows the analysis

of more layers, and was used in this study to see whether its measurements correlates well

with FWD measurements. The correlation between their measurements are presented and

discussed in the next chapter. The LWD deflection measurements for cluster-3 (Auglaize

+ Mercer) are shown in Table 3.2.

-10

-5

0

5

10

15

20

25

0 50 100 150 200 250 300

Loa

d C

ell (

psi)

Time (millisecond)

Load Cell (psi) D0 (mils) D1 (mils) D2 (mils)

65

Table 3.2: Prima 100 LWD Sensor Deflection Measurements for Cluster # 3

Road

Name

Secti

on

D0 D1 D2 Road Name Section

D0 D1 D2

Mils Mils Mils Mils Mils Mils

East

Shelby -

C71

1 49.79 23.16 12.79Southland-

C3

1 9.60 7.49 5.74

2 55.88 23.50 12.67 2 11.29 8.51 6.30

3 34.07 20.16 13.01 3 13.23 9.55 6.99

4 68.19 24.97 9.45 Minster

Fort

Recovery -

C30

1 17.42 10.32 1.97

5 58.97 23.81 11.14 2 15.05 9.37 8.98

6 66.36 28.42 13.20 3 15.50 9.96 3.26

7 66.01 32.91 0.03 Blank Pike-

C160

1 22.08 11.07 7.33

8 72.89 36.06 7.85 2 23.79 11.36 7.28

9 89.64 36.29 10.18 3 24.94 12.05 7.82

Fairground

-FG

1 151.43 40.00 9.87 Neptune

Mendon-

C161C-7

1 14.39 9.23 5.96

2 144.90 39.67 16.13 2 14.16 9.39 6.24

3 54.94 18.02 10.04 3 14.44 10.00 6.82

4 31.79 18.59 8.14 Harris-

C175B-8

1 7.12 6.20 5.41

5 95.87 41.28 14.65 2 14.86 10.84 7.47

6 30.87 15.86 5.09 3 10.88 8.76 6.33

7 98.00 9.20 3.75

Dutton-

C230A-3

1 34.88 17.70 9.94 8 134.55 56.27 12.63

9 26.06 8.39 4.94 2 26.26 14.87 9.23

Kossuth

Loop-

C216A

1 162.30 43.85 8.15

2 238.65 43.53 12.92

3 28.07 14.72 8.93

3 244.78 46.90 13.47

It is significant to mention that the Prima 100 LWD and FWD repeatability are

slightly weak in rough or soft surfaces rather than in stiff/hard surfaces. Measured

deflections on rough or soft surfaces (typically aggregate overlay and full depth grinding

in this study) from multiple sensors across all test sites resulted variations and/or somewhat

66

identified to be outliers in the deflection data. Figures were addressed in appendix B in this

study. The prima 100 LWD sensor deflection basin for Southland Road is shown in

Figure 3.8 as follows:

Figure 3.8: LWD Deflection Basins, Same Loads, Cluster # 3, Southland Road (Aug-C3-15), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate

3.4 Backcalculation Methodology and Pavement Layer Moduli

Table 3.3 shows a basic backcalculation procedure. The output of this methodology

are modulus of elasticity, effective structural number, layer coefficients, and subgrade

resilient modulus of pavement layer system.

0

10

20

300 12 24

LW

D S

enso

r D

efle

ctio

n, m

ils

Radial Distance, (in)

2340 lb

2400 lb

2360 lb

67

Table 3.3: Representation of Backcalculation Procedure (Murillo & Bejarano, 2013)

Direct Calculation E, D, ν d , σ, ε

Backcalculation E, σ, ε d, D, ν

Where:

E = Elastic Modulus of materials.

Ν = Poisson’s Ratio

D = Layer thickness

D = Defection of the pavement structure

Ε = Strain

Σ = Stress on each layer of the pavement structure.

3.4.1 AASHTO Method (Section 5.4.5, FWD)

The AASHTO (1993), Design Guide for Pavement Structures establishes a

pavement analysis method based on FWD testing results. This method is mostly used to

calculate the subgrade resilient module (MR) and effective structural number. The method

works based on elastic layer theory using the deflection at a sufficiently large distance from

the center load to calculate the MR value. MR is then used as an input parameter thereafter

to calculate the effective structural number (pavement structural capacity) and layer

coefficients (hereafter referred to as the AASHTO 5.4.5). The MR is calculated using

Equation 3.2.

68

M . ∗

∗andr 0.7a Equation (3.2

Where:

MR = subgrade resilient modulus, psi

P = load magnitude, lb (9,000 lb recommended by AASHTO).

dr = measured deflection at distance r from the center of the load, inches.

r = radial offset (distance from plate center, inches).

AASHTO 5.4.5 proposes a radial spacing which exceeds 70% of the effective

radius (ae) of the stress bulb at the subgrade/pavement interface. The effective radius can

be estimated using Equation 3.3.

a a D ∗ Equation (3.3)

Where:

ae = effective radius of stress bulb at subgrade/pavement interface, inches.

a = FWD load plate radius, inches.

D = total pavement depth above subgrade, inches.

MR = subgrade resilient modulus, psi.

Ep = effective modulus of all pavement layers above the subgrade, psi.

AASHTO 5.4.5 also presents Equation 3.4 for predicting the modulus of all layers

above the subgrade called, the effective modulus of the pavement structure (Ep). This can

be determined in terms of calculated MR and total thickness of layers above the subgrade.

69

d 1.5pa

∗

Equation (3.4)

Where:

d0 = measured deflection at the center of load, adjusted to a temperature of 20˚C

(68˚F), inches.

p = pressure of load plate (P/πa2), psi.

D = total thickness of all layers above subgrade, inches.

a = FWD load plate radius, inches.

EP = effective modulus of all pavement layers above subgrade, psi.

MR = back calculated subgrade resilient modulus, psi.

The EP value can be simply determined from equation 3.4 in an excel spreadsheet

by using an iterative procedure such as the built‐in Solver function. Also, this can be

estimated by using the bisection method that will produce the measured center deflection

(d0). After the Ep has been calculated, Equation 3.3 is used to verify that the reasonable

radial distance parameter (r) criteria has been met. Moreover, the effective structural

number which represents the structural strength of the overall pavement sustains traffic

loadings. The effective structural number (SNeff) for the entire pavement system can be

calculated based on the total thickness of the pavement system and its computed effective

modulus using Equation 3.5.

70

SN 0.0045 ∗ D ∗ E Equation (3.5)

Where:

SNeff = effective structural number of in‐place pavement.

D = total pavement depth above subgrade, inches.

Ep = effective modulus of all pavement layers above the subgrade, psi.

AASHTO 5.4.5 procedure was applied based on FWD field-collected data for

flexible pavement. The calculated effective structural number, subgrade modulus, total

thickness of pavement, effective modulus, and central deflections for each site are shown

in appendix C of this study.

3.4.2 Determining Layer Coefficients from AASHTO 5.4.5 Equations

There is no standard method of calculating layer coefficients (ai) for flexible

pavement based on FWD data. In order to find layer coefficients, knowing effective

structural number of pavement system is necessary. Therefore, the effective structural

number for every site was determined as discussed in the preceding section. The proposed

equation of structural number structural number is a combination of the thicknesses for

each layer and layer coefficients of that specific layer as shown in below:

SN a D a D a D Equation (3.6)

71

Where:

a1, a2, a3, = are empirical layer coefficients for the pavements layers (surface, base,

and subbase). D1, D2, D3 = thicknesses for the surface, base, and subbase of the

pavement layer system.

The structural layers coefficients were determined by solving simultaneously

equations for structural number of all sites with similar material, while there was only one

equation representing each site. Any possible combination of equations which was

necessary in computing layer coefficients due to effective structural number was

considered. The process consisted of combining equations form several sites in different

groups based on similar materials. Solutions containing negative values and values larger

than 1.0 were considered non feasible solution in this study. Materials characterized layer

coefficient determined from solving simultaneous equation are shown in Table 3.1 below.

72


Materials Types Calculated Layer Coefficient Range

Portland Cement Concrete (PCC) 0.46 - 0.99

White topping 0.35 - 0.86

HMA 0.40 - 0.43

Mechanical FDR 0.12 - 0.69

Fiber Cement 0.03 - 0.85

Cement FDR 0.08 - 0.78

70 30 asphalt/Cement 0.14 - 0.34

Concrete Steel 0.13 - 0.88

Full Depth Grindings 0.08 - 0.22

Partial Depth Grindings 0.19 - 0.41

Lime FDR 0.13 - 0.28

Permazine FDR 0.05 - 0.11

Fabric Reinforced Stone 0.19 - 0.36

Fly Ash FDR 0.13 - 0.16

Brick & 411 0.18 - 0.52

Surge & 411 0.05 - 0.10

Motorpave 0.03 - 0.85

Asphalt FDR 0.10 - 0.25

Geogrid 0.17 - 0.24

Aggregate Overly 0.11 - 0.14

The layer coefficients ranged in Table 3.4 was summarized as box plots in

Figure 3.9 to show the variability of the data.

73

Figure 3.9: Box Plot of Layer Coefficients for Each Widening/Construction Treatment, Layer Type Based on AASHTO 5.4.5.

As shown in Figure 3.9, the bottom of the box represents the first quartile (Q1) and

the top represents the third quartile (Q3). The line within the box (a line across the box)

represents the median value and lastly the bold dot within the box represents the mean of

the response within that group (the mean value). The two lines extending from the box

upward and downward, each represents values outside the first and third quartile.

Furthermore the horizontal bars at the end of upper and lower extended vertical lines

represent the maximum and minimum values respectively. To determine the spread and

skew of the data, box plots are useful. The plots can be used to identify outliers for removal

from the data analysis. According to Tukey (1977), the space between Q3-Q1 is known as

0.73

0.62

0.43

0.24

0.420.38

0.220.44

0.17 0.300.20

0.08

0.270.15

0.33

0.07

0.46

0.15

0.21

0.120.000.100.200.300.400.500.600.700.800.901.001.10

Lay

er C

oeff

icie

nt

Layer Type

Box Plot for Layer Coefficient (FWD AASHTO 5.4.5)

Bottom 2Q Box 3Q Box Mean

74

inter quartile range (IQR) and this measure is significant in detecting outliers in the data.

“Any observation falling outside Q3+1.5*IQR or Q1-1.5*IQR could be flagged as

potential outlier”. In Figure 3.7, the observation falling outside 0.34 or 0.045 for

Mechanical FDR, 0.38 or 0.05 for 70/30 asphalt/cement, 0.25 or 0.1 for Full depth

grindings, and 0.83 or 0.05 for Motorpave could be flagged as potential outliers for

characterized materials.

Meanwhile, the box plot can be further used when comparing various materials. If

the boxes do not overlap, the two layer coefficients are difference from each other. If the

boxes overlap, but do not contain both medians, the layer coefficients are likely different

from each other. When the boxes overlap and contain both medians, then both materials

are considered to have the same layer coefficient values.

3.4.3 AASHTO Method (Section 2.3.5, LWD)

The AASHTO (1993), Guide for Design of Pavement Structure describes a

procedure for estimating the structural layer coefficients from laboratory data. In this study,

AASHTO section 2.3.5 hereafter AASHTO 2.3.5 was used to determine layer coefficients

for granular base and subbase layers using backcalculated modulus values. This section

proposed the relationship via equation 3.7 for base materials such as gravel or crushed

gravel from its elastic (resilient) modulus.

a 0.249 log E 0.977Equation 3.7

75

Where:

EBS = base layer modulus.

a2 = base layer coefficient.

For the crushed stone as a subbase layers equation 3.8 was used as described below:

a 0.227 log E 0.839Equaiton 3.8

Where:

ESB = subbase layer modulus.

a3 = subbase layer coefficient.

Also, AASHTO 2.3.5 provides a graph shown in Figure 3.8, which can be used to

determine the structural layer coefficient of asphalt concrete surface course from its elastic

(resilient) modulus. Extrapolation procedure was used to determine structural layer

coefficients when the surface moduli that exceeded the standard range of proposed chart

by AASHTO. The graph is described as follows:

76

Figure 3.10: Chart for Estimating Structural Layer Coefficient of Asphalt Concrete

(AASHTO, 1993)

Moreover, the AASHTO 2.3.5 provides the chart show in Figure 3.11 for estimating

the structural layer coefficient of cement-treated base materials according to its elastic layer

modulus. Extrapolation technique was used to determine structural layer coefficients from

the cement-treated base modulus that exceeded the proposed standard range of the chart.

The chart is showing below:

77

Figure 3.11: Used Chart for Cement-Treated Base Materials, (AASHTO, 1993).

The material characterized layer coefficients range determined from the above

procedure are shown in Table 3.5.

78

Table 3.5: Calculated Layer Coefficients Range Based on Material Types, AASHTO 2.3.5 LWD



White topping 0.34 - 0.68

HMA 0.33 - 0.56



Cement FDR 0.01 - 0.28

70 30 asphalt/Cement 0.21 - 0.35




Lime FDR 0.01 - 0.21



Fly Ash FDR 0.14 - 0.18

Brick & 411 0.12 - 0.30

Surge & 411 0.10 - 0.20



Geogrid 0.02 - 0.20


Also, the results presented in Table 3.5 are graphically presented in the box plot in

Figure 3.12

79


Layer Type Based on AASHTO 2.3.5.

As shown in Figure 3.12, few outliers were identified in the above box plot as

indicated in Figure 3.10. Generally, any observation falling outside 0.90 or 0.42 for PCC,

0.25 or 0.05 for Full depth grindings, 0.30 or 0.14 for Concrete Steel, 0.28 or 0.01 for Lime

FDR, 0.33 or 0.20 for Brick & 411, and 0.38 or 0.08 for Motorpave could be flagged as

potential outliers for characterized materials.

