i

Accelerated Implementation of Intelligent Compaction Technology for 

Embankment Subgrade Soils, Aggregate Base, and Asphalt Pavement 

Materials 

Final Report 

Publication No. FHWA-IF-12-002 

July 2011 
i

1. Report No.

FHWA-IF-12-002

2. Government Accession No.

N/A

3 Recipient Catalog No.

N/A

4. Title and Subtitle

Accelerated Implementation of Intelligent Compaction Technology for 

Embankment Subgrade Soils, Aggregate Base, And Asphalt Pavement 

Materials -

Final Report

5. Report Date

July 2011

6. Performing Organization Code

N/A

7. Author(s)

George Chang, Qinwu Xu, and Jennifer Rutledge of the Transtec Group; Bob 

Horan of Asphalt Institute; Larry Michael of LLM Asphalt Technology 

Consultant; and David White and Pavana Vennapusa of Iowa State 

University

8. Performing Organization Report No.

N/A

9. Performing Organization Name and Address

The Transtec Group, Inc.

6111 Balcones Drive

Austin TX 78731

10. Work Unit No. (TRAIS)

N/A

11. Contract or Grant No.

DTFH61-07-C-0032

12. Sponsoring Agency Name and Address

Federal Highway Administration

Office of Pavement Technology, HIPT-10

1200 New Jersey Avenue, SE

Washington, DC 20590

13. Type of Report and Period 

Covered

Final report 

14. Sponsoring Agency Code

N/A

15. Supplementary Notes

Contracting Officer’s Technical Representative: Victor (Lee) Gallivan

16. Abstract

Intelligent compaction (IC) is an emerging technology, and for some applications it is mature enough for implementation 

in field compaction of pavement materials. The intent of this project is to realize the blueprint in the FHWA IC strategic 

plan. This study was under the Transportation Pooled Fund project, TPF-5(128), which includes 12 participating state 

department of transportation: Georgia, Indiana, Kansas, Maryland, Minnesota, Mississippi, New York, North Dakota, 

Pennsylvania, Texas, Virginia, Wisconsin.

This document is the final report for this pooled fund IC project.

17. Key words

Compaction, intelligent compaction, roller, asphalt, HMA overlay, pavement 

performance.

18. Distribution Statement

No restrictions. This document is 

available to the public through the 

National Technical Information Service, 

Springfield, Virginia 22161

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages 22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized (art. 5/94)
ii

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius o

C 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit o

F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

Tables of Contents

Executive Summary......................................................................................................................................2

Major Findings .........................................................................................................................................3

Benefits of IC Technology .......................................................................................................................3

Soils IC.................................................................................................................................................3

HMA IC ...............................................................................................................................................4

Recommendations for Future Research....................................................................................................4

Intelligent Compaction (IC) Road Map....................................................................................................5

Acknowledgement ........................................................................................................................................7

Symbols and Terminology..........................................................................................................................10

Chapter 1 Introduction...........................................................................................................................13

Background.............................................................................................................................................13

Report Structure......................................................................................................................................14

Chapter 2 IC Roller Systems .................................................................................................................16

IC Rollers................................................................................................................................................16

How IC Rollers Work ........................................................................................................................16

Single Drum IC Rollers......................................................................................................................19

Double Drum IC Rollers....................................................................................................................20

GPS Requirements and Setup.................................................................................................................22

GPS Precision.....................................................................................................................................23

Datum Coordinate System .................................................................................................................24

GPS Equipment Setup for IC .............................................................................................................25

IC Measurement Types...........................................................................................................................27

Introduction........................................................................................................................................27

Development of IC Measurements.....................................................................................................28

IC Measurement Types ......................................................................................................................29

Review of Field Correlation Studies.......................................................................................................40

Examine IC Measurements and Correlation Analysis............................................................................49

Influence of Drum Behavior on ICMVs.............................................................................................49

Correlation between in-situ point tests and IC indices.......................................................................50

Auto-feedback Control (AFC) mode..................................................................................................51

Correlation among Different IC Measurements.................................................................................51

Verification of the IC models.............................................................................................................52

A Proposed New IC Model ....................................................................................................................54

Chapter 3 Field Spot Tests for IC Implementation................................................................................56

In-Situ Spot Tests for Soils and Subbase................................................................................................56

In-Situ Spot Tests for HMA ...................................................................................................................59

Chapter 4 IC Analysis and Report .........................................................................................................63

IC Data....................................................................................................................................................63

Required IC Data Elements................................................................................................................63

IC Data Storage Types.......................................................................................................................64

IC Data Comparisons.........................................................................................................................66

Viewing of IC Data.................................................................................................................................66

Basic Statistics and Geostatistics............................................................................................................67

Basic Statistics ...................................................................................................................................67

Geostatistics - Semi-variogram..........................................................................................................68

Compaction Curves................................................................................................................................69

Correlation Analysis...............................................................................................................................69

Univariate linear regression ...............................................................................................................69

Multivariate linear regression.............................................................................................................70
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Chapter 5 Demonstration Projects Overview ........................................................................................72

Soils and Subbase IC Demonstrations....................................................................................................73

Demonstration Activities....................................................................................................................73

Hot Mix Asphalt (HMA) IC Demonstration ..........................................................................................75

Demonstration activity .......................................................................................................................75

Recommendations ..................................................................................................................................77

Chapter 6 Demonstration Projects for Soils IC......................................................................................77

