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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
Correlation Study on the Falling Weight Deflectometer and Light Weight Deflectometer
for the Local Pavement Systems
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
Correlation Study on the Falling Weight Deflectometer and Light Weight Deflectometer
for the Local Pavement Systems
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
4
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.
5
DEDICATION
I dedicate this work to my family for giving me support and encouragement throughout
my career
6
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
9
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
10
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
Table 3.5: Calculated Layer Coefficients Range Based on Material Types, AASHTO 2.3.5
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
11
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
13
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
Figure 3.12: Box Plot of Layer Coefficients for Each Widening/Construction Treatment,
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
14
Figure 3.20: Box Plot Showing Backcalculated Layer Moduli for Each Widening
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
15
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
Figure 4.14: Regression Analysis Fitting Linear Trendline to All Layer Coefficients
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
16
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
Figure F2: Effective Structural Numbers Based on County Roads, AASHTO Equations
versus Rohde Method of Mercer County. ....................................................................... 161
Figure F3: Effective Structural Numbers Based on County Roads, AASHTO Equations
versus Rohde Method of Madison County. .................................................................... 162
Figure F4: Effective Structural Numbers Based on County Roads, AASHTO Equations
versus Rohde Method of Champaign County. ................................................................ 162
17
Figure F5: Effective Structural Numbers Based on County Roads, AASHTO Equations
versus Rohde Method of Muskingum County. ............................................................... 163
Figure F6: Effective Structural Numbers Based on County Roads, AASHTO Equations
versus Rohde Method of Carroll County. ....................................................................... 163
Figure F7: Effective Structural Numbers Based on County Roads, AASHTO Equations
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.
D 0.1586 d . Equation 2.3
39
D 0.2353 d . Equation 2.4
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
w 0.22d 0.05Equation 2.6
w 0.28d 0.01Equation 2.7
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
LWD(MPa) = 1.03FWD(MPa)
Granular subgrade
0.97 Prima 100 (Nazzal et al., 2004)
LWD(MPa) = 1.33FWD(MPa)
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,
D3 = FWD sensor deflection 36 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
Multilayered Linear Elastic,
Boussinesq theory
Does not Calculate
MODCOMP 5.0
Flexible and
Rigid Static
Bowl Matching
Root Mean Square Error
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
Multilayered Linear Elastic,
ELSYM5
User Defines Position
MECHBACK
Flexible Static Bowl
Matching
Root Mean Square Error
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
Root Mean Square Error
Odemark-Boussinesq
Method Fixed
MODULUS 6.0
Flexible and
Rigid Static
Bowl Matching
Root Mean Square Error
Multilayered Linear Elastic,
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
FWDa , LWDb , PSPAc, & DCPd
Permazine FDR HMA
Asphalt FDR
Carroll
Aggregate Overlay
HMA
Cement FDR
Auglaize 3 13
HMA
FWDa , LWDb , PSPAc, & DCPd
Full Depth Grinding
Partial Grinding
Mercer 70/30 asphalt/cement
HMA
Champaign
4 14
HMA
FWDa , LWDb , PSPAc, & DCPd
Mechanical FDR Cement FDR
Madison HMA
Cement FDR Geogrid
Muskingum 5 24
HMA
FWDa , LWDb , PSPAc, & DCPd
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)
Radial Distance (in)
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
Table 3.4: Calculated Layer Coefficients Range Based on Material Types, AASHTO 5.4.5
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
Materials Types Calculated Layer Coefficient Range
Portland Cement Concrete (PCC) 0.40 - 0.78
White topping 0.34 - 0.68
HMA 0.33 - 0.56
Mechanical FDR 0.07 - 0.24
Fiber Cement 0.02 - 0.51
Cement FDR 0.01 - 0.28
70 30 asphalt/Cement 0.21 - 0.35
Concrete Steel 0.13 - 0.33
Full Depth Grindings 0.09 - 0.31
Partial Depth Grindings 0.16 - 0.31
Lime FDR 0.01 - 0.21
Permazine FDR 0.11 - 0.16
Fabric Reinforced Stone 0.10 - 0.35
Fly Ash FDR 0.14 - 0.18
Brick & 411 0.12 - 0.30
Surge & 411 0.10 - 0.20
Motorpave 0.10 - 0.53
Asphalt FDR 0.15 - 0.34
Geogrid 0.02 - 0.20
Aggregate Overly 0.20 - 0.25
Also, the results presented in Table 3.5 are graphically presented in the box plot in