3.4.4 Rohde’s [1994] Method of Determination of Pavement Structural Number

and Subgrade Modulus from FWD Testing.

Rohde (1994) developed a procedure for obtaining the structural number of a

pavement system based on FWD measurements. The structural number equation adopted

0.64

0.490.53

0.18 0.16

0.10

0.290.23

0.16

0.22

0.14

0.130.23

0.16

0.25

0.23

0.27

0.25

0.09

0.22

0.000.100.200.300.400.500.600.700.800.901.001.10

Lay

er C

oeff

icie

nt

Layer Type

Box Plot for Layer Coefficient (LWD AASHTO 2.3.5)


80

the one modified by the Transport Research Laboratory (TRL) in 1957 which was used in

the World Bank Highway Design and Maintenance pavement performance model in the

United Kingdom (Janoo, 1994). Modified structural number (SNC) equation is described

as:

SNC a h SN Equation 3.9

Where:

SNC = Modified Structural number,

SNsg = 3.51(log CBR) 2 – 1.43,

CBR = in situ California bearing ratio,

ai = material and layer coefficient, and

hi = layer thickness (inches)

Rohde assumed that the surface deflection measured at an offset of 1.5 times the

structural pavement thickness (h) is due to the subgrade only. After comparing this

deflection with the maximum or peak deflection, he established the Structural Index of the

Pavement (SIP). The SIP correlated with the deflection above the subgrade is defined in

Equation 3.10 as follows:

SIP D D . Equation 3.10

81

Where:

SIP = Structural Index of pavement,

D = Maximum or peak deflection measured under a standard 9000lb (40KN) FWD

Load.

D1.5Hp = 1.5 times Hp offset measured surface deflection under 9000lb (40KN) of

FWD impulse load.

Hp = total pavement thickness.

Rohde hypothesized the SIP must be fully correlated with the stiffness of the

pavement structure and thus the structural number. Rohde investigated and developed the

best relationship between structural number and SIP based on regression analysis. A

relationship of Equation 3.11 was selected:

SN k SIP h Equation 3.11

Where:

SN = Structural number, inches

SIP = Structural index of pavement (µm),

Hp = total pavement thickness (mm),

k1, k2, k3 = Coefficient as listed in Table 3.6.

82

Table 3.6: Coefficient for Structural Number versus SIP Relationships, (ROHDE, 1994).

Surface Type k1 k2 k3 r2* n**

Surface Seal 0.1165 -0.3248 0.8241 0.984 1944

Asphalt Concrete 0.4728 -0.481 0.7581 0.957 5832

* Coefficient of determination **Sample Size

Moreover, Rohde (1994) used field-measured FWD deflection data to obtain the

subgrade modulus (Esg). He developed a second index called the structural index of

subgrade (SIS). This index was defined as:

SIS D . D Equation 3.12

Where:

Ds = measured deflection spaced 30 inches from the center of the loading plate.

The subgrade modulus can be describes as follows:

E 10 SIS Hp Equation 3.13

Where:

Esg = subgrade modulus, Mpa

k4, k5, k6 = coefficients as given in Table 3.7.

83

Table 3.7: Coefficient for E versus SIS Relationship, (Rohde, 1994)

Total Pavement Thickness k4 k5 k6 r2 n

Hp ≤ 380 mm 9.138 -1.236 -1.903 0.862 2592 380 mm < Hp ≤ 525 mm 8.756 -1.213 -1.780 0.810 2592 525 mm < Hp 10.655 -1.254 -2.453 0.809 2592

The Rohde method of determination of effective structural number, and subgrades

modulus was applied to FWD field measured deflections to improve and confirm

correlations between FWD and LWD. Results of this procedure are presented in Table 3.8.

Table 3.8: Effective Structural Numbers and Subgrade Modulus from Rohde Procedure

Road Name Structural Number

Subgrade Modulus Esg (ksi)

Road Name Structural Number

Subgrade Modulus Esg (ksi)

Christy-C164 10* 20 Southland-C3-15 4 14

Blosser-C72-07 2 9 Minster Fort

Recovery-C30 -16 3 12

Mountain Perry-C30-11

7* 33 Dutton (C230A) 3 11

Arch Hill-C82-03 10* 22 Neptune Mendon-

(C161C) 4 25

Vista View Drive 9* 22 Harris (C175B) 4 20

Air Park 9* 25 East Shelby-C71-08 2 8

Airport-C797-02 10* 21 Mansfield-C6-14C 2 10

Mansfield-C6-14 3 10 Blank PikeC160-12 3 18

Elliott Road-C53-10 4 13 Salt Creek Road-

C44-19 3 48

Elliott-C53-18 5 20 Dietz Ln-C449-06 3 21

84

Table 3.8: Continued

Banner School-C70-09

5 10 Apollo-C12-05 5 15


5 23 Canyon-C54-04 9* 59

Blosser-C72-15 4 16 Chase-C66-06 8* 54

Rosedale-C117 -01A 2 9 Meter-T269-03 5 57

Rosedale-C117 - 01B 3 11 Plum Run-C8-06 2 23

Rosedale-C117-03 3 14 Birmingham-C10-02 2 30

WCC- C123 04 2 13 Unionvale-C12-03 8* 54

The Bend-C134-12 6 10 Bakers Ridge-C51-

04 6 44

Krouse-C134-13 14* 22 Fountain-C39-16 2 12

Harding-C195-02 4 8 Flory- C68-08 4 10

Kite-1-C22-14 2 29 Blosser-C72-06 1 7

Heck Hill-C62-07 3 29 WCC-l-C123-17 2 16

Nine Miles-C37-12 1 3 Hammon-T187-05 1 12

Nine Miles-C37-20 2 3 Taylor Blair-C14-S4 2 4

Sullivan-C45-15 2 11 Taylor Blair-C14-N5 1 5

Lippincott-C115-17 2 6 MCEO 3 21

Dallas-C184-19 2 11 Charleston

Chillicothe-C15B-02 1 4

Old Troy Pike-C193-18

1 12 Davis-C95-03 3 18

Old Troy Pike-C193-21

2 6 Rural Dale-C31-18 2 20

Pisgah-C236-03 3 13 Ellis Dam-C49-08 2 22

Fairground (West) 1 12 Powelson-C49-16 2 10

Pledge-T370-01 1 4 Narrows-C76-12 2 13

Meter-T269-02 1 43 Friendly Hill-C418 -

10 2 15

Kossuth Loop-C216A-03

1 6 New Hope-C20 2 10

Fairground (Center) 1 9 Southern-C107-20 2 63

Fairground (East) 1 18 Norfield-C64-14 2 19

85

In above table the structural numbers greater than 6 were concrete pavements

and/or concrete steel.

In addition, materials characterized structural layer coefficients range determined

from Rohde method are shown in Table 3.9

Table 3.9: Calculated Layer Coefficients Range Based on Material Types, Rohde [1994] Method



Whitetopping 0.23 - 0.73

HMA 0.40 - 0.43



Cement FDR 0.002 - 0.50

70 30 asphalt/Cement 0.003 - 0.18




Lime FDR 0.017 - 0.25



Fly Ash FDR 0.12 - 0.15

Brick & 411 0.027 - 0.48

Surge & 411 0.053 - 0.18



Geogrid 0.024 - 0.18


To better see the variability of the data and due to the high volume of the collected

and analyzed data, Table 3.9 values were graphically plotted. A figure of graphical box

plots for the layer coefficients is shown in Figure 3.13.

86

Figure 3.13: Box Plot Showing Layer Coefficients for Each Widening/Construction

Treatment as Determined Using Rohde [1994] Procedure

In Figure 3.11, a few outliers were identified. Typically any observation falling

outside describing ranges could be flagged as potential outlier. For PCC 1.12 or 0.52, White

topping 0.80 or 0.40, Mechanical FDR 0.24 or 0.05, Full depth grindings 0.20 or 0.07,

Partial depth grindings 0.25 or 0.04, Lime FDR 0.25 or 0.02, Fabric reinforced stone 0.28

or 0.05, Asphalt FDR 0.21 or 0.02, and lastly Geogrid 0.15 or 0.03.

3.4.5 Pavement Layer Moduli

As previously discussed, Modulus 6.0 and Evercalc 5.0 were used to backcalculate

layer moduli during this study. These programs are the most commonly used

backcalculation programs that can evaluate pavement structural capacity up to five

different unknown layers, (Tawfiq, 2003). Also, the Dynamic Cone Penetration (DCP) was

0.77

0.55

0.43

0…

0.36

0.270.10

0.44

0.13 0.14 0.140.05

0.17

0.13

0.23

0.11

0.38

0.11

0.07

0.12

0.000.100.200.300.400.500.600.700.800.901.00

Lay

er C

oeff

icie

nt

Layer Type

Box Plot for Layer Coefficients, Rohde Procedure

Bottom 2Q Box

87

used to identify pavement material types and layer thicknesses. For each tested

location/section, a separate layer thickness was assigned based on coring results and DCP

testing. According to ASTM-D5858 (2003), a Poisson’s ratio, 0.35, along with field

temperature obtained from FWD testing were some initial software inputs.

Evercalc 5.0 software works based on multi-layer elastic forward calculation

subroutines. To begin the backcalculation process, a general file including but not limited

to the following input parameters must be generated: Loading plate radius, number of

layers, number of sensors, sensor spacing, and Poisson’s ratio. Additional input parameters,

options pertaining to the treatment of three drops at almost same load levels, and deflection

basin for one of the test section, Pisgah road, Pisgah-C-236-3, Champaign County are

shown in Figure 3.14 through Figure 3.16 respectively.

88

Figure 3.14: Evercalc 5.0 General File Data Entry Screen for Pisgah Road, Champaign

County.

Figure 3.15: Evercalc 5.0 LWD Deflection File screen for Pisgah Road, Champaign

County.

89

Figure 3.16: Evercalc 5.0 LWD Deflection Basin for Pisgah Road, Champaign County

Similarly, Modulus 6.0 works based on the linear elastic theory. WESLEA, a

layered elastic solution platform developed by US Army Corps of Engineers covered in

Modulus as a subroutine to perform the forward calculation for building a database of

calculated deflection basin (Tutumluer et al., 2009). The general window of Modulus 6.0

is shown in Figure 3.17.

90

Figure 3.17: Main Window of Modulus 6.0 (Liu and Scullion, 2001)

Figure 3.18: Backcalculation Routine Window, Krouse Road, Defiance County.

91

Moreover, a number of backcalculated layer moduli were computed based on

material properties. A summary of backcalculated layer moduli from Modulus 6.0, FWD

testing and from Evercalc 5.0, LWD testing, are reported in Appendix D. Outliers amongst

the characterized material layer moduli for each section were also identified. A similar

graphical representation of backcalculated layer moduli, computed from seven FWD, three

LWD, sensor deflections with three varying applied loads, for three typical test sections in

each location, with the 11.8-in (300mm) plates of both devices can be seen in the box plots

presented in Figure 3.19 and Figure 3.20, respectively.

Figure 3.19: Box Plot Showing Backcalculated Layer Moduli for Each Widening Treatment as Determined Using Modulus 6.0 Software, FWD Testing.

2,3431,702

800

33

581361

104 100 52 71831

15 77 44 9832

25449 25 21 10

0

500

1000

1500

2000

2500

3000

Bac

kcal

cula

ted

Lay

er M

odul

i (ks

i)

Layer Type

Backcalculated Layer Moduli Box Plots, Modulus 6.0 (FWD)


92

In the box plots of Figure 3.19, any observation falling outside describing ranges

could be flagged as potential outlier as described: For PCC 3718 or 1190, White topping

2617 or 780, Fiber Cement 1791 or 688, Cement FDR 1459 or 773, Concrete Steel 186 or

8, Lime FDR 1173 or 500, and Motorpave 1298 or 649.

Similarly, backcalculated layer moduli box plots based on materials

characterization from Evercalc 5.0, LWD testing with three sensor deflections

measurements, are shown in Figure 3.20.


Treatment as Determined Using Evercalc 5.0 Software, LWD Testing.

2,288

1,758

808

55

636

40712839 43 69382

19 99 3894 72222 74 17 4712

0

500

1000

1500

2000

2500

3000

Bac

kcal

cula

ted

Lay

er M

odul

i (ks

i)

Layer Type

Backcalculatd Layer Moduli Box Plot, Evercalc 5.0 (LWD)


93

Figure 3.20 indicates backcalculated layer moduli from LWD deflection data. Few

outliers were identified. Any observation falling outside the ranges could be flagged as

potential outlier as described: For PCC 3201 or 1535, White topping 3530 or 64, HMA

1604 or 6, Fiber Cement 1242 or 35, Cement FDR 790 or 6, 70/30 asphalt/cement 230 or

18, Lime FDR 1689 or 946, and lastly for Motorpave 262 or 17.

94

CHAPTER 4 RESULTS AND DISCUSSION

4.1 Introduction

As described in Chapter 2 of this study, several studies have been done previously

to determine the relationship between FWD and LWD. Various pavement structures and

material types were examined in these studies. In this chapter the author conducted a

comprehensive statistical analysis to correlate FWD and LWD sensor deflections,

backcalculated layer moduli, and layer coefficients obtained from their measurements. The

best correlation parameters are based on routine regression analysis using a statistical

software, the Statistical Package for the Social Sciences (SPSS). Also FWD and LWD

correlations was verified and proved by the Rohde method using, effective structural

numbers, layer coefficients, and subgrade moduli.

4.2 Regression Analysis

As mentioned previously, in order to determine the correlation between the FWD,

LWD measurements and prove it by the Rohde methods, a statistical analysis using SPSS

and Microsoft excel are used to perform an extensive regression analysis on data described

in the previous chapter. The main objective of regression analysis is to obtain the parameter

in the least squared error model that can predict the FWD layer coefficients, layer moduli,

and effective structural number from the LWD measurements and the Rohde method with

their corresponding coefficient of determination, R2, standard error, and statistical

significant level. Linear and nonlinear regression models were utilized in this study.