State Demonstration Results ..............................................................................................................78

Compaction Curves..........................................................................................................................110

Compaction Uniformity ...................................................................................................................116

Correlation Study .............................................................................................................................123

Chapter 7 Demonstration Projects for HMA IC ..................................................................................134

State Demonstration Results ............................................................................................................134

Rolling Patterns and Mapping..........................................................................................................183

Compaction Curves..........................................................................................................................184

Compaction Uniformity ...................................................................................................................186

Correlation Study .............................................................................................................................188

Chapter 8 Guidelines for IC Implementation.......................................................................................194

IC Road Map ........................................................................................................................................194

General IC Plan ....................................................................................................................................196

Special Provision Documents...........................................................................................................197

Project Website ................................................................................................................................197

On-Site Data Collection Schedule....................................................................................................200

Data Sources.....................................................................................................................................201

IC Data Management............................................................................................................................202

Objectives of Data Management ......................................................................................................202

Data Management Framework.........................................................................................................202

IC Specifications...................................................................................................................................204

IC Specification for Soils and Subbase ............................................................................................204

IC Specification for HMA................................................................................................................208

Generic IC Specifications for Soils, Subbase HMA ........................................................................210

IC Training and Workshops .................................................................................................................211

Open House Activities of IC Demonstrations..................................................................................211

Recommended Future IC Training and Workshops.........................................................................211

References.................................................................................................................................................213

Appendix A. Understand Semi-variogram as a Metric for Uniformity ...................................................217

Data conversion................................................................................................................................217

Location and lag vector definition....................................................................................................218

Direction and model parameter definition........................................................................................218

Calculation of semi-variogram:........................................................................................................219

Model fit...........................................................................................................................................220

Appendix B. Understand GPS and Datum...............................................................................................223

How GPS Works ..................................................................................................................................223

Use of Differential GPS in IC Systems ................................................................................................224

Position and Time from GPS................................................................................................................225

Use of UTM in IC Data Process...........................................................................................................226

Datum Conversion between Geodetic Longitude/latitude and UTM ...................................................227

Appendix C. How to Setup GPS for IC ...................................................................................................228

Appendix D. Generic Soils IC Specification ...........................................................................................230

Appendix E. Generic HMA IC Specification ..........................................................................................242

Appendix F. Generic Subbase IC Specification.......................................................................................253
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List of Tables

Table 1. Summary for Single Drum IC Rollers.........................................................................................20

Table 2. Summary for Asphalt IC Rollers .................................................................................................21

Table 3. Key features of different ICMV systems......................................................................................30

Table 4 Summary of IC Measurements ......................................................................................................34

Table 5. Summary of field correlation studies............................................................................................42

Table 6: Essential IC Data Information .....................................................................................................63

Table 7: Essential IC Data Elements..........................................................................................................64

Table 8. Comparison of Exported IC Data. ...............................................................................................66

Table 9. FHWA/TPF Soil/Subbase IC Demonstration Activities..............................................................74

Table 10. FHWA/TPF HMA IC Demonstration activities. .......................................................................76

Table 11. KS, results of multiple regression analysis for influence of amplitude and moisture content on 

CCVPD – TBs 1, 3(lift4), 4, 6, and 7 and MDP80 – TBs 1 and 2.......................................................132

Table 12. NY, Summary of multiple regression analysis – TB10 lane 2..................................................133

Table 13. Fitted Semi-variogram parameters...........................................................................................187

Table 14. Multivariate Correlation Results of Minnesota Project ...........................................................192

Table 15. Multivariate Correlation Results of Wisconsin Project ...........................................................193

Table 16. An example of an on-site daily activity schedule (ICPF TxDOT demo).................................200

Table 17. An example of an on-site test schedule and machine settings.................................................201

Table 18. Review of Current IC Specifications........................................................................................205

Table 19. IC specifications for Equipment, GPS, Documentation, Mapping, and Machine Setting........208

Table 20: GPS connection requirements for IC rollers............................................................................225

List of Figures

Figure 1. Participating TPF State DOTs and schedule for field demonstrations. ........................................2

Figure 2. IC Road Map. ...............................................................................................................................6

Figure 3. Bomag VarioControl System......................................................................................................16

Figure 4. Case/Ammann auto-feedback control (AFC) system. ................................................................17

Figure 5. Calibration test – with compaction growth curves for Case/Ammann ICMV (kSΙPD) and in-situ 

point measurements (ICPF TXDOT demo)........................................................................................18

Figure 6. Infra-red temperature sensor on a Sakai IC roller. .....................................................................19

Figure 7. Single smooth drum IC rollers....................................................................................................19
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Figure 8. Double drum IC rollers...............................................................................................................21

Figure 9. RTK GPS receiver and antenna on a Sakai roller (ICPF MnDOT demo)..................................22

Figure 10. RTK using a Handheld rover/receiver with a single base/reference station.............................23

Figure 11. RTK using a Handheld rover/receiver with a reference network. ............................................24

Figure 12. UTM Zones in the US. .............................................................................................................25

Figure 13. A Trimble GPS base station (ICPF MnDOT demo)..................................................................26

Figure 14. Validation of roller mounted GPS with a hand-held rover.......................................................26

Figure 15. Overview of ICMV compaction monitoring systems...............................................................27

Figure 16. Ammann and Case rollers (padfoot and smooth drum) equipped with roller-integrated ks

measurement system on-board display units with ACE-Plus® software.............................................31