Figure 3.12
79
Figure 3.12: Box Plot of Layer Coefficients for Each Widening/Construction Treatment,
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)
Bottom 2Q Box 3Q Box Mean
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
Banner School-C70-11
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
Materials Types Calculated Layer Coefficient Range
Portland Cement Concrete (PCC) 0.35 - 0.92
Whitetopping 0.23 - 0.73
HMA 0.40 - 0.43
Mechanical FDR 0.12 - 0.27
Fiber Cement 0.29 - 0.71
Cement FDR 0.002 - 0.50
70 30 asphalt/Cement 0.003 - 0.18
Concrete Steel 0.122 - 0.71
Full Depth Grindings 0.078 - 0.16
Partial Depth Grindings 0.085 - 0.18
Lime FDR 0.017 - 0.25
Permazine FDR 0.026 - 0.08
Fabric Reinforced Stone 0.099 - 0.31
Fly Ash FDR 0.12 - 0.15
Brick & 411 0.027 - 0.48
Surge & 411 0.053 - 0.18
Motorpave 0.053 - 0.73
Asphalt FDR 0.022 - 0.14
Geogrid 0.024 - 0.18
Aggregate Overly 0.103 - 0.13
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)
Bottom 2Q Box 3Q Box Mean
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.
Figure 3.20: Box Plot Showing Backcalculated Layer Moduli for Each Widening
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)
Bottom 2Q Box 3Q Box Mean
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
D 1.4607 d . Equation 4.2
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)
Extrapolated LWD Sensor Deflections, (mils)
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)
Extrapolated LWD Sensor Deflections, (mils)
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
model was considered statistically significant with a probability level of P = 0.00 < 0.001
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
model was considered statistically significant with a probability level of P = 0.00 < 0.001
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)
Extrapolated LWD Sensor Deflections, (mils)
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
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
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
Defiance Augaize +Mercer
Champaign +Madison
Muskingum Harrison +Carroll
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
Defiance Augaize +Mercer
Champaign +Madison
Muskingum Harrison +Carroll
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.
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
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
REFERENCES
Ahmed, M. U. (2010). Evaluation of FWD software and deflection basin for airport pavements. Master thesis, Civil Engineering Department, The University of New Mexico, Albuquerque, New Mexico.
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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
Layer 1 Layer 2 Layer 3
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
Layer 1 Layer 2 Layer 3
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
*Placed Layer on the Natural Subgrade, No Thickness Was Measured.
137
Table A4: Layer Thicknesses and Material Properties, Auglaize County
Road Name Core ID
Layer 1 Layer 2 Layer 3
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
*Placed Layer on the Natural Subgrade, No Thickness Was Measured.
138
Table A5: Layer Thicknesses and Material Properties, Mercer County
Road Name
Core ID
Layer 1 Layer 2 Layer 3
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
asphalt/cement Natural
Subgrade 2 6.0 14.0
3 6.0 14.0
Harris (C175B)
1 9.8
HMA
5.0 70/30
asphalt/cement Natural
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
Layer 1 Layer 2 Layer 3
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
*Placed Layer on the Natural Subgrade, No Thickness Was Measured.
140
Table A7: Layer Thicknesses and Material Properties, Madison County
Road Name
Core ID
Layer 1 Layer 2 Layer 3
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)
Radial Distance (in)
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
Radial Distance, (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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)
Radial Distance (in)
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
Radial Distance, (in)
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
Banner School-C70-09
3.99 537.66 13.75 18.30 6.66 Birmingham
-C10-02 8.12 71.76 25.14 14.50 2.68
Banner School-C70-11
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
Figure F3: Effective Structural Numbers Based on County Roads, AASHTO Equations
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
AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations
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
AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations
ROHDE Method
0
2
4
6
8
10
12
Eff
ectiv
e St
ruct
ural
Num
ber
Carroll County Roads
AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations
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
AASHTO 5.4.5 Equations vs. Rohde MethodAASHTO 5.4.5Equations
ROHDE Method
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