According to Field (2013) a common form of linear regression model is describes as:

95

Y b b x ε Equation 4.1

Where:

Yi = Dependent variable,

b0 = intercept value,

b1 = slope of the regression, and

εi = residual term (difference between the prediction and actual).

The measurements determined from LWD and the Rohde method were used as the

independent variable in comparing with their dependent variable, FWD measurements, in

all the regression models. However, the Rohde method is used as a dependent variable in

the regression model obtained, while comparing it with its independent variable LWD.

Moreover, the coefficient of determination, R2, statistical significance level, and

the standard error are considered to be reported for each regression model developed in

this study. The coefficient of determination, R2, is a number that represents the proportion

of variation in the dependent variable which is predictable from the independent variable

and has a value which ranges from 0 to 1. A perfect correlation exists when the value is

equal to one, this means all points lie on the suggested least square line. The significance

level is the result for a given null hypothesis test for which a typical P-value of less than or

equal to 0.1, 0.05, and 0.01 is considered statistically significant. Lastly, the standard error

is define as the standard deviation of the sampling distribution of a statistic or can be the

square root of the mean square errors, MSE, (Nazzal, 2003).

96

4.3 Comparison FWD and LWD Sensor Deflections

The author conducted a comprehensive regression analysis to find the best

correlation between FWD and LWD sensor deflection data. To compare collected

deflection data, all the deflection data from both the FWD and LWD were normalized to

9000 lb (LWD sensor deflections were extrapolated linearly to 9000 lb). Since the Prima

100 LWD has geophones/sensors up to 24 inches only (600mm) from the center of the

loading plate, the deflection data of both devices were compared at 0, 12, and 24 inches

about (0, 300, and 600mm) from the center of the loading plate. Each sensor has a subscript

(0, 1, and 2.) which represents the deflections at 0, 12, and 24 inches (0, 300, and 600mm)

respectively. The Statistical Package for the Social Sciences (SPSS) is used to perform the

regression analysis between FWD and LWD sensor deflections individually.

Also, FWD and LWD sensor deflection data were filtered to detect outliers using

SPSS. Some abnormal deflections amongst normal deflections were identified.

Accordingly, a decision was made to exclude/remove outliers due in part to plate vibration

and/or soft surface layers from regression modeling prior to data analysis. A typical

normalized FWD and LWD sensor deflections of all tested sites, at 0, 12, and 24 inches

(D0, D1, and D2) from the center of loading plate and a brief list of the detected outliers are

available in appendix E of this study.

4.3.1 Deflections at the Center of Loading plate, (D0)

Deflection data collected from the FWD and LWD measurements at the center of

11.8 inches (300mm) loading plate were compared. For all tested locations/sections, the

FWD central deflection data at (0.0 distance) is plotted against LWD. The regression

97

analysis yielded a nonlinear model with a power function that gives the best fit based on

the best regression coefficient of determination, R2 = 0.85. It is worth mentioning that other

linear and nonlinear relationships (exponential, polynomial, and linear) that improve the

fit model were also checked, but did not fit into the data point. To better illustrate the

relationship, a separate plot of regression model was developed and presented in

Figure 4.1below:

Figure 4.1: Comparison Between FWD and LWD Deflections at the Center of Loading Plate, (D0)

As previously stated, the regression model shown in Figure 4.1 yielded nonlinear

model. Overall, as indicated in the figure above, a strong correlation exists between the

Prima 100 LWD and FWD central sensor deflections (D0). A nonlinear regression model

is defined in Equation 4.2 as follows.

y = 1.4607x0.8831

R² = 0.8521

0

1020

3040

50

6070

8090

100

0 10 20 30 40 50 60 70 80 90 100

Nor

mal

ized

FW

D S

enso

r D

efle

ctio

ns, (

mil

s)

Extrapolated LWD Sensor Deflections, (mils)

FWD versus LWD Center Deflecions, (D0)

98


With R2 = 0.85, and correlation coefficient R = 0.86, it is important to note that the

model was considered statistically significant with a probability level of P = 0.00 < 0.001

across all test sites. Meanwhile, the nonlinear regression equation at the plate center

explored by Horak et al. (2008) produced a moderate correlation (R2 = 0.61) and the

regression equation was reported to be DFWD = 1.6178(dLWD) 0.8236. Where various layer

thickness (75 and 100mm) of sand treated with emulsion constructed on Berea red type

sand subbase and subgrade (Horak et al., 2008). The Horak et al. (2008) equation along

with the one suggested by the author at the center of loading plate were plotted in Figure 4.2

below:

Figure 4.2: DFWD vs. dLWD Correlation, Comparison to, (Horak et al., 2008)

y = 1.4607x0.8831

R² = 0.8521

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Nor

mal

ized

FW

D S

enso

r D

efle

ctio

n, (

mil

s)


Power(Suggested bythe author)

Power (Horaket al., 2008)

99

4.3.2 Deflections at Radial Offset Distance r = 300mm, (D1)

Deflections measured at the second sensor with radial distance of 12-inches about

(300mm) from center loading plate of both FWD and LWD were compared. The plot of

regression model is presented in Figure 4.3. The regression analysis yielded a nonlinear

model with a power function that gives the best fit based on the best regression coefficient

of determination, R2 = 0.78.

Figure 4.3: Comparison of FWD and LWD Deflections at r = 300mm from the Center of Loading Plate, (D1)

As a result, Figure 4.3 indicates that a correlation was made with the correlation

coefficient, R = 0.82, between the Prima 100 LWD and FWD sensor deflection at r =

300mm radial distance from center of loading plate. The Prima 100 LWD sensor

y = 1.6881x0.9049

R² = 0.7752

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Nor

mal

ized

FW

D S

enso

rD

efle

ctio

ns, (

mil

s)


FWD versus LWD at r = 300mm (D1)

100

deflections (D1) correlates better to FWD. A nonlinear regression model with a power

function is defined in Equation 4.3 below:

D 1.6881 D . Equation 4.3

With R2 = 0.78, and correlation coefficient R = 0.82. It is important to note that the


across all test sites.

4.3.3 Deflections at Radial Offset Distance r = 600mm, (D2)

Lastly, as the LWD has geophones only up to 24-inches (600mm) from the center

of the loading plate, therefore, the sensor deflections measured at the third sensor, D2, were

also compared with FWD. The regression analysis yielded a linear model that is presented

in Table 4.1 below:

Table 4.1: Statistical Analysis Model Summary of FWD vs. LWD Sensor Deflections (D2).

Model Summaryb

Model R R

Square Adjusted R Square

Std. Error of the

Estimate

Change Statistics R Square Change

F Change

df1 df2 Sig. F

Change 1 .851a .724 .723 2.45526 .724 660.174 1 252 .000

a. Predictors: (Constant), LWD Sensor Deflections (D2) b. Dependent Variable: FWD Sensor Deflections (D2)

Overall, as indicated in Table 4.1, a strong correlation with the correlation

coefficient, R = 0.85, exists between the Prima 100 LWD and FWD sensor deflections

(D2). The coefficient of determination, shown in column three is a measure of how much

101

of the variability in the outcome is accounted for by the predictors. For this model, its value

is about, R2 = 0.72, which means that the LWD sensor deflections (D2) accounts for 72%

of the variation in the FWD sensor deflections. The adjusted R2 in column four of the above

table gives us an idea of how good our model generalizes while typically, its value should

be the same, or very close to the value of R2. In this example the difference is very small

(the difference between the values is 0.724 – 0.723 = 0.001, or 0.1%). The standard error

of 2.45 is reported with the significance level of R2 which can be tested using F-ratio. So,

this model example causes R2 to change from 0 to 0.72, and this change in the amount of

variance explained gives rise to an F-ratio of 660.174, which is significant with a

probability level of, P = 0.00 < 0.001, means the model is considered statistically

significant (Field, 2013). To better illustrate the relationship between FWD and LWD

sensor deflection, a separate plot of (D2) regression model was developed and presented in

Figure 4.4 below which gives the best fit based on the best regression coefficient of

determination value.

102

Figure 4.4: Comparison of FWD and LWD Deflections at r = 600mm from the Center of Loading Plate, (D2)

Figure 4.4 shows a similar and better linear relationship at radial offset distance of

600mm. A linear regression model is defined in Equation 4.4

D 0.9284 D 2.6519Equation 4.4

With R2 = 0.72, and correlation coefficient R = 0.85, it is important to note that the


across all test sites.

y = 0.9284x + 2.6519R² = 0.7237

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Nor

mal

ized

FW

D S

enso

rD

efle

ctio

ns, (

mil

s)


FWD versus LWD at r = 600mm, (D2)

103

4.4 Area Under Pavement Profile (Deflection Basin Parameter)

Upon positive relationships between FWD and LWD sensor deflections in the

previous section, the author was interested to examine if the area under pavement profile

between AUPP (FWD 4 sensors) and AUPP (LWD 3 sensors) has any relationship.

Therefore, for LWD results at radial distances 0, 12, and 24 inches about (0, 300, and

600mm) were considered rather than 0, 12, 24, and 36 inches from the load center. The

modified area under pavement profile is now indicated in Figure 4.5 as follows:

Figure 4.5: AUPP (LWD 3 Sensors) Modified Deflection Basin Parameter

From the results of Figure 4.5 presented above, all LWD sensor deflections were

normalized to 9000 pounds. The new AUPP deflection basin shape parameter model is

shown in Equation 4.5.

104

AUPP 12 3D 2D D Equation 4.5

Where:

D0 = FWD sensor deflection at the center of the loading plate, mils



A comprehensive regression analysis was performed to find the relationship

between AUPP (FWD 4 Sensors) and AUPP (LWD 3 Sensors). The findings from

regression analyses support the findings demonstrated in the previous section. The results

in Figure 4.6 below show evidence of the correlation between AUPP’s. Fitting a linear

trendline to the data reveals a high correlation across all sites.

Figure 4.6: AUPP Comparison of FWD and FWD across All Sites

y = 2.2664x0.8789

R² = 0.8283

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120 140 160 180

AU

PP

(FW

D 4

-Sen

sors

)

AUPP (LWD 3-Sensors)

AUPP FWD versus AUPP LWD

105

As indicated in Figure 4.6, regression analysis yielded a nonlinear model with a

power function that gives the best fit based on the best regression coefficient of

determination, R2 = 0.83. A nonlinear regression model is defined in Equation 4.6 as:

AUPP 2.2664 AUPP . Equation 4.6

With R2 = 0.83, while correlation coefficient R = 0.86. It is important to mention

that the model was considered statistically significant with a probability level of P = 0.00

< 0.001.

Overall, the model presented in Equation 4.6 between FWD (AUPP) and LWD

(AUPP) demonstrated high relationships. The results presented in Equation 4.6 was

adopted and substituted into Equations 2.3 and 2.4. The tensile strain at the bottom of the

asphalt layer (εAC), for full-depth asphalt is computed from Equation 4.7 in term of LWD

measurements as below:

Log ε 0.9000 ∗ Log AUPP 1.3649Equation (4.7

Similarly, for aggregate base pavements, the tensile strain can be predicted using Equation

4.8 as follows:

Log ε 0.7216 ∗ Log AUPP 1.5017Equation 4.8

106

Where:

εAC = tensile strain at the bottom of the HMA layer, macrostrain

AUPP = Area under Pavement Profile based on LWD 3-sensors deflection, mils.

This method of predicting the (εAC) is to use the AUPP value from LWD

measurements with 3 sensors. The suggested models presented in Equations 4.7 and 4.8

can be used to design overlays and predict remaining life of pavement sections without

using backcalculation technique.

4.5 Comparison of Backcalculated Layer Moduli

The next component in correlations between FWD and LWD within this study was

the backcalculated layer moduli of pavement layer system. For all sites, the FWD

backcalculated layer moduli were plotted against backcalculated layer moduli measured

with Prima 100 LWD in the box plot of the previous chapter (Figure 3.19 and Figure 3.20,

respectively). The SPSS software was used to perform a comprehensive regression

analysis on backcalculated layer moduli in order to find the best correlation between

backcalculated layer moduli obtained from Modulus 6.0 and Evercalc 5.0. Also, the

subgrade modulus obtained from Rohde method was correlated with the backcalculated

subgrade modulus. The results of backcalculated layer moduli upon FWD and LWD

measurements are now plotted and presented based on material properties in Figure 4.7

below:

107

Figure 4.7: Backcalculated Layer Moduli of Pavement Layers Based on FWD and LWD Measurements

Figure 4.7 shows a column chart of the averaged backcalculated layer modulus

based on material type. Comparing the FWD modulus with the LWD modulus in the figure

above, it is noted, the FWD modulus is typically slightly less than or somewhat equal to

the LWD modulus. However for two materials, lime FDR and concrete steel, they are

slightly greater than the LWD modulus. To find a better relationship of layer moduli from

FWD and LWD deflection data, statistical analysis was conducted. A model summary from

the SPSS outputs is shown in Table 4.2.

0

500

1000

1500

2000

2500L

ayer

Mod

uli (

ksi)

Material Type

FWD vs. LWD, Layer Moduli

LWD

FWD

108

Table 4.2: Statistical Analysis, Model Summary of FWD & LWD Procedures. Model Summaryb

Model R R

Square Adjusted R Square

Change Statistics

R Square

Change F Change df1 df2

Sig. F

Change

1 .981a .963 .963 .963 9085.192 1 348 .000

A discussion of model summary was previously provided with Table 4.1. Table 4.2

herein generated from SPSS is a model summary which describes the overall model and it

tells us whether the LWD is successful in predicting FWD. The Table also provides, R =

0.98 and the R2 = 0.96. This tells us that the layer modulus of LWD account for 96% of the

variation in the layer modulus of the FWD. Also, the model causes R2 to change from 0 to

0.96, and this change in the amount of variance explained, gives rise to an F-ratio of 9085.2,

which is considered statistically significant across all test sites with a probability level of

P = 0.00 < 0.001.