Figure 17. A single-drum Bomag roller equipped with roller-integrated EVIB measurement system onboard display units with BCM-05® software. .....................................................................................31

Figure 18. Caterpillar rollers (padfoot and smooth drum) equipped with roller-integrated MDP and CMV 

measurement systems on-board display unit with AcuGrade® software. ...........................................32

Figure 19. A single-drum Dynapac roller equipped with roller-integrated CMV measurement system and 

on-board display unit with DCA® software. .......................................................................................32

Figure 20. Sakai rollers (padfoot and smooth drum) equipped with roller-integrated CCV measurement 

system and on-board display unit with Aithon-MT® software. ..........................................................33

Figure 21. Trimble CB430 on-board display unit with roller-integrated CMV measurement system. ......33

Figure 22. Illustration of changes in drum harmonics with increasing ground stiffness (modified from 

Thurner and Sandström 1980). ...........................................................................................................35

Figure 23. Changes in amplitude spectrum with increasing ground stiffness (modified from Scherocman 

et al. 2007). .........................................................................................................................................36

Figure 24. Force-displacement diagram for kb............................................................................................36

Figure 25. Lumped parameter two-degree-of-freedom spring dashpot model representing vibratory 

compactor and soil behavior (reproduced from Yoo and Selig 1980)................................................37

Figure 26. Ammann two-piece eccentric mass assembly and AFC of vibration amplitude and frequency 

(picture courtesy of Ammann)............................................................................................................38

Figure 27. Bomag roller eccentric mass assembly and vectoring to vary the vertical excitation force (left) 

and principle of Bomag’s automatic feedback control (AFC) system (right).....................................39

Figure 28. Various in-situ QC/QA test measurements: (a) nuclear moisture-density gauge; (b) electrical 

density gauge; (c) light weight deflectometers; (d) soil stiffness gauge; (e) static plate load test; (f) 

falling weight deflectometer; (g) Briaud compaction device; (h) seismic pavement analyzer; (i) 

Clegg hammer; (j) shelby tube sampling; (k) dynamic cone penetrometer; (l) static cone 
vii

penetrometer; and (m) heavy test rolling............................................................................................41

Figure 29. Illustration of differences in measurement influence depths for different measurements 

(modified from White 2008)...............................................................................................................49

Figure 30. Influence of soil modulus and drum behavior on ICMVs (from Adam and Kopf 2004). .........50

Figure 31. Influence of drum behavior on ICMVs relative to soil modulus (based on numerical 

simulations from Adam and Kopf 2004) (Note kB = ks).....................................................................50

Figure 32. Interface for ICMV simulation..................................................................................................52

Figure 33. Simulated Case/Ammann Kb. ....................................................................................................53

Figure 34. Simulated vs. measured Case/Ammann Kb. ..............................................................................53

Figure 35. Simulated Bomag Evib................................................................................................................54

Figure 36. Proposed Roller-pavement Interaction model. .........................................................................55

Figure 37. Soil and Subbase in-situ test devices subgrade and flex base material. ...................................57

Figure 38. EV1 and EV2 determination procedure from static PLT for subgrade and flex base material....59

FIGURE 39. HMA in-situ test devices and mechanisms...........................................................................60

Figure 40. Time history data vs. post-processed data (Sakai America).....................................................65

Figure 41. All passes data and proofing data (Sakai America)..................................................................65

Figure 42. A Screen Shot of the Veda tool (GDOT IC demonstration).....................................................67

Figure 43. Basic univariate statistics and histogram report in Veda..........................................................67

Figure 44. Semivariogram and the Fitted Parameters................................................................................68

Figure 45. Semi-variogram report in Veda. ...............................................................................................68

Figure 46. Compaction Curve Report in Veda. .........................................................................................69

Figure 47. Correlation Report in Veda.......................................................................................................70

Figure 48. Distribution and Schedule of IC Field Demonstrations............................................................72

Figure 49. Texas soil/subbase demonstration. ...........................................................................................79

Figure 50. Soft and hard spots identified by IC rollers..............................................................................80

Figure 51. Comparison between Case/Ammann kSΙSD and DynapacCMV maps– Test Bed 2 flex base 

and lime-stabilized subgrade...............................................................................................................81

Figure 52. Comparison of laboratory Proctor curves and in-situ w-γd measurements – TB 5 limestabilized subgrade material................................................................................................................81

Figure 53. CMV, BV, and a values for compaction passes on lane 4 of TB6 flex base material..............82

Figure 54. Relationship between kSΙPD and kSΙSD measurements..........................................................83

Figure 55. Comparison between CMV measurements in manual and automatic settings (nominal f = 30 

Hz, v = 3.5 km/h)................................................................................................................................84

Figure 56. Kansas soil IC demonstration...................................................................................................86
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Figure 57. Caterpillar MDP80 and elevation maps and Sakai CCVPD (TB1 subgrade clay material lanes 1 

and 2) ..................................................................................................................................................87

Figure 58. Sakai CCVPD (pad-foot drum, left) vs. CCVSD map (right, smooth drum, right).....................88

Figure 59. MDP80, elevation, pass coverage, and average MDP80 (a = 0.90 mm, f = 33 Hz, v = 4 km/h) per 

pass on TB3 foundation subgrade layer..............................................................................................89