Similar and strong relationship (R2 = 0.96) provided in Figure 4.8 indicates fitting

a linear trendline to all materials. This means the backcalculated layer modulus of LWD,

ELWD (ksi), predicts the FWD backcalculated layer modulus, EFWD (ksi). The results are

presented below:

109

Figure 4.8: Regression Analysis Fitting Linear Trendline to Data Points

The results presented in Figure 4.8 demonstrated the dependent variable, FWD

layer modulus, was highly predicted by the independent variable, LWD layer modulus.

Results of regression analysis yielded linear model shown in Equation 4.9.

E 0.9253 E 4.8167Equation 4.9

y = 0.9253x - 4.8167R² = 0.9631

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Lay

er M

odul

us, F

WD

(ksi

)

Layer Modulus, LWD (ksi)

FWD versus LWD, Layer Moduli PCC

White Topping

HMA

Mechanical FDR

Fiber Cement

Cement FDR

70 30 asphalt/Cement

Concrete Steel

Full Depth Grindings

Partial DepthGrindingsLime FDR

Permazine FDR

Fabric ReinforcedStoneFly Ash FDR

Brick & 411

Surge & 411

Motorpave

Asphalt FDR

Geogrid

Aggregate Overly

Linear (Best FitModel)

110

It is important to note that the suggested model, Equation 4.9 is compatible with

the models proposed by Steinert et al. (2005), Nazzal (2003), and Fleming et al. (2000).

Their proposed model equations are indicated as follows:

E 0.904 ∗ E 0.005Equation 4.10

(Steinert et al., 2005), equation with R2 = 0.94

E 0.97 E Equation 4.11

(Nazzal, 2003), equation with R2 = 0.94

E 1.03 E Equation 4.12

(Fleming et al., 2000), equation with R2 = 0.97.

The above three equations along with the one suggested by the author were

combined to a single plot shown in Figure 4.9 below:

111

Figure 4.9: EFWD vs. ELWD, Comparison to Steinert et al. (2005), Nazzal (2003), and Fleming et al. (2000)

4.5.1 Comparison of Subgrade Moduli

Subgrade modulus of the FWD measurement was compared with the subgrade

modulus of the LWD. To better understand the differences, a floating column plots was

prepared similarly for each method as indicated in Figure 4.10 through Figure 4.12. These

plots were developed based on the volume of the collected and analyzed data in this study.

Consequently a columns chart was produced to compare the analyses results.

y = 0.9253x - 4.8167R² = 0.9631

0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Lay

er M

odul

us, F

WD

(ks

i)

Layer Modulus, LWD (ksi)

Linear(Suggested bythe author )

Linear(Steinert etal., 2005)

Linear (Nazzal,2003)

Linear(Fleming etal., 2000)

112

Figure 4.10: FWD Measured Modulus of the Subgrade. Values Indicated are Minimum;

Mean; and Maximum Respectively. (1 ksi = 6.89 MPa)

Figure 4.10 presents the subgrade modulus of the FWD measurements. Values

indicated at the bottom of the column is the minimum resilient modulus, the top of the

column is the maximum resilient modulus, and the numbers shown in each column bar

near the point are the mean values.

6.18 8.97 4.0311.58

21.97

23.7024.97

41.94

66.15

82.11

12.88 16.99 20.1627.81

35.89

0

10

20

30

40

50

60

70

80

90

100

110

Defiance Augaize +Mercer

Champaign +Madison

Muskingum Harrison +Carroll

Mod

ulus

of

the

Sub

grad

e (k

si)

Cluster

(FWD AASHTO 5.4.5)

113

Figure 4.11: LWD Measured Modulus of the Subgrade. Values Indicated are Minimum; Mean; and Maximum Respectively. (1 ksi = 6.89 MPa)

Figure 4.12: Rohde Method Measured Modulus of the Subgrade. Values Indicated are Minimum; Mean; and Maximum Respectively. (1 ksi = 6.89 MPa)

5.01 4.55 4.83 7.50 10.34

22.2518.45

24.50

31.2035.69

12.67 10.41 12.2515.74 17.47

0102030405060708090

100110


Champaign +Madison


Mod

ulus

of

the

Sub

grad

e (k

si)

Cluster

(LWD AASHTO 2.3.5)

7.27 6.00 4.75 10.004.26

23.01 24.7733.35

56.1169.87

13.26 14.00 12.77

23.99

39.23

0102030405060708090

100110


Champaign +Madison


Mod

ulus

of

the

Sub

grad

e (k

si)

Cluster

(FWD Rohde Method)

114

The results in Figure 4.10 through Figure 4.12 demonstrate that FWD subgrade

modulus in the first and second clusters have a weak subgrade modulus when compared to

other clusters. This is consistent with the results from LWD and Rohde methods. Floating

column plots reveal from the FWD results an average values of 12.88 ksi (88.8 MPa), and

16.99 ksi (117.14 MPa) respectively. Moreover, in the remaining three clusters,

Champaign/Madison, Muskingum, and Harrison/Carroll, the average subgrade modulus

values were ascending based on clusters variation subsequently in all three comparisons.

4.6 Comparison of Layer Coefficients

A regression analysis was also performed to better understand the variation in

FWD and LWD corresponding to layer coefficients. AASHTO 5.4.5-FWD was compared

with AASHTO 2.3.5-LWD. The results yielded a linear relationship indicated in

Figure 4.13.


Obtained from AASHTO 5.4.5-FWD & AASHTO 2.3.5-LWD Methods

y = 0.735x + 0.1104R² = 0.7797

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

AA

SH

TO

5.4

.5-F

WD

AASHTO 2.3.5-LWD

AASHTO 5.4.5-FWD vs. AASHTO 2.3.5-LWDLayer Coefficients

PCCWhitetoppingHMAMechanical FDRFiber CementCement FDR70 30 asphalt/CementConcrete SteelFull Depth GrindingsPartial Depth GrindingsLime FDRPermazine FDRFabric Reinforced StoneFly Ash FDRBrick & 411Surge & 411MotorpaveAsphalt FDRGeogridAggregate OverlyLinear (Best Fit Model)

115

The model shown in Figure 4.13 reveal that the AASHTO 5.4.5-FWD has a high

correlation (R2 = 0.78) with AASHTO 2.3.5 across all sites. The linear regression equation

was reported as follows:

a 0.735 a 0.1104Equation 4.12

Also, layer coefficients obtained from AASHTO 2.3.5-LWD was correlated with

the layer coefficients of the Rohde method. The regression analysis yielded a linear model.

To better explain this, a separate plot was created for all sites corresponding to material

properties. The plot is shown in Figure 4.14 below:

Figure 4.14: Regression Analysis Fitting Linear Trendline to All Layer Coefficients Obtained from AASHTO 2.3.5-LWD and Rohde Method

y = 1.0209x + 0.0171R² = 0.8167

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

Roh

de M

etho

d

AASHTO 2.3.5-LWD

AASHTO 2.3.5-LWD versus Rohde MethodLayer Coefficients

PCCWhite ToppingMechanical FDRFiber CementCement FDR70 30 asphalt/CementConcrete SteelFull Depth GrindingsPartial Depth GrindingsLime FDRPermazine FDRFabric Reinforced StoneFly Ash FDRBrick & 411Surge & 411MotorpaveAsphalt FDRGeogridAggregate OverlyLinear (Best Fit Model)

116

As presented in Figure 4.14, a proposed linear model has high correlation (R2 =

0.82). This reveals that the Rohde method tends to improve/confirm correlation of FWD

versus LWD. For the convenience of the reader, both models were plotted in a single plot.

This is presented in Figure 4.15 below:

Figure 4.15: FWD vs LWD Layer Coefficient Models, Comparison to Rohde Method

The results in Figure 4.15 demonstrate that the FWD layer coefficients are highly

correlated with LWD layer coefficients. It is important to understand that both linear

trendlines indicate that the relationship is compatible. This was confirmed by the model

developed by Rohde method.

y = 0.8246x + 0.058R² = 0.7797

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80

AA

SH

TO

5.4

.5-F

WD

AASHTO 2.3.5-LWD

Linear (FWDversus LWD)

Linear (RohdeMehod)

117

4.7 Comparison of Effective Structural Numbers

Effective structural numbers were determined based on AASHTO 1993, section

5.4.5 design guide for the pavement structures, and the Rohde method of determination for

the pavement structural number from FWD measurements. A separate single column chart

was developed to compare the effective structural numbers. The chart for the first cluster

(Defiance County), is shown in Figure 4.16 while the remaining column charts are

available in appendix F of this study.

Figure 4.16: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Defiance County

The results in Figure 4.16 demonstrate that the AASHTO 5.4.5 effective structural

numbers are slightly greater than, or somewhat equal to the Rohde effective structural

0

2

4

6

8

10

12

14

16

18

Eff

ecti

ve S

truc

tura

l Num

ber

Defiance County Roads

AASHTO 5.4.5 equations versus Rohde method

AASHTO 5.4.5Equations

ROHDEMethod

118

numbers for almost the entire test sites in the first cluster. However, for a few test sites, the

Blosser-C72-07, Blosser-C-15, and Flory-C68-08 the result was opposite. In order to

understand a better relationship between these two methods, a regression analysis was

performed which yielded a linear model shown in Figure 4.17.

Figure 4.17: Regression Model of Effective Structural Numbers Obtained from, the AASHTO Equations and the Rohde Method

The model and fitting linear trendline to the data points presented in Figure 4.17

reveal a very high correlation (R2 = 0.95) across all test sites. On the other hand, the

dependent variable, AASHTO equations is highly predicted by the independent variable,

the Rohde method. The regression equation for the effective structural numbers was

suggested to be:

y = 1.2121x + 0.076R² = 0.9549

0

2

4

6

8

10

12

14

16

18

0 2 4 6 8 10 12 14 16

AA

SH

TO

Equ

atio

ns

Rohde Method

Effective Structural Number AASHTO Equations vs. Rohde Method

119

SN 1.2121 SN 0.076Equation 4.13

Table 4.3 provides a summary of aforesaid regression equations developed by the

author to relate FWD and LWD parameters across all test sites. It is important to mention

that fitting trendline varied between linear and power based on high regression correlation

value of R2, to see which one generates the best fit to the model.

120

Table 4.3: Summary of Regression Analysis of FWD versus LWD Generated from Developed Models

Parameter Index Regression Equation Best Fitting Trendline

(R2)

Deflections at Radial Offset Distance of 0,

300, and 600mm from the Center of the Loading

Plate.

Do DFWD = 1.4607(dLWD)^0.8831 Power 0.85

D1 DFWD = 1.6881(dLWD)^0.9049 Power 0.78

D2 DFWD = 0.9284(dLWD) + 2.6519 Linear 0.72

Area Under Pavement

Profile (AUPP). AUPP

(AUPP)FWD 4-Sensors = 2.2664*[(AUPP)LWD 3-Sensors]0.8789

Power 0.83

Backcalculated Layer Moduli

E EFWD = 0.9253(ELWD) + 4.8167 Linear 0.96

Layer Coefficients

a aFWD = 0.735(aLWD) + 0.1104 Linear 0.78

Effective Structural Numbers

SN(eff) SN(eff)FWD = 1.2121(SN(eff)LWD) +

0.076 Linear 0.95

121

CHAPTER 5 CONCLUSION AND RECOMMENDATIONS

5.1 Summary

The principal goals in this study were to determine the structural adequacy of the

low-volume road using nondestructive test (NDT) technology, and to investigate the

potential use the Light Weight Deflectometer (LWD). Regression analysis was conducted

using the SPSS statistical software between the FWD and Prima 100 LWD to evaluate

whether LWD could be employed to measure the stiffness/strength parameters of the

existing local pavement materials and embankment.

The data was used to correlate: sensor deflections, backcalculated layer moduli,

layer coefficients, and the effective structural numbers. Modulus 6.0 and Evercalc 5.0 were

chosen to perform backcalculation analyses on pavement layers. The results of the

relationship between FWD and LWD was corroborated by the Rohde method for the layer

coefficients and subgrade modulus across all test sites. Also, In the course of this study, a

modified method of calculating Area under Pavement Profile (AUPP) was devised.

5.2 Conclusion

The results presented in this thesis demonstrate a good correlation exists between

FWD and LWD. These findings correspond with but not limited to AASHTO 1993 Guide,

statistical analysis outputs, and the Rohde method. Statistical analysis demonstrates and

suggests that the LWD could be reliably used to evaluate low-volume roads systems. The

correlation coefficients (R) and coefficient of determination (R2) values vary from (0.85 to

0.98) and (0.72 to 0.96), respectively for all correlated parameters.

122

The results highlight several significant findings. First, in the comparison between

FWD and LWD sensor deflections at the center of loading plate, and sensor deflections at

r = 300mm, yield a nonlinear model (power function) with quite a high relationship (R2

=0.85 and R2 = 0.78) respectively. This appears to be consistent for all sites. On the

contrary, the relationship between sensor deflections at radial offset distance, r = 600mm,

yield a linear regression model with a moderate relationship (R2 = 0.72). Also, it was found

that FWD and LWD sensor measurements at 0, 300, and 600mm are influenced by the

material behavior. This means that on soft soil surfaces (typically aggregate overlay in this

study), the FWD and LWD relationships result in a much lower R2 or deflections identified

to be outliers.

Also, the results for the Area under Pavement Profile (AUPP) yielded a nonlinear

model with moderate correlation (R2 = 0.83) for the modified AUPP. This modification at

radial offset distances 0, 300, and 600mm from the load center using LWD measurements,

now appears to be a new valid parameter for overlay design, and it can be used to predict

tensile strain on the bottom of an asphalt layer.

The subgrade modulus results reveal, soil stiffness/strength consistently increase

by cluster variation from one, two, and so forth. The subgrade modulus values in the first

cluster, using FWD, LWD, and Rohde method, was found to be 12.88 ksi (88.8 MPa),

12.67 ksi (87.36 MPa), and 13.26 ksi (91.42 MPa) respectively. In the second cluster,

Auglaize/Mercer, the LWD obtained subgrade modulus seems to be the weakest subgrade

with an average value of 10.41 ksi (71.77 MPa). However, this cluster had approximately

the same subgrade modulus with average values of 16.99 ksi (117.14 MPa) and 14.00 ksi

123

(96.52 MPa) from the FWD and Rohde methods respectively. Moreover, for the remaining

three clusters, Champaign/Madison, Muskingum, and Harrison/Carroll, average modulus

of subgrade values were consistently ascending, based on clusters variation across all

comparison parameters.

In the comparison of effective structural numbers, the AASHTO method results

were slightly greater than the Rohde method, or somewhat equal to the Rohde effective

structural numbers in all sites. This consistency was compatible with and supported by the

Rohde procedure, since he consider the contribution of the subgrade to SN.

Finally, in the correlation of the layer coefficient between AASHTO 5.4.5-FWD

versus AASHTO 2.3.5-LWD; the AASHTO 5.4.5-FWD was closely predicted by

AASHTO 2.3.5-LWD. AASHTO 2.3.5-LWD could account for 78.0% of the variation in

AASHTO 5.4.5-FWD. Although the correlation coefficient was high enough, R = 0.88, a

moderate fitting linear trendline of the model was established between the AASHTO 5.4.5-

FWD and AASHTO 2.3.5-LWD. Also, in the correlation between Rohde and AASHTO

2.3.5-LWD, the Rohde method was predicted very well by the AASHTO 2.3.5-LWD. This

method accounts for 81.67% of variation in the Rohde procedure and correlation

coefficient was found to be R = 0.90; so, a fitting linear trendline to the model was

established between the Rohde and AASHTO 2.3.5-LWD. Finally, statistical analysis

proved that all the correlation models were highly correlated with each other and were

considered statistically significant with a probability level of P = 0.00 < 0.001.

The aforementioned observations, as major points with regard to the acceptability

of the Prima 100 LWD, as a nondestructive device for low traffic volume follows:

124

1. The Prima 100 LWD device, with a 300mm diameter load bearing plate, is usable

for characterizing pavement layer moduli in the local pavement system.

Specifically, because the benchmark test, FWD, layer moduli are highly correlated

with the LWD layer moduli, and the FWD is economically prohibitive device for

local agencies. The results of this study confirm the hypothesis the LWD is

effective and adequate to evaluate in-situ deformation parameters during local

pavement investigations, and has the advantage of portability, cost effectiveness,

and ease of use.

2. The results presented in this study demonstrate that sensor deflections at the center

of loading plate, and the sensor deflections at r = 300mm have high relationship

(R2 =0.85 and R2 = 0.78) between FWD and LWD respectively. However, the

correlation of sensor deflections at radial offset distance, r = 600mm has a

moderate relationship (R2 = 0.72).

3. The author modification of the Area Under Pavement Profile is valid for the Prima

100 LWD measurement in the evaluation of pavement responses at a radial offset

distance of 0, 12, and 24 inches (0, 300, and 600mm) from the center of the loading

plate. This modification appears to be a new valid parameter for the overlay

design, and it can be used to predict tensile strain on the bottom of an asphalt layer.

4. The Prima 100 LWD, is highly affected by inadequate/inaccurate seating of the

bearing plate (300mm in this study) on the pavement surfaces. Moreover, radial

geophone configuration of 300 and 600mm records critical deflections to the

125

backcalculation process, and also produced the most accurate layer moduli

backcalculation results.

5. The FWD and the Prima 100 LWD measurements are less repeatable on a rough

or soft surfaces compared to a stiff or hard surfaces. Measured deflections on rough

or soft surfaces (typically aggregate overlay in this study) from multiple sensors

across all test sites resulted in a much lower R2, or somewhat identified to be

outliers in the data.

6. Based on observations made during testing, adjustment/measurements of radial

sensors, the PDA device Bluetooth connection, and verticality of the guide rod are

other factors affecting the Prima 100 LWD results. Also, test operators have to be

trained enough to identify and avoid incorrect device reading.

7. As shown in the column chart, Figure 4.7, the Evercalc 5.0 software consistency

for the backcalculated modulus from LWD measurements is slightly higher

compared to Modulus 6.0 software consistency of the FWD measurements.

5.3 Recommendations

This study investigated the feasibility of using Prima 100 LWD to evaluate

pavement performances such as: layer moduli, effective structural numbers, and layer

coefficients for the low volume roads. Based on the objectives set for this study, the

suggested recommendations are drawn as follows:

1. Statistical analysis demonstrated that all regression models are highly correlated

with each other and were agreed with those studied previously. Thus, derived

126

equations are recommended to use with confidence for structural analyses of the

pavement systems in the local roads with the LWD.

2. Follow up studies should investigate the use of the LWD on rough surfaces and

soft soil to reevaluate variability of measurements.

3. Finite element analysis is recommended for further correlation study between

these nondestructive devices. Such analyses and should consider the rate of

stiffness evaluation at various stress and strain levels.

4. It is recommended that laboratory determined resilient moduli should be correlated

with the moduli obtained from FWD and LWD. This may develop or modify

precise shift factors between field backcalculated and laboratory estimated layer

resilient moduli and also revalidate the presented correlation in this study.

5. The author does not prescribe any specific backcalculation software for LWD

deflection data analysis. However, particular caution against using any software is

needed in the conventional sense in addition of knowing that, incorrect input

parameters result in incorrect outcomes.

6. It is highly recommended the use of the modified AUPP should be investigated.

The correlations should be made between additional deflection basin parameters

and pavement responses.

7. Lastly, further research should include conducting the correlation between FWD

and LWD measurements with different plate size and drop height while

maintaining the same radial offset distance. This results to better understand the

effects of influence depth on the underlying layer systems.

127

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132

APPENDIX A: PAVEMENT LAYER THICKNESSES AND MATERIAL

PROPERTIES BY COUNTY.

Table A1: Layer Thicknesses and Material Properties, Defiance County

Road Name

Core ID

Layer 1 Layer 2 Layer 3

Thickness (in)

Material Thickness

(in) Material

Thickness (in)

Material

Rosedale C117 (A)

1 2.0

HMA

5.5 Fiber

Cement *

Natural Subgrade

2 1.5 6.0

3 1.2 6.6

Rosedale C117 (B)

1 2.0

HMA

12.0 FDR

Cement *

Natural Subgrade

2 2.5 12.0

3 2.5 12.0

Harding C195

1 11.0

HMA

4.0 FDR

Asphalt *

Natural Subgrade

2 11.0 4.0

3 9.0 6.0

Rosedale C117

1 2.5

HMA

14.0 FDR

Cement *

Natural Subgrade

2 3.0 14.0

3 3.8 14.0

Willams Center Cecil C123

1 2.7

HMA

10.0 FDR

Cement *

Natural Subgrade

2 2.8 10.0

3 2.8 10.0

Hammon T187

1 0.7 Chip Seal

8.0 FDR

Cement *

Natural Subgrade

2 0.7 8.0

3 0.7 8.0

Blosser C72

1 0.5 Chip Seal

10.0 FDR

Cement *

Natural Subgrade

2 0.6 10.0

3 0.5 10.0

Blosser C72

1 0.2 Chip Seal

3.3 Fiber

Cement *

Natural Subgrade

2 0.2 3.1

3 0.1 2.2

Flory C68

1 1.0

HMA

6.0 Fiber

Cement *

Natural Subgrade

2 1.0 6.1

3 1.0 6.0

133

Table A1: Continued

Banner School-

C70

1 3.0

HMA

5.5 Fiber

Cement

10.0 Aggregate

Base 2 2.5 5.5 10.0

3 3.0 5.5 10.0

Elliot C53

1 4.3

HMA

7.0 Fiber

Cement

6.2 Aggregate

Base 2 3.5 7.0 5.3

3 4.5 6.0 4.0

Banner School Rd

C70

1 5.0

HMA

6.2 Fiber

Cement *

Natural Subgrade

2 4.3 7.0

3 4.5 7.5

The Bend C134

1 3.3

HMA

8.0 Fiber

Cement

10.0 Aggregate

Base 2 3.3 8.0 10.0

3 3.3 8.0 10.0

Krouse-C146-13

1 5.0

HMA

11.0 Fiber

Cement

12.0 Aggregate

Base 2 4.5 13.0 12.0

3 6.0 12.5 12.0

Mansfield C6

1 2.8

HMA

5.5 Fiber

Cement

7.0 Aggregate

Base 2 2.5 5.5 5.0

3 3.0 5.5 4.0

Mansfield C6

1 6.0

HMA

3.5 Full Depth Grindings

* Natural

Subgrade 2 8.0 3.0

3 6.0 3.5

Blosser C72

1 0.5

Chip Seal

10.0

FDR Cement

* Natural

Subgrade

2 0.5 10.0

3 0.5 10.0

4 0.5 10.0

5 0.5 10.0

6 0.5 10.0

7 0.5 10.0

8 0.5 10.0

9 0.6 10.0

Fountain

Street C39

1 0.1

Chip Seal

6.0

Fiber Cement

* Natural

Subgrade

2 0.2 5.0

3 0.2 5.5

4 0.1 4.6

5 0.2 4.8

6 0.1 4.7

7 0.2 5.2

134

Table A1: Continued

Williams Center Cecil

C123 ( BX1, BX2, BX3)

C1 1.5

Chip Seal

6.0

Fabric Reinforced

Stone *

Natural Subgrade

BX2 1.3 9.0

BX3 1.5 8.0

C2 1.5 5.0

F2 1.5 8.0

C3 1.5 11.0

Elliot C53

1 9.0

HMA

6.0

Fabric Reinforced

Stone *

Natural Subgrade

2 9.0 7.0

3 9.0 8.0

4 10.0 12.0

5 9.0 13.0

6 9.0 8.0

7 9.0 10.0

8 10.0 9.5

9 9.0 11.0

Christy C164

1 6.3

White topping

6.5

HMA

5.0

Aggregate Base

2 5.0 7.5 4.0

3 5.5 4.5 4.5

4 5.0 8.5 3.5

5 4.5 8.5 3.0

6 5.0 7.5 3.5

7 6.0 5.5 4.0

8 5.5 6.5 3.5

9 5.5 6.0 4.5

*Placed Layer on the Natural Subgrade, No Thickness Was Measured.

135

Table A2: Layer Thicknesses and Material Properties, Harrison County

Road Name Core ID


Thickness (in)

MaterialThickness

(in) Material

Thickness (in)

Material

Birmingham- (C10)

1 3.5

HMA

12.0 FDR

Asphalt *

Natural Subgrade

2 3.5 11.5

3 3.0 10.0

Unionvale-(C12)

1 3.0

HMA

15.0 FDR

Cement *

Natural Subgrade

2 3.0 13.5

3 3.5 15.0

Bakers Ridge (C51)

1 3.5

HMA

14.0 FDR

Cement *

Natural Subgrade

2 3.5 13.5

3 3.0 13.5

Plum Run (C8)

1 3.5

HMA

11.0

FDR Permazine

* Natural

Subgrade

2 3.5 12.0

3 3.3 13.0

4 3.0 12.0

5 3.0 11.5

6 3.0 10.5

7 3.5 10.5

8 4.0 10.0

9 4.0 11.0

*Placed layer on natural subgrade, No thickness was measured.

136

Table A3: Layer Thicknesses and Material Properties, Carroll County

Road Name

Core ID


Thickness (in)

Material Thickness

(in) Material

Thickness (in)

Material

Pledge (T370)

1 8.0 Aggregate Overlay

* Natural

Subgrade*

Natural Subgrade

2 8.0

3 8.0

Meter (T269-

02)

1 6.0 Aggregate Overlay

* Natural

Subgrade*

Natural Subgrade

2 6.0

3 6.0

Meter- (T269-

03)

1 3.5

HMA

13.0 Cement

FDR *

Natural Subgrade

2 3.5 13.0

3 3.5 13.0

Canyon (C54)

1 13.5

HMA

9.0 Cement

FDR *

Natural Subgrade

2 14.0 8.0

3 13.5 9.0

Apollo (C12)

1 3.3

HMA

14.0 Cement

FDR *

Natural Subgrade

2 3.3 14.0

3 3.0 14.0

Chase (C66)

1 3.0

HMA

12.0 Cement

FDR *

Natural Subgrade

2 3.0 12.0

3 4.0 12.0


137

Table A4: Layer Thicknesses and Material Properties, Auglaize County

Road Name Core ID


Thickness (in)

Material Thicknes

s (in) Material

Thickness (in)

Material

Kossuth Loop (C216A)

1 9.0 Full Depth

Grindings

* Natural

Subgrade *

Natural Subgrad

e

2 9.0

3 9.0

East Shelby (C71)

1 2.0

HMA

9.0

Full Depth

Grindings

* Natural Subgrad

e

2 2.0 9.0

3 1.8 9.0

4 1.8 9.0

5 2.0 9.0

6 2.0 9.0

7 2.5 9.0

8 2.0 9.0

9 2.3 9.0

Blank Pike (C160)

1 6.0

HMA

9.0 Partial Depth

Grindings

* Natural Subgrad

e

2 6.5 9.0

3 6.0 9.0

Southland (C3)

1 6.8

HMA

5.0 Partial Depth

Grindings

* Natural Subgrad

e

2 6.5 5.0

3 6.5 5.0

Minster Fort Recovery(C30

)