Figure 60. MDP80, elevation, pass coverage, and average MDP80 (a = 0.90 mm, f = 33 Hz, v = 4 km/h) per 

pass on TB3 lift 1 clay fill and shale fill material...............................................................................90

Figure 61. Comparison between MDP80 and EFWD-D4.5 point measurements – TB1 subgrade clay material 

(note: passes 1 and 2 with opposite machine direction of travel).......................................................91

Figure 62. Mississippi Soil/subbase IC demonstration..............................................................................92

Figure 63. Regression analyses between CCV and FWD measurements – TB 1 (left), TBs 1 and 2 (right)

............................................................................................................................................................93

Figure 64. Regression analyses between CCV and LWD measurements..................................................94

Figure 65. Comparison of ks, a*, and f maps obtained shortly after compaction (TB3 and TB5/6) and 

after 2-day cure in manual and AFC modes (TB8 and TB9)..............................................................95

Figure 66. Semi-variograms of ks measurements obtained on TB3 (shortly after mixing and compaction) 

in manual mode, TB8 (after two days of curing) in manual mode, and TB8 in AFC mode...............96

Figure 67. Roller spatial maps, MDP40, CMV, and ks measurements along the middle lane, and DCPCBR profiles at selected locations – TB7 granular subgrade material ...............................................97

FIGURE 68 New York soil/subbase IC Demonstration. ............................................................................98

Figure 69. Good correlation between Bomag EVIB (pass 3) and FWD tests on lane 3 (TB3 embankment 

material)..............................................................................................................................................99

Figure 70. EVIB spatial maps with different machine amplitude settings in manual and AFC mode – TB3 

embankment material, and jumping..................................................................................................100

Figure 71. North Dakota IC demonstration. .............................................................................................103

Figure 72. MDP* and elevation maps of lanes 1 to 3, and MDP* spatial maps for multiple padfoot roller 

passes on lanes 1 to 3 – TB1.............................................................................................................104

Figure 73. (a) Comparison of MDP semi-variograms on TB 4 mixed subgrade, base 1, and base 2 layers, 

(b) Comparison of MDP semi-variograms on TB4, 5, and 6 base layers.........................................105

Figure 74. Photographs of TB4 during placement of TX5 geogrid and salvage base layers....................106

Figure 75. Construction site, materials and in-situ testing........................................................................107

Figure 76. MDP* maps on lanes 1 and 2 – TB1 granular embankment fill..............................................108

Figure 77. Average MDP* with increasing roller passes in forward and reverse gears – TB2 granular 

embankment fill ................................................................................................................................108
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Figure 78. MDP* maps from passes 1 to 8 on lanes 1 and 2 – TB4 silty clay subgrade ..........................109

Figure 79. Geostatistical semi-variograms and histograms of passes 1 and 2 on lift # 1 – TB 3 granular 

embankment fill production area ......................................................................................................109

Figure 80. Correlations between MDP* and in-situ point-MVs – padfoot roller with static setting (TBs 4 

and 5 cohesive embankment fill)......................................................................................................110

Figure 81. NY project: average ICMV and In-situ measurement compaction curves on lanes 2 and 3 –

TB4 gravel subbase material.............................................................................................................112

Figure 82. TX project: average ICMV and In-situ measurement compaction curves ..............................113

Figure 83. KS project: average ICMV and In-situ measurement compaction curves...............................114

Figure 84. ND average MDP* and in-situ point measurement values with increasing roller passes on lanes 

1 to 3 – TB1 ......................................................................................................................................115

Figure 85. Indiana MDP* and in-situ point-MV compaction curves lane 1 (a = 0.90 mm) and lane 2 (a = 

1.80 mm) – TB1 granular embankment fill ......................................................................................116

Figure 86. Texas Kriged contour maps and semi-variograms of CMV and EFWD on TB 7 flex base 

material. ............................................................................................................................................118

Figure 87. KS project: change in semi-variograms, spatial statistics of Caterpillar MDP and Sakai CCV 

with pass. ..........................................................................................................................................119

Figure 88. NY project: semi-variograms of ICMV for different passes...................................................120

Figure 89. MS project: semi-variograms of ICMV for different passes and manual/AFC controls.........121

Figure 90. ND project: semi-variograms of roller MDPs. ........................................................................122

Figure 91. INDOT project: semi-variograms of roller MDPs (granular embankment fill). .....................122

Figure 92. ICMVs vs. LWD measurements..............................................................................................124

Figure 93. ICMVs vs. FWD measurements..............................................................................................125

Figure 94. ICMVs vs. Plate Loading Tests...............................................................................................127

Figure 95. ICMVs vs. DCP tests...............................................................................................................128

Figure 96. ICMVs vs. NG densities..........................................................................................................130

Figure 97. Minnesota HMA IC demonstration. .......................................................................................135

Figure 98. The rolling pattern before and after the roller operator made use of the Sakai CIS. ..............136

Figure 99. Comparison of CCVs of HMA base course layer and subbase layer mapping. ......................136

Figure 100. CCVs of HMA base course layer and subbase layer mapping..............................................137

Figure 101. Comparison of CCV measurements on HMA base course layer and subbase layer (5m 

moving average). ..............................................................................................................................137

Figure 102. Comparison of CCV maps of the HMA base course and subbase. .......................................138

Figure 103. Correlation of CCVs between different layers. .....................................................................138
x

Figure 104. Correlation of CCVs with LWD and DCP tests on subbase.................................................139

Figure 105. Correlation between nuclear gauge test, lab core density results, and CCV for the HMA base 

course (Route 4, SB).........................................................................................................................140

Figure 106. Mississippi HMA IC demonstration.....................................................................................141

Figure 107. Mapping of tack-coated, stabilized subbase with the Sakai double-drum IC roller. ............142

Figure 108. Roller pass No. and Sakai CCV of HMA base.....................................................................143

Figure 109. Sakai CCV of mapping subbase and paving HMA base course...........................................144

Figure 110. Surface temperatures and Sakai CCVs of HMA base course (EB Lane 2). .........................144

Figure 111. Comparison of Sakai ICMV among different layers (EB Lane 1, HMA base vs. Subbase). 145

Figure 112. Sakai CCV vs FWD measurements.......................................................................................145

Figure 113. Geostatistic semi-variogram to access uniformity of asphalt pavement (US 84, EB Lane 1, 

Base course)......................................................................................................................................146

Figure 114. New York State HMA IC demonstration. ............................................................................147

Figure 115. Roller measurement values of HMA 2nd lift base.................................................................148

Figure 116. Sakai ICMVs and HMA temperatures of TB 15 - HMA base course (second lift)..............149

Figure 117. Comparison of Sakai ICMV among different layers.............................................................150

Figure 118. Sakai ICMV vs. NNG (left) and NG (right) density. ............................................................151

Figure 119. Maryland HMA IC demonstration. ......................................................................................152

Figure 120. Night paving of SMA overlay. .............................................................................................153

Figure 121. Mapping results - Bomag Evib and Sakai CCV - TB 02......................................................154

Figure 122. Sakai CCV vs. FWD deflection and PSPA seismic modulus................................................154

Figure 123. Semi-variograms of Sakai CCV for the mapping areas.........................................................155

Figure 124. NG density vs Sakai CCV of TB 03 SMA overlay at discrete test locations.......................155

Figure 125. Georgia HMA IC demonstration at the Park & Ride. ..........................................................157

Figure 126. Sakai CCV map and statistics...............................................................................................158

Figure 127. Sakai CCV of GAB. .............................................................................................................159

Figure 128. Sakai CCV vs NG density of GAB material. .......................................................................160

Figure 129. Sakai CCV vs FWD measurements......................................................................................160

Figure 130. Sakai CCV vs Gmm% of HMA. ..........................................................................................161

Figure 131. Sakai CCV VS LWD measurements of HMA. ....................................................................161

Figure 132. Indiana HMA IC Demonstration. .........................................................................................162

Figure 133. Mapping of milled HMA on PCC slabs using Bomag. ........................................................163

Figure 134. Mapping of milled HMA shoulder on PCC slabs.................................................................163

Figure 135. NNG density of each breakdown roller pass following Bomag and Sakai compaction, 
xi

respectively. ......................................................................................................................................164

Figure 136. Sakai CCV vs. NNG gauge measured Gmm% for combined single passes.........................164

Figure 137. FWD measured deflection under loading plate. ...................................................................165

Figure 138. Bomag Evib vs. FWD measured deflection under the loading plate....................................165

Figure 139. Bomag measurements vs. cored sample Gmm% of HMA intermediate layer (TB 03)........166

Figure 140. Wisconsin HMA IC demonstration. .....................................................................................167

Figure 141. Improvement of rolling patterns using the IC technologies. ................................................168

Figure 142. Sakai mapping existing rubblized and crack-and-seat PCC bases (TB2M). .........................168

Figure 143. Sakai CCV of existing rubblized and crack-and-seat PCC bases (TB1M)............................169

Figure 144. Sakai CCV vs. NNG gauge measured Gmm% for combined single passes.........................170

Figure 145. Night paving and HMA compaction.....................................................................................170

Figure 146. Roller MVs vs. FWD deflections and NG Densities............................................................171

Figure 147. Semi-variograms of HMA base and 2nd lift HMA................................................................171

Figure 148. Compaction curves of HMA base and 2nd lift HMA. ...........................................................172

Figure 149. Texas HMA IC demonstration. ............................................................................................173

Figure 150. Sakai CCVs from the IC mapping on the cement stabilized base, TB 01M.........................173

Figure 151. Correlation between Sakai CCVs (mapping run) and in-situ tests on HMA (FWD deflections 

normalized to 9,000 lb in mils, NNG in %Gmm), TB 01A..............................................................174

Figure 152. Correlation between Sakai CCVs (mapping run) and PSPA tests (normalized to 77 °F in ksi) 

on HMA, TB 01A (left) and TB 02A (right) at 1 and 2 hours after paving, respectively. ...............174

Figure 153. Sakai CCVs for mapping run on fresh HMA and it’s associated semi-variogram, TB 01A.174

Figure 154. Pennsylvania HMA IC demonstration..................................................................................176

Figure 155. Sakai CCVs of mapping binder course and paving on wearing course................................177

Figure 156. Semi-variograms of HMA wearing course...........................................................................178

Figure 157. Compaction curve for HMA wearing course........................................................................179

Figure 158. Virginia HMA IC demonstration..........................................................................................180

Figure 159. Roller GPS validation...........................................................................................................181

Figure 160. Sakai CCV of mapping HMA surface..................................................................................181

Figure 161. Semi-variogrqams of HMA overlay. ....................................................................................182

Figure 162. Compation curves of HMA overlay. ....................................................................................182

Figure 163. NG density at different Volvo roller pass.............................................................................183

Figure 164. Roller Passes before and after using IC technology (Indiana IC demonstration).................183

Figure 165. Sakai CCV of mapping existing stabilized subbase (Mississippi IC demonstration)...........184

Figure 166. Compaction curves of TPF HMA IC Projects......................................................................185
xii

Figure 167. NG/NNG Density Correlation (NY, MS, MD, IN, WI) ........................................................188

Figure 168. Cores density vs. ICMV (MN, MD, GA, IN).......................................................................189

Figure 169. Correlation of Sakai CCVs with Normalized FWD Defections or moduli ..........................190

Figure 170. Correlation of ICMV with LWD measurements (layer moduli unit: MPa, deflection unit: x 

10-6 m)..............................................................................................................................................191

Figure 171. IC Road Map ........................................................................................................................195

Figure 172. An example of IC project webpage (1 of 4) .........................................................................198

Figure 173. An example of IC project webpage (2 of 4) .........................................................................198

Figure 174. An example of IC project webpage (3 of 4) .........................................................................199

Figure 175. An example of IC project webpage (4 of 4) .........................................................................199

Figure 176. IC data management framework...........................................................................................203

Figure 177. Compaction curve – Breakdown Compaction of HMA Base Course - TPF WisDOT IC demo 

(TB 01B-Section 2)...........................................................................................................................209

Figure 178. Semivariogram – Breakdown Compaction of HMA Base Course - TPF WisDOT IC demo 

(TB 01B-Section 2)...........................................................................................................................210

Figure 179. Semi-variogram models with exponential and Gaussian fits (Houlding, 2000)...................217

Figure 180. location and lag vectors. .......................................................................................................218

Figure 181. semnivariogram direction.....................................................................................................219

Figure 182. semnivariogram direction.....................................................................................................220

Figure 183. Experimental semnivariogram..............................................................................................220

Figure 184. Semivariogram model fit......................................................................................................221

Figure 185. Semi-variogram report of IC data.........................................................................................222

Figure 186. Three segments of GPS. .......................................................................................................223

Figure 187. Use GPS in an IC system......................................................................................................224

Figure 188. Position, velocity and time from GPS. .................................................................................225

Figure 189. Earth-centered, earth-fix x-y-z GPS position. ......................................................................226

Figure 190. Geodetic coordinates – latitudes, longitudes, and height above the ellipsoid. .....................226

Figure 191. UTM Zones in the US. .........................................................................................................227

Figure 192. A Trimble GPS base station (left) and a TopCon GPS base station (ICPF WisDOT demo).228

Figure 193. A Trimble GPS rover (left) and a TopCon GPS rover (ICPF WisDOT demo).....................229

Figure 194. Validation of roller mounted GPS with a hand-held rover (ICPF VADOT demo)..............230
2

Executive Summary

The FHWA/Transportation Pooled Fund (TPF) project, TPF-5(128), “Accelerated 

Implementation of Intelligent Compaction Technology for Embankment Subgrade Soils, 

Aggregate Base, and Asphalt Pavement Materials” demonstrated tried-and-true Intelligent 

Compaction (IC) technologies through sixteen (16) field projects and open house activities 

(Figure 1) , numerous meetings and training for TPF State personnel and local earthwork/paving 

contractors, and assistance on the development of State IC specifications.

MN

KS

TX

MS

IN

NY

PA

VA

MD

ND

GA

WI

2008

2009

2010

Mini demo

2 demos

Figure 1. Participating TPF State DOTs and schedule for field demonstrations. 

The key for the success of this three-year project is the seamless cooperation of the Technical 

Working Group (TWG) formed dynamically with knowledgeable and passionate representatives 

from FHWA, the IC project team, the TPF pooled fund states, earthwork/paving contractors and 

many IC roller and component vendors. Twelve (12) State departments of transportation (DOTs) 

participate in the TWG: Georgia, Indiana, Kansas, Maryland, Minnesota, Mississippi, North 

Dakota, New York, Pennsylvania, Texas, Virginia, and Wisconsin. Most importantly, support 

from major IC roller and component vendors, as a part of the TWG, has made these efforts 

possible by providing IC machines and technical support: Ammann/Case, Bomag America, 

Caterpillar, Dynapac, Sakai America, TopCon, Trimble, and Volvo. 

The goals of this project include: 

1. Demonstration of soils/subbase and Hot Mix Asphalt (HMA) IC technologies to 

department of transportation (DOT) personnel, contractors, etc., 

2. Develop an experienced and knowledgeable IC expertise base within DOT, 

3. Assisting DOT in the development of IC quality control (QC) specifications for the 

subgrade, subbase, and HMA pavement materials, and 

4. Identification and prioritization of needed improvements and further research for IC 

equipment and data analysis. 
3

Goal No. 1 was accomplished by demonstrating the abilities of the IC system such as:

tracking roller passes, HMA surface temperatures, and intelligent compaction measurement 

values (ICMV).

Goal No. 2 was accomplished by building the IC knowledge base with extensive field 

experiences, data, and analysis/reports from diverse demonstration projects.

Goal No. 3 was accomplished by training DOT personnel and earthwork/paving contractors 

on the IC technologies via field demonstrations and open house activities. Continuous support 

was provided to the TPF State DOTs for the development of local, customized IC specifications 

during the project period.

Goal No. 4 was accomplished by compiling a comprehensive list of recommendations for the 

IC roller vendors to further improve their systems for widespread use of the technologies. 

Various IC systems were reviewed in-depth and gaps were identified for future research and 

engineering practices.

Major Findings

Benefits of IC Technology

The immediate benefits of IC technologies common to both the earthwork and paving 

industries include:

• IC mapping of existing support layers is effective in identifying weak support areas for 

corrective actions prior to the compaction of the upper layers.

• With HMA IC, tracking roller passes and HMA surface temperatures provide necessary 

means to maintain a consistent rolling pattern within optimal ranges of temperatures for 

100 percent coverage of a construction area.

• IC technologies can be especially beneficial to maintain consistent rolling patterns under 

lower visibility conditions such as night paving operations.

• IC technology will have profound influence on the responsibilities of various stages of 

pavement constructions and will eventually help produce better and more consistent 

pavement products.

Soils IC

The major findings regarding soils and subbase IC include:

• Influence on measurements depths associated with various devices and in-situ moisture 

conditions are the main factors for correlation between ICMV and other in-situ point test 

devices. Other factors include different target material properties such as stiffness and 

density.

• A linear correlation was shown between ICMV and back-calculated layer stiffness from 

deflections measurements, e.g., light-weight deflectometer (LWD) and falling weightdeflectometer (FWD) tests.

• A less desirable correlation was found between ICMV and plate-loading-test-based 

stiffness, and between ICMV and California bearing ratio (CBR) values from dynamic 

cone penetrometer (DCP) tests.

• The least desirable correlation was found between ICMV and nuclear/non-nuclear density 

gauge measurements.
4

• ICMV is also influenced by factors such as machine settings (frequency, amplitude). 

Therefore, it is recommended that all measurements at calibration areas and production 

areas during QA should be obtained at a constant amplitude setting to avoid complication 

in data analysis and interpretation.

• Multi-linear correlation has revealed the significant influences of multiple factors on the 

ICMVs such as soils moisture contents and vibration amplitudes.

• Using the compaction curves of IC data on a test strip, optimal roller passes can be 

determined to prevent over/under compaction as long as the supports are relatively 

uniform.

• Semivariograms of IC data can be used to evaluate the compaction uniformity that can be 

crucial for long-lasting support and bearing capacity.

HMA IC

The major findings regarding HMA IC include:

• Mapping existing support materials using IC rollers prior to subsequent HMA paving is 

proved to be effective on identifying weak locations. Evidences from the IC field 

demonstration have shown that the desired densities of the HMA materials would be 

difficult to achieve at weak support locations and even premature failure may occur under 

construction traffic.

• Mapping has been successfully demonstrated on granular subbase, stabilized subbase, 

and milled asphalt surfaces with IC roller settings at low vibration amplitudes and low 

frequencies.

• The correlation between ICMV and HMA core densities is inconsistent due to factors 

such as limited spot tests and the different nature of measured properties (e.g.,

mechanical property vs. material proportioning).

• The correlation between ICMV and FWD/LWD-based deflections or moduli indicates a 

relatively higher linear correlation than that between ICMV and HMA core densities.

• The relatively low correlation between ICMV and nuclear/non-nuclear density gauge 

(NG/NNG) measurements can be due to: a) ICMV reflects the stiffness of the entire

pavement structure and the underlying support while NG/NNG only measure the top 6” 

of HMA layers; b) ICMVs do not yet factor in the temperature effects in the 

measurements while NG/NNG measurements are independent of HMA temperatures.

• Using mutli-linear regression has improved the correlation analysis between ICMVs and 

in-situ spot tests. These analyses indicated the influences of multiple factors on ICMVs 

including the machine settings (i.e. amplitude, vibration frequency), conditions of 

underlying layers (i.e. ICMV), and HMA temperatures.

• IC data can be used to build a compaction curve for a specific material of a specific 

project. The compaction curve can then be used to identify the optimal roller pass so that 

over/under compaction can be prevented.

• IC data can be used to produce semivariograms that serve as the metrics for compaction 

uniformity. Generally, compaction uniformity increases for subsequent lifts.

Recommendations for Future Research

• An improved (or even better, universal) ICMV model is recommended to decouple 
5

stiffness for each pavement layer, so that correlation of ICMV and in-situ spot 

measurements can be improved.

• For soil/subbase compaction, real time moisture mapping is recommended and an 

improved ICMV model would be needed to account for the effect of moisture.

• For HMA compaction, an improved ICMV model is recommended to account for HMA 

temperatures, and ICMVs are recommended to be reported with respect to a reference 

temperature (e.g., 68 °F).

• Multivariate technique is recommended to characterize each vendor’s ICMV in order to 

better understand the effects of machines and operation settings.

• A standardized IC data management and analysis tool is recommended to help speed up 

the acceptance of IC technologies by agencies.

• Long term pavement performance monitoring is recommended in order to identify 

performance trends that may relate to ICMV values and uniformity metrics.

Intelligent Compaction (IC) Road Map

An IC Road Map (Figure 2) was developed under this FHWA/TPF project to provide 

practical guidelines for IC implementation via the following four main tracks: 

• Track 1 — Equipment and Technologies

• Track 2 — Data Management and Integration

• Track 3 — Specifications

• Track 4 — Technology Transfer and Training

While the IC is still evolving and improving, this report provides a wealth of state-of-the-art 

IC knowledge for agencies and the industry to accelerate implementation of IC technologies.
6

Figure 2. IC Road Map.
7

Acknowledgement

This IC project is not possible if not for the funding and assistance from FHWA and the TPF 

State departments of transportation, IC roller vendors, GPS vendors, and earthwork/paving 

contractors of the seventeen demonstration projects. The list is too long to fit in this limited space, 

so that only a partial list is presented below.

Lee Gallivan of FHWA

Tom Harman of FHWA

Fred Faridizar of FHWA

Michael Arasteh of FHWA

Angela Fortson of FHWA

Ben Zaslow of FHWA

Jessica Granell of Georgia FHWA Division Office

Tom Duncan of Indiana FHWA Division Office

Lisa Rold of Iowa FHWA Division Office

Tom Deddens of Kansas FHWA Division Office

Azmat Hussain of Maryland FHWA Division Office

Bill Lohr of Minnesota FHWA Division Office

Roger McWilliams of Mississippi FHWA Division Office

Tim LaCoss of New York FHWA Division Office

Gary Goff of North Dakota FHWA Division Office

Zahur Siddiqui of Pennsylvania FHWA Division Office

Darrin Grenfell of Texas FHWA Division Office

Lorenzo Casanova of Virginia FHWA Division Office

Wes Shemwell of Wisconsin FHWA Division Office

Sheila Hines of Georgia DOT

Tommy Nantung of Indiana DOT

David Andrewski of Indiana DOT

James Brennan of Kansas DOT

Dan Sajedi of Maryland SHA

Tom Ravn of Minnesota DOT

Glenn Engstrom of Minnesota DOT

Richard Sheffield of Mississippi DOT

Randy Battey of Mississippi DOT

Don Dwyer of New York DOT
8

Zoeb Zavery of New York DOT

Jon Ketterling of North Dakota DOT

Thomas Bold of North Dakota DOT

Kerry Petrasic of Pennsylvania DOT

German Claros of Texas DOT

Zhiming Si of Texas DOT

Brian Diefenderfer of Virginia DOT

William (Bill) Maupin of Virginia DOT

Robert Arndorfer of Wisconsin DOT

Winfried Schramm of Bomag

Chris Connolly of Bomag Americas, Inc.

Dave Dennison of Bomag Americas, Inc.

Hans-Josef Kloubert of Bomag

Gary Corley of Bomag Americas, Inc.

Bill Stalzer of Bomag Americas

George Whitaker of Case Construction Equipment

Kirby Carpenter of Texana Machinery/Case

Kuno Kaufmann of Ammann Compaction Ltd. (Switzerland)

Nick Oetken of Caterpillar

Allen DeClerk of Caterpillar

Mario Souraty of Caterpillar

Mark Tarvin of Caterpillar

Steve McCown of Caterpillar

Mark Tarvin of Caterpillar

Kevin Adams of Caterpillar Global Paving

Dann Rawls of Caterpillar Global Paving

Mike Prichard of Dynapac USA, Inc.

Gert Hansson of Dynapac USA, Inc.

Fredrik Akesson of Dynapac (Sweden)

Dale Starry of Volvo

Todd Mansell of Sakai America Inc.

Yuki Tsukimoto of Sakai Heavy Industry Ltd (Japan)

Kei Uchiyama of Sakai Heavy Industry Ltd (Japan)

Wayne R. Evans of Wirtgen America/HAMM

Andy Sprado of Zielger (Trimble retrofit system)
9

Ron Oberlander of TopCon Positioning Systems, Inc.

Pete Kaz of Trimble

Jeff Drake of Trimble

Bruce Hanes of Trimble

Luke Magnuson of Trimble
10

Symbols and Terminology

a Theoretical vibration amplitude (eccentric moment divided by the drum mass)

AΩ Acceleration at fundamental frequency

a* Actual measured vibration amplitude (double integral of acceleration data)

A’ Machine acceleration

AFC Automatic feedback control

AXΩ Acceleration at X-order harmonic

B Contact width of the drum 

b machine internal loss coefficient used in MDP calculation

b0 Intercept in a linear regression equation

b1, b2, b3 Regression coefficients 

BCD Briaud compaction device

C Semivariogram scale

C+C0 Semivariogram sill

C0 Semivariogram nugget

CBR California bearing ratio

CBR300 Weighted average CBR to a depth of 300 mm

CBRBase Weighted average CBR to a depth equal to the depth of the base material

CCC Continuous compaction control

CCV compaction control value (by Sakai)

CMV Compaction meter value

COV Coefficient of variation (calculated as the ratio of mean and standard deviation)

d0 measured settlement under plate

D10 Particle size corresponding to 10% passing

D30 Particle size corresponding to 30% passing

D60 Particle size corresponding to 60% passing

DPI Dynamic cone penetration index

E Elastic modulus

EBCD Modulus determined from Briaud Compaction Device (BCD)

EFWD-K3 Elastic modulus determined from 300-mm plate KUAB falling weight 

deflectometer

ELWD-Z2 Elastic modulus determined from 200-mm plate Zorn light weight deflectometer

ELWD-Z3 Elastic modulus determined from 300-mm plate Zorn light weight deflectometer
11

EV1 Initial modulus from 300-mm diameter static plate load test

EV2 Reload modulus from 300-mm diameter static plate load test

EVIB Vibratory modulus