1 9.5

HMA

5.0 Full Depth

Grindings

* Natural Subgrad

e

2 10.0 3.0

3 9.5 7.0

Fairgrounds

1 9.0

Full Depth

Grindings

* Natural

Subgrade *

Natural Subgrad

e

2 9.0

3 9.0

4 9.0

5 9.0

6 9.0

7 9.0

8 9.0

9 9.0


138

Table A5: Layer Thicknesses and Material Properties, Mercer County

Road Name

Core ID


Thickness (in) Material Thickness (in) Material Material

Dutton (C230A)

1 6.0

HMA

6.0 70/30

asphalt/cement Natural

Subgrade 2 6.5 6.0

3 6.0 6.0

Neptune Mendon

Rd. (C161C)

1 6.0

HMA

14.0 70/30


Subgrade 2 6.0 14.0

3 6.0 14.0

Harris (C175B)

1 9.8

HMA

5.0 70/30


Subgrade 2 7.5 5.0

3 8.0 5.5

139

Table A6: Layer Thicknesses and Material Properties, Champaign County

Road Name

Core ID


Thickness (in)

MaterialThickness

(in) Material

Thickness (in)

Material

Pisgah (C236)

1 7.5

HMA

10.0 Mechanical

FDR

6.5 Aggregate

Base 2 7.5 10.0 6.0

3 8.0 10.0 6.0

Heck Hill (C26)

1 2.8

HMA

10.0 Mechanical

FDR

6.0 Aggregate

Base 2 2.8 10.0 6.5

3 2.8 10.0 6.0

Nine Mile (C37)

1 1.2 Chip Seal

8.5 FDR

Cement *

Natural Subgrade

8.5 8.5

Kite (C22)

1 3.0

HMA

10.0 Mechanical

FDR

6.0 Aggregate

Base 2 3.0 10.0 6.5

3 3.0 10.0 6.0

Sullivan Rd (C45)

1 1.0 Chip Seal

12.0 Mechanical

FDR

6.0 Aggregate

Base 2 1.0 12.0 6.0

3 0.8 12.0 6.0

Lippincott (C115)

1 1.3 Chip Seal

10.0 Mechanical

FDR *

Natural Subgrade

2 1.3 10.0

3 1.4 10.0

Old Troy Pike

(C193)

1 0.5 Chip Seal

12.0 Mechanical

FDR *

Natural Subgrade

2 0.5 12.0

3 0.5 12.0

W. Dallas Rd.

(C184)

1 6.2

HMA

6.0 Cement

FDR *

Natural Subgrade

2 6.5 6.0

3 4.5 6.0

Nine Mile (C37)

1 1.2 Chip Seal

13.0 Mechanical FDR

* Natural

Subgrade 13.0

Old Troy Pike

(C193)

1 0.5 Chip Seal

13.0 Mechanical

FDR *

Natural Subgrade

2 0.8 13.0

3 0.7 13.0


140

Table A7: Layer Thicknesses and Material Properties, Madison County

Road Name

Core ID


Thickness (in)

MaterialThickness

(in) Material

Thickness (in)

Material

MCEO

1 7.0

HMA

8.0

Geogrid * Natural

Subgrade

2 7.0 8.0

3 6.8 8.0

4 6.4 8.0

5 7.5 8.0

6 7.2 8.0

7 7.5 8.0

8 7.0 8.0

9 6.7 8.0

Charleston-Chillicothe

(C15B)

1 1.5 Chip Seal

10.0 FDR

Cement *

Natural Subgrade

2 1.5 10.0

3 1.5 10.0

Davis Rd (C95)

1 1.0 Chip Seal

10.0 FDR

Cement *

Natural Subgrade

2 1.0 10.0

3 1.0 10.0

Taylor Blair-C14-

S4

1 0.5 Chip Seal

4.0

HMA

10.0 Aggregate

Base 2 0.5 3.8 10.0

3 0.5 3.7 10.0

Taylor Blair-C14-

N5

1 0.5 Chip Seal

4.0 FDR

Cement

10.0 Aggregate

Base 2 0.5 4.0 10.0

3 0.5 4.0 10.0

*Placed Layer on the Natural Subgrade, No Thickness Was Measured

141

Table A8: Layer Thicknesses and Material Properties, Muskingum County

Road Name

Core ID

Layer 1 Layer 2 Layer 3 Layer 4

Thickness (in)

Material

Thickness (in)

Material

Thickness (in)

Material

Thickness (in)

Material

Airport (797)

1 1.5

HMA

11.0 Concrete Steel

10.0 Aggregate

Base *

Natural Subgra

de 2 1.5 9.0 13.0

3 1.5 11.0 11.0

Arch Hill

1 1.3 Motorp

ave

8.0 Concrete Steel

* Natural Subgra

de * - 2 1.5 10.0

3 1.0 9.0

Dietz Lane (449)

1 7.0

HMA

4.5 Brick &

411 *

Natural Subgra

de * - 2 7.0 6.0

3 7.0 5.5

Elis Dam(C

49)

1 0.5

Chip Seal

11.0

FDR Fly Ash

* Natural Subgra

de *

2 0.5 12.0

3 0.5 12.0

4 0.7 12.0

5 0.6 13.0

6 0.8 12.0

7 1.0 13.0

8 1.0 12.0

9 1.0 13.0

Friendly Hills (418)

1 1.0 Chip Seal

12.0 FDR Lime

8.0 Aggregate

Base *

Natural Subgra

de 2 1.0 12.0 8.0

3 1.0 12.0 7.0

Mt Perry (C30)

1 1.0

HMA

7.0 Concrete Steel

* Natural Subgra

de * - 2 1.0 7.0

3 1.0 7.0

Narrows (C76)

1 2.0

Chip Seal

3.0

Motorpave

8.0

Aggregate

Base *

Natural Subgra

de

2 2.0 3.0 7.8

3 2.5 4.0 6.0

4 2.5 4.5 8.0

5 1.5 5.0 10.0

6 2.0 5.5 8.0

7 2.0 5.5 6.5

8 2.0 6.0 8.0

142

Table A8: Continued

New Hope (C20)

1 2.0

HMA

15.0 Surge & 411

* Natural Subgra

de * - 2 2.0 16.0

3 2.0 16.0

Norfield (C64)

1 1.8

Motorpave

5.0

HMA

3.0

Chip Seal

5.0

Aggregate

Base

2 2.0 5.0 3.0 5.0

3 2.0 4.5 3.0 5.5

4 2.0 4.5 2.8 5.5

5 2.0 4.0 3.0 6.0

6 1.8 4.0 3.5 7.0

7 2.0 4.5 3.0 6.0

8 1.8 5.0 2.8 6.0

9 2.0 4.5 3.0 6.0

Powelson

(C49)

1 2.0 Chip Seal

3.0 Motorp

ave

9.0 FDR Lime

* Natural Subgra

de 2 2.0 3.5 11.0

3 2.3 3.0 10.0

Rural Dale (C31)

1 3.0

Chip Seal

8.0

FDR Asphalt

* Natural Subgra

de * -

2 2.8 8.0

3 3.3 8.0

4 2.8 10.0

5 3.0 10.0

6 2.5 9.0

7 3.0 10.0

8 3.0 9.0

Salt Creek (C44)

1 3.0

HMA

4.0 Brick &

411

13.0

Bricks * Natural Subgra

de 2 2.8 4.3 12.0

3 2.8 3.5 13.5

Vista View

1 9.5

PCC

9.5 Concrete Steel

* Natural Subgra

de * - 2 10.0 9.5

3 9.0 9.5

AirPark

1 9.0

PCC

8.0 Concrete Steel

* Natural Subgra

de * - 2 10.0 6.0

3 9.0 8.0

Southern

(C107)

1 1.0 Motorp

ave

12.5 Surge & 411

* Natural Subgra

de * - 2 1.0 12.5

3 1.0 11.5

*Placed Layer on the Natural Subgrade, No Thickness Was Measured

143

APPENDIX B: TYPICAL FWD AND LWD DEFLECTION BASINS

Figure B1: Deflection Basins for Three Loads, Cluster # 2, Section of Minster Recovery Road (Aug-C30-16), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate.

Figure B2: LWD Deflection Basins Same Loads, Meter Road (CAR-T269-2), Aggregate

Overlay Surface Layer, Carroll County, 11.8-in. (300-mm) Plate.

0

4

8

12

16

20

24

28

0 12 24 36 48 60F

WD

Sen

sor

Def

lect

ion

(mil

s)


6000 lbf

9000 lbf

12000 lbf

0

2

4

6

8

10

12

14

16

18

200 12 24

LW

D s

enso

r D

efle

ctio

n, m

ils


2701 lb

2589 lb

2521 lb

144

Figure B3: FWD Deflection Basins for Three Loads, Cluster # 2, Section of East Shelby Road (Aug-C71-8), HMA Surface layer, Auglaize County, 11.8-in. (300-mm) Plate.

Figure B4: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Blank Pike Road (Aug-C160-12), HMA Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate.

0

4

8

12

16

20

24

28

32

36

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

-2

2

6

10

14

18

22

26

30

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

145

Figure B5: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Kossuth Loop (Aug-C216A-3), Full depth Grindings layer, Auglaize County, and 11.8-in. (300-

mm) Plate.

Figure B6: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Fairground (Aug-FG-18), Full Depth Grindings Layer, Auglaize County, 11.8-in. (300-mm) Plate.

0102030405060708090

100110120

0 12 24 36 48 60

FW

D s

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

0

10

20

30

40

50

60

70

80

90

100

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

146

Figure B7: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Neptune

Mendon Road (MER-C161C-7), HMA Surface Layer, Mercer County, 11.8-in. (300-mm) Plate.

Figure B8: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Harris Road

(MER-C175B-8), HMA Surface Layer, Mercer County, 11.8-in. (300-mm) Plate.

02468

1012141618202224

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

0

2

4

6

8

10

12

14

16

18

20

22

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

147

Figure B9: FWD Deflection Basins for Three Loads, Cluster # 2, Section of Dutton Road

(MER-C230A-3), HMA Surface Layer, Mercer County, 11.8-in. (300-mm) Plate.

Figure B10: LWD Deflection Basins Same Loads, Cluster # 2, Kossuth Loop (Aug-

C216A-3), Full Depth Grindings Surface Layer, Auglaize County, 11.8-in. (300-mm) Plate

0

4

8

12

16

20

24

28

32

36

0 12 24 36 48 60

FW

D S

enso

r D

efle

ctio

n (m

ils)


6000 lbf

9000 lbf

12000 lbf

0

30

60

90

120

150

180

210

240

2700 12 24

LW

D S

enso

r D

efle

ctio

n, m

ils


2336 lb

2395 lb

2360 lb

148

APPENDIX C: AASHTO 5.4.5 PROCEDURE OUTPUTS USING FWD SENSOR DEFLECTIONS

Table C1: AASHTO 5.4.5 Equations Outputs Calculated from FWD Sensor Deflection Using 11.8-in. (300mm) Plate.

Road Name

Subgrade MR (ksi)

Effective EP (ksi)

Central Deflection

d0 (mil)

Total thickness

D (in)

Effective Structural Number

(Seff)

Road Name Subgrade MR (ksi)

Effective EP (ksi)

Central Deflection

d0 (mil)

Total thickness

D (in)

Effective Structural Number (SNeff)

Christy-C164

7.82 6378.23 4.53 16.10 13.10 East Shelb-

C71-08 5.82 254.27 23.12 11.00 3.05

Blosser-C72-07

3.86 1940.82 48.25 3.00 1.62 Mansfield-

C6-14C 3.61 394.24 26.82 10.00 3.27

Mt Perry-

C30-11 3.82 11600.01 10.53 8.00 8.04

Blank Pike-C160-12

8.24 127.10 20.52 15.20 3.38

Arch Hill-

C82-03 8.18 9697.57 5.67 10.30 9.62

Salt Creek-C44-19

25.30 40.67 23.07 19.60 3.01

Vista View

6.34 2174.57 6.09 19.00 10.87 Dietz Ln-C449-06

5.30 510.26 18.42 12.30 4.12

Air Park 4.83 20711.60 6.34 9.30 11.48 Apollo-C12-

05 9.49 396.00 9.77 17.20 5.64

Airport-C797-02

6.97 1987.64 4.64 23.20 13.10 Canyon-C54-04

7.25 1542.95 5.23 22.30 11.57

149

Table C1: Continued

Mansfield-C6-14

3.61 187.38 26.82 13.60 3.48 Chase-C66-

06 7.25 4473.03 5.15 15.30 11.14

Elliott-C53-10

4.09 523.47 16.03 15.90 5.58 Meter-T269-

03 41.83 231.69 6.10 16.50 4.50

Elliott-C53-18

6.72 494.94 10.28 18.60 6.53 Plum Run-

C8-06 8.01 49.09 36.73 14.70 2.30


3.99 537.66 13.75 18.30 6.66 Birmingham

-C10-02 8.12 71.76 25.14 14.50 2.68


3.85 3380.34 12.48 11.50 7.45 Unionvale-

C12-03 12.33 1844.37 4.70 17.70 9.36

Blosser-C72-15

4.13 543.48 24.16 10.50 3.61 Bakers

Ridge-C51-04

12.08 1720.92 4.36 17.00 9.15

Rosedale-C117 -

01A 3.58 504.42 32.90 7.60 2.69

Fountain-C39-16

2.63 2632.14 53.94 5.20 2.48

Rosedale-C117 -

01B 4.42 382.86 20.14 14.30 4.45

Flory- C68-08

2.04 1371.14 38.82 7.00 3.42

Rosedale-C117-03

4.73 275.99 16.66 17.10 4.98 Blosser-C72-06

2.89 21.70 83.79 10.50 1.30

WCC- C123 04

3.68 91.03 36.99 12.80 2.55 WCC-C123-

17 5.01 205.89 31.53 9.30 2.38

150

Table C1: Continued

The Bend-

C134-12 3.77 706.10 11.02 21.30 8.52

Hammon-T187-05

4.69 31.83 61.94 8.70 1.23

Krouse-C134-13

6.04 1549.67 4.64 29.30 15.30 Taylor

Blair-C14-S4

3.44 15.23 79.54 14.30 1.58

Harding-C195-02

4.10 519.44 16.14 15.00 5.39 Taylor

Blair-C14-N5

3.98 19.53 64.53 14.50 1.76

Kite-1-C22-14

11.06 29.53 32.03 19.20 2.67 Charleston -

C15B-02 2.92 69.42 51.04 11.50 2.06

Heck Hill-

C62-07 13.84 37.70 25.60 19.00 2.87 MCEO 13.02 123.46 16.08 15.00 4.63

Nine Miles-C37-12

1.40 282.02 52.91 9.70 2.86 Davis-C95-

03 8.68 777.46 12.84 11.00 4.43

Nine Miles-C37-20

1.33 239.05 45.37 14.20 3.83 Rural Dale-

C31-18 15.08 26.57 36.90 12.00 1.59

Sullivan-C45-15

6.41 23.00 44.21 18.90 2.42 Ellis Dam-

C49-08 7.82 35.47 43.38 13.00 1.86

Lippincott-C115-

17 8.78 38.18 35.12 11.30 1.71

Powelson-C49-16

7.50 32.13 38.95 15.30 2.17

Dallas-C184-19

6.12 120.69 32.47 11.70 2.39 Southland-

C3-15 4.39 1169.05 15.01 11.60 5.38

151

Table C1: Continued

Old Troy Pike-

C193-18 6.65 16.98 56.19 12.50 1.45

Minster Fort -C30 -16

5.38 237.83 18.57 14.70 4.07

Old Troy Pike-

C193-21 4.03 49.25 44.34 13.70 2.21

Dutton (C230A)

2.96 359.64 27.73 12.20 3.85

Pisgah-C236-03

8.13 58.15 20.97 23.80 4.15

Neptune M-C161C

5.31 170.32 17.34 20.00 4.99

Harris (C175B)

5.96 642.03 15.53 13.60 4.93

152

APPENDIX D: SUMMARY OF BACKCALCULATED LAYER MODULI FROM FWD AND LWD TESTING

Table D1: Summary of Averaged Backcalculated Layer Moduli Computed from FWD Sensor Deflections Using 11.8-in. (300mm) Plate, Modulus 6.0 Software.

Road Name Backcalculated Layer

Moduli (ksi) Road Name Backcalculated Layer Moduli (ksi)

E1 E2 E3 E1 E2 E3 E4

Christy-C164 2168 1158 21 Elliott-C53-10 1081 267 12 8

Blosser-C72-07 515a 3 Elliott-C53-18 675 60 9 *

Mt Perry-C30-11 2535 230 7 Banner School-C70-11 1148 833 6 *

Arch Hill -C82-03 578 121 16 Blosser-C72-15 488 128 6 *

Vista View Drive 2193 25 22 Rosedale-C117 -01A 1303 178 4 *

Air Park 2844 32 17 Rosedale-C117 - 01B 1166 82 9 *

WCC-C123-04 523 24 7 Airport-C797-02 1670 86 13 20

Lippincott-C115-17 90a 13 Mansfield-C6-14 & 14C 815 801 61 7

Dallas-C184-19 372 19 6 The Bend-C134-12 1179 861 23 7

Old Troy Pike-C193-18 48 24 4 Krouse-C134-13 1298 1305 257 17

Old Troy Pike-C193-21 54a 1 Harding-C195-02 950 74 9 *

Southland-C3-15 982 103 7 Kite-1-C22-14 1072 34 11 6

Minster Fort Recovery -C30 -16 502 34 5 Pisgah-C236-03 165 31 17 5

Nine Miles-C37-12 530a 4 Heck Hill-C62-07 1078 15 27 8

Dutton-C230A-3 515 100 10 Neptune Mendon-C161C-7 967 186 19 *

Nine Miles-C37-20 131a 3 East Shelby-C71-08 777 99 8 *

Sullivan-C45-15 41a 14 Blank Pike-C160-12 456 35 7 *

153

Table D1: Continued

Harris-C175B-8 704 140 18 Salt Creek-C44-19 368 25 11 7

Fountain Street-C39-16 539a 6 Dietz Ln-C449-06 414 213 7 *

Flory- C68-08 926a 3 MCEO Office Drive 1187 40 13 *

Blosser-C72-06 483a 6 Apollo-C12-05 982 121 17 *

WCC-123-17 159a 12 Canyon-C54-04 884 179 12 3

Hammon-T187-05 591a 7 Taylor Blair-C14-N5 399a 10 3

Taylor Blair-C14-S4 626a 10 Chase-C66-06 946 887 20 *

Charleston Chillicothe-C15B-02 500a 5 Meter-T269-03 688 109 89 *

Davis-C95-03 656a 12 Plum Run-C8-06 751 15 11 *

Rural Dale-C31-18 59a 3 Unionvale-C12-03 675 643 32 *

Ellis Dam-C49-08 44a 4 Powelson-C49-16 72a 450 4

Fairground (Center) 26 5 ** Bakers Ridge -C51-04) 659 583 39 *

Fairground (East) 22 10 ** Narrows-C76-12 124a 43 4

Fairground (West) 23 7 ** Friendly Hill-C418 -10 987a 42 8

Pledge-T370-01 34 11 ** Norfield-C64-14 105 82a 6

Meter-T269-02 67 26 ** Southern-C107-20 152 40 8 *

New Hope-C20 20a 11 Birmingham-C10-02 585 27 8 *

Kossuth Loop-C216A-03 23 7 ** Rosedale-C117-03 1145 57 12 * * Three Layer System; **Two Layer System; a Top Layer Was Combined With the Bottom Layer

154

Similarly, an averaged of backcalculated Layer moduli from LWD sensor deflection using Evercalc 5.0 are shown in table D2

as following:

Table D2: Summary of Averaged Backcalculated Layer Moduli Computed from LWD Sensor Deflections Using 11.8-in. (300mm) Plate, Evercalc 5.0 Software.

Road Name

Backcalculated Layer Moduli (ksi) Road Name

Backcalculated Layer Moduli (ksi)

E1 E2 E3 E1 E2 E3 E4

Christy-C164 2244 1262 19 Elliott-C53-10 1006 319 24 14

Blosser-C72-07 671a 6 Elliott-C53-18 682 42 20 *

Mt Perry-C30-11 2320 318 8 Banner School-C70-11 1080 703 14 *

Arch Hill -C82-03 605 150 20 Blosser-C72-15 549 239 8 *

Vista View Drive 2354 48 20 Rosedale-C117 -01A 1127 242 5 *

Air Park 2593 46 21 Rosedale-C117 - 01B 1039 129 12 *

WCC-C123-04 541 58 12 Airport-C797-02 1497 112 26 12

Lippincott-C115-17 77a 13 Mansfield-C6-14 & 14C 994 757 47 *

Dallas-C184-19 464 37 5 The Bend-C134-12 1063 796 28 14

Old Troy Pike-C193-18 89a 6 Krouse-C134-13 1139 1039 137 20

Old Troy Pike-C193-21 52a 7 Harding-C195-02 1084 69 13 *

Southland-C3-15 984 97 11 Kite-1-C22-14 891 32 18 11

Minster Fort Recovery -C30 -16 663 36 8 Pisgah-C236-03 205 25 19 8

Nine Miles-C37-12 484a 7 Heck Hill-C62-07 951 21 15 5

155

Table D2: Continued

Dutton-C230A-3 637 138 10 Neptune Mendon-C161C-7 990 139 11 *

Nine Miles-C37-20 70a 7 East Shelby-C71-08 836 93 10 *

Sullivan-C45-15 79a 12 Blank Pike-C160-12 473 31 9 *

Harris-C175B-8 830 116 16 Salt Creek-C44-19 392 35 9 *

Fountain Street-C39-16 585a 6 Dietz Ln-C449-06 485 186 10 *

Flory- C68-08 906a 5 MCEO Office Drive 1150 37 8 *

Blosser-C72-06 535 11 Apollo-C12-05 984 137 22 *

WCC-123-17 166a 10 Canyon-C54-04 991 217 11 *

Hammon-T187-05 515a 8 Taylor Blair-C14-N5 329a 15 5

Taylor Blair-C14-S4 572a 8 Chase-C66-06 1008 847 16 *

Charleston Chillicothe-C15B-02 453a 6 Meter-T269-03 676 117 49 *

Davis-C95-03 587a 14 Plum Run-C8-06 696 19 10 *

Rural Dale-C31-18 121a 6 Unionvale-C12-03 715 693 21 *

Ellis Dam-C49-08 38a 6 Powelson-C49-16 86a 336 10

Fairground (Center) 39 11 ** Bakers Ridge -C51-04) 656 549 27 *

Fairground (East) 27 6 ** Narrows-C76-12 136a 50 15

Fairground (West) 41 9 ** Friendly Hill-C418 -10 828a 46 14

Pledge-T370-01 50 12 ** Norfield-C64-14 124 101a 14

Meter-T269-02 44 17 ** Southern-C107-20 168 58 8 *

New Hope-C20 81 19 7 Birmingham-C10-02 628 33 10 *

Kossuth Loop-C216A-03 24 5 ** Rosedale-C117-03 1231 300 11 * * Three Layer System; **Two Layer System; a Top Layer Was Combined With the Bottom Layer

156

APPENDIX E: FWD AND LWD SENSOR DEFLECTIONS

Table E1: Normalized/Extrapolated to 9000 Pounds Sensor Deflections (D0, D1, and D2) at Radial Offset Distance 0, 12, 24 inches from the Center of the Load.

Normalized FWD Sensor Deflections Extrapolated LWD Sensor Deflections

D0

(mils) D1

(mils) D2

(mils) D0

(mils) D1

(mils) D2

(mils) D0

(mils) D1

(mils) D2

(mils) D0

(mils) D1

(mils) D2

(mils)

10.74 9.07 6.84 3.77 3.41 3.21 9.60 7.49 5.74 3.50 3.19 2.40

14.97 11.90 8.54 4.71 3.61 3.32 11.29 8.51 6.30 6.10 2.69 2.49

16.71 13.70 9.34 4.28 3.67 3.30 13.23 9.55 6.99 3.60 2.46 2.25

19.75 14.78 8.48 3.83 3.60 3.02 17.42 10.32 1.97 4.43 3.83 3.44

16.61 12.25 8.24 4.32 4.24 4.11 15.05 9.37 8.98 5.62 6.28 7.01

16.12 12.19 8.00 3.94 3.70 3.33 15.50 9.96 3.26 3.37 3.19 3.01

19.56 14.07 9.48 3.25 3.00 2.68 49.79 23.16 12.79 5.50 4.54 3.73

20.57 15.14 9.94 6.88 5.48 4.38 55.88 23.50 12.67 6.96 5.20 4.45

17.81 13.25 8.96 3.84 3.56 2.88 34.07 20.16 13.01 7.28 5.46 4.48

25.01 16.88 9.91 3.98 3.90 3.47 68.19 24.97 9.45 6.14 4.82 3.84

22.69 15.75 9.84 13.97 11.05 8.08 58.97 23.81 11.14 11.04 7.73 5.82

27.87 19.62 11.28 17.44 13.48 9.41 66.36 28.42 13.20 13.32 9.08 6.71

24.89 16.78 10.42 13.70 10.61 7.88 22.08 11.07 7.33 11.53 7.26 5.66

25.13 17.13 10.33 64.97 31.43 12.57 23.79 11.36 7.28 55.56 16.82 8.25

28.10 18.39 11.28 56.94 28.84 12.49 24.94 12.05 7.82 54.16 15.39 8.02

27.63 9.18 5.09 68.80 32.30 12.84 26.06 8.39 4.94 54.25 15.75 7.86

7.74 5.49 3.95 29.29 18.91 10.66 4.53 2.46 2.04 22.06 11.48 6.35

10.91 7.40 4.96 26.77 16.87 9.64 4.35 2.35 1.88 21.74 10.72 6.21

4.90 4.03 3.23 25.16 14.82 8.23 4.13 3.35 2.88 22.99 10.74 6.01

4.68 3.97 3.42 78.57 36.18 9.60 4.86 3.24 2.76 49.47 18.33 6.57

4.92 4.15 3.52 36.90 16.79 6.01 6.10 3.39 2.89 24.53 10.05 4.16

5.33 3.74 3.11 29.66 15.59 7.16 5.27 3.25 2.69 19.58 10.45 5.23

4.84 3.94 3.56 41.24 23.39 11.08 6.16 3.07 2.54 33.93 14.59 7.48

4.42 3.51 3.09 37.49 21.00 9.10 4.99 2.63 2.34 26.78 10.33 4.45

28.55 16.52 9.61 34.25 19.58 9.34 10.60 4.08 2.78 21.51 10.33 5.49

18.40 9.89 5.80 28.10 18.49 10.81 17.92 6.68 3.20 20.59 16.15 6.92

22.19 13.09 7.17 21.43 14.00 8.51 15.86 7.28 4.12 24.97 11.06 5.77

36.63 19.87 10.33 25.33 14.48 7.43 30.61 14.36 5.44 28.20 10.11 4.36

39.67 22.08 11.10 5.95 3.98 3.01 39.23 14.03 4.94 7.78 3.56 2.79

46.00 24.31 10.62 4.30 2.94 2.30 36.61 12.82 6.00 5.27 2.62 2.19

157

Table E1: Continued

29.81 17.56 8.28 3.12 2.26 1.91 27.73 6.83 3.94 4.07 1.94 1.74

58.19 34.77 15.37 4.73 3.53 2.81 33.08 12.85 7.27 5.66 2.91 2.40

48.91 29.19 13.52 4.42 3.21 2.60 36.37 13.89 5.50 5.24 2.60 2.15

48.90 22.39 8.62 24.97 13.17 4.88 22.17 9.52 3.80 25.67 12.27 4.93

67.54 36.69 11.13 16.45 11.62 6.42 26.21 8.41 5.17 14.74 10.30 5.72

31.71 20.18 10.35 16.60 10.68 5.80 28.66 15.93 8.75 13.07 8.80 5.31

20.25 15.52 9.28 15.07 10.45 5.98 18.25 12.52 8.28 10.93 8.43 5.07

25.38 14.58 6.19 12.03 8.59 4.99 23.38 13.58 8.19 8.72 7.21 4.50

24.04 13.75 6.43 10.67 7.65 4.58 26.21 13.30 7.06 9.58 6.41 4.33

24.70 14.25 6.90 10.73 8.10 4.98 35.61 18.08 10.50 8.94 6.37 4.41

26.41 15.42 7.64 13.87 10.32 6.26 38.96 18.80 9.99 10.12 7.24 4.77

28.45 8.04 3.68 17.68 13.46 8.34 30.03 9.60 4.07 13.91 10.09 6.74

32.26 8.28 3.59 10.27 7.36 4.84 30.68 9.08 3.26 9.12 6.20 4.49

21.68 8.31 3.94 11.71 8.60 6.01 26.16 11.08 5.69 14.62 6.97 5.34

35.58 19.94 8.70 13.90 11.27 8.43 30.37 20.60 13.50 12.26 9.31 7.00

33.52 16.23 7.61 83.53 33.38 15.43 38.25 19.49 12.48 92.68 35.43 16.38

40.70 27.85 17.54 72.84 45.88 21.26 37.64 21.13 14.46 62.63 31.60 16.48

55.44 28.29 19.49 78.01 49.03 22.01 63.66 34.86 20.62 77.20 39.17 17.08

57.86 24.07 20.38 57.06 34.39 9.86 69.78 28.42 20.90 47.68 27.75 10.40

48.70 24.77 15.62 40.27 25.45 13.40 50.27 28.03 13.95 34.65 18.77 9.77

36.74 21.08 17.59 58.96 29.57 13.63 29.83 18.59 14.97 59.02 20.69 10.18

34.53 22.53 16.36 62.56 31.24 13.68 27.21 19.48 16.13 59.60 21.68 9.33

34.34 25.02 15.94 15.57 12.22 7.64 21.64 13.95 12.47 14.39 9.23 5.96

38.06 31.94 25.50 16.22 12.75 8.12 36.75 19.93 14.97 14.16 9.39 6.24

37.97 31.98 25.65 17.36 13.86 9.08 37.68 18.07 13.76 14.44 10.00 6.82

48.60 23.04 13.18 15.57 12.22 7.64 31.62 14.04 8.62 7.12 6.20 5.41

41.94 23.70 14.21 16.22 12.75 8.12 38.48 16.31 9.86 14.86 10.84 7.47

62.58 36.34 15.70 17.36 13.86 9.08 58.24 24.46 13.32 10.88 8.76 6.33

32.53 17.62 10.63 31.51 23.38 13.79 25.97 9.87 6.07 34.88 17.70 9.94

42.14 24.35 14.48 22.62 18.15 12.26 34.47 15.97 9.92 26.26 14.87 9.23

35.49 19.28 8.88 24.08 19.07 12.42 27.58 11.39 6.88 28.07 14.72 8.93

20.10 12.67 7.10 5.96 5.61 4.94 26.42 12.63 7.18 3.89 3.48 3.11

20.77 13.23 7.46 6.64 6.19 5.45 17.92 10.11 6.16 4.79 4.12 3.73

22.10 14.20 8.08 36.37 19.43 10.00 15.66 9.36 5.75 29.00 10.32 6.40

32.80 23.19 14.35 39.06 20.29 9.30 22.05 13.56 8.65 43.86 10.38 5.77

26.05 19.03 12.40 46.60 24.27 11.24 20.46 13.05 8.73 31.73 12.55 6.94

25.13 19.24 13.56 23.83 11.69 6.66 17.69 11.02 7.61 29.20 6.66 4.05

158

Table E1: Continued

24.75 18.29 11.60 37.11 20.19 9.34 28.08 18.45 11.42 29.31 10.33 5.74

22.82 17.18 11.30 38.84 16.00 6.36 24.00 15.84 10.57 49.63 8.30 4.76

25.69 15.55 10.40 24.02 14.59 7.07 27.00 15.52 10.28 24.04 9.82 3.65

25.69 23.21 18.77 65.38 34.69 10.28 18.74 15.58 12.52 64.92 21.90 7.94

42.81 25.72 17.99 45.04 27.23 10.24 36.59 23.48 18.73 36.88 11.88 5.71

17.68 21.04 12.76 63.22 32.06 11.62 20.49 26.06 18.63 45.28 11.75 5.93

27.06 22.97 16.36 9.57 8.78 7.24 23.45 19.80 17.33 8.68 6.88 5.42

13.06 11.30 9.18 8.75 8.25 6.93 8.43 8.05 6.91 5.97 5.30 4.46

14.56 11.82 9.13 11.33 10.58 8.96 9.83 8.12 6.49 8.02 7.36 5.21

18.00 15.06 10.60 71.21 32.07 10.07 12.03 10.49 8.08 83.61 15.92 5.69

10.97 8.72 6.62 53.34 23.71 10.14 14.07 7.65 5.76 65.44 10.31 5.34

10.88 8.63 6.35 49.39 24.35 9.94 10.95 7.56 5.44 67.17 12.73 5.81

10.33 8.23 6.18 39.95 15.62 7.65 10.93 6.82 5.41 52.83 10.03 4.46

7.83 6.42 4.98 48.19 29.66 12.68 9.80 6.18 4.90 76.12 19.04 7.73

10.03 8.02 5.79 34.97 21.24 10.01 10.55 7.03 5.02 46.87 11.62 5.48

13.06 10.69 7.87 25.03 12.90 5.53 12.29 9.10 6.71 39.85 8.03 3.91

8.59 6.94 5.32 31.86 16.36 7.04 11.44 6.30 4.70 43.51 10.48 4.69

7.12 5.83 4.49 41.03 22.65 10.15 10.44 5.29 4.08 52.66 15.52 6.87

8.15 6.44 4.68 59.75 21.45 7.70 9.46 5.99 4.37 61.54 18.04 6.95

45.48 39.13 28.88 60.64 34.03 13.49 44.84 35.43 26.01 70.00 31.80 13.56

43.68 37.45 26.90 78.15 31.11 13.44 47.52 40.94 30.07 88.79 24.71 8.56

24.19 23.11 18.90 48.57 21.56 10.50 21.82 20.19 16.98 58.81 16.80 6.52

13.27 10.69 8.38 46.65 26.65 10.86 10.80 8.37 6.48 68.57 19.21 7.98

13.11 11.13 8.67 30.64 16.87 7.79 10.31 8.51 6.73 45.37 11.99 4.64

11.33 10.15 8.29 5.94 7.01 4.89 9.33 7.94 6.66 4.85 4.25 3.59

16.22 13.23 10.28 4.68 4.11 3.38 12.70 9.66 7.47 3.72 3.03 2.59

9.12 8.00 6.71 5.88 5.77 4.32 8.55 6.30 5.62 4.99 3.69 3.22

9.03 8.08 6.77 65.22 25.82 6.77 9.04 6.82 5.61 86.21 12.82 4.13

37.71 22.98 13.71 21.08 13.21 5.90 81.38 25.48 10.66 18.21 7.69 3.57

44.59 23.85 12.15 18.97 12.77 6.12 57.72 18.40 9.98 19.79 8.60 4.48

65.37 34.28 13.73 40.46 12.12 9.81 43.70 15.75 9.88 38.08 10.23 4.99

86.28 51.79 21.45 37.46 10.18 8.01 71.23 43.91 20.48 29.51 9.72 5.66

82.99 48.40 27.37 31.37 13.81 8.56 88.18 47.81 20.15 21.99 9.95 5.37

18.98 12.46 9.26 20.41 12.57 6.65 35.71 10.65 7.30 17.64 8.23 4.26

27.64 18.00 11.94 19.35 11.05 5.69 18.71 6.03 5.05 18.21 6.27 3.90

16.26 10.21 7.84 23.91 15.02 7.29 15.68 8.49 6.22 16.45 8.04 4.51

30.35 17.74 11.68 25.68 16.91 9.04 48.50 15.52 9.30 19.51 10.67 5.83

159

Table E1: Continued

25.87 15.81 10.19 26.61 18.29 9.83 36.60 11.07 7.32 26.07 11.07 6.25

35.68 20.73 12.22 39.92 27.56 15.28 28.35 12.10 7.74 28.79 15.44 8.92

19.03 12.86 8.69 19.67 12.62 7.42 26.68 7.49 6.12 19.04 8.55 5.49

13.76 8.53 6.54 23.19 13.89 6.99 35.65 12.46 8.48 26.06 9.86 4.46

23.50 14.68 10.02 28.07 18.49 9.93 26.75 9.42 7.17 25.74 12.48 7.35

28.93 22.05 14.78 12.09 10.11 7.36 29.30 16.92 11.70 15.62 9.94 7.58

30.90 25.24 17.78 22.92 15.25 8.61 24.34 17.35 12.95 29.23 11.76 6.85

34.07 29.43 18.44 18.74 14.07 8.81 31.69 30.43 16.98 23.21 11.11 4.42

25.02 14.46 8.55 3.82 3.63 3.09 24.07 10.22 9.13 4.32 2.92 2.62

17.71 10.74 7.38 4.47 4.16 3.44 20.66 9.05 5.16 3.18 3.07 2.67

13.82 9.86 7.57 4.84 4.24 3.54 18.71 9.01 6.72 3.58 2.62 3.94

14.52 9.20 6.23 4.44 4.13 3.42 18.97 7.97 4.70 3.69 3.36 3.00

17.69 11.45 7.07 7.72 6.72 5.25 22.05 10.45 5.56 4.91 3.88 3.26

14.84 10.48 6.65 4.98 4.65 3.98 14.77 9.20 5.56 4.02 3.62 3.13

28.89 16.82 10.01 35.09 22.37 11.28 28.90 11.43 6.95 36.83 16.65 10.11

36.84 21.81 11.90 37.11 22.68 10.63 27.42 14.68 9.01 40.86 15.50 2.31

47.10 27.49 13.36 43.74 23.52 8.96 40.86 16.90 9.99 57.13 15.52 7.99

38.84 23.02 12.52 41.02 24.14 10.27 39.67 8.94 5.29 46.60 12.17 8.50

30.74 19.02 10.63 49.12 29.85 12.76 41.75 12.08 6.94 47.75 15.83 5.47

22.08 12.11 7.65 42.63 25.91 11.77 47.15 17.40 8.63 32.62 14.54 5.64

9.72 8.58 7.24 12.31 8.63 5.92 9.39 6.62 5.91 20.76 7.76 3.20

10.44 9.06 7.48 24.29 12.29 6.88 7.74 6.04 5.22 36.06 14.12 2.98

10.31 8.80 7.17 26.13 16.10 8.16 9.48 6.85 5.75 19.14 4.94 1.58

160

Table E2: Deleted Outliers/ Abnormal Sensor Deflections Obtained from FWD and LWD Testing

FWD LWD

D0 (mils) D1 (mils) D2 (mils) D0 (mils) D1 (mils) D2 (mils)

189.07 124.61 * * 36.06 20.62

165.68 * 26.90 134.55 36.29 20.90

116.02 * 39.88 162.30 43.85 *

* 71.99 * * 43.53 23.53

103.24 55.64 36.72 * 46.90 23.13

105.98 * 28.88 151.43 40.00 18.73

121.38 97.91 * * 39.67 21.63

* 66.44 27.37 * 41.28 *

100.24 63.29 * * 56.27 *

104.3 58.37 * 134.04 38.42 20.48

103.62 48.44 * * 38.03 20.15

* 113.94 37.07 238.65 42.76 *

134.74 * 26.34 244.78 35.43 30.57

126.67 76.98 * * 40.94 *

* 95.16 * 144.90 42.23 26.01

117.15 * 32.69 * 35.43 30.07

*No outlier was identified.

161

APPENDIX F: EFFECTIVE STRUCTURAL NUMBERS OF AASHTO EQUATIONS

AND THE ROHDE METHOD

Figure F1: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Auglaize County.

Figure F2: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Mercer County.

0

1

2

3

4

5

6

Eff

ectiv

e St

ruct

ural

Num

ber

Auglaize County Roads

AASHTO 5.4.5 Equations vs. Rohde Method

AASHTO 5.4.5Equations

ROHDE Method

0

1

2

3

45

6

Eff

ectiv

e St

ruct

ural

N

umbe

r

Mercer County Roads

AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations ROHDE Method

162


versus Rohde Method of Madison County.

Figure F4: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Champaign County.

0

1

2

3

4

5

Eff

ectiv

e St

ruct

ural

Num

ber

Madison County Roads

AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations

ROHDE Method

0

1

2

3

4

5

Eff

ectiv

e St

ruct

ural

Num

ber

Champaign County Roads


ROHDE Method

163

Figure F5: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Muskingum County.

Figure F6: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Carroll County.

0

2

4

6

8

10

12

14

Eff

ectiv

e St

ruct

ural

Num

ber

Muskingum County Roads


ROHDE Method

0

2

4

6

8

10

12

Eff

ectiv

e St

ruct

ural

Num

ber

Carroll County Roads


ROHDE Method

164

Figure F7: Effective Structural Numbers Based on County Roads, AASHTO Equations versus Rohde Method of Harrison County.

0

2

4

6

8

10

Eff

ectiv

e St

ruct

ural

Num

ber

Harrison County Roads


ROHDE Method

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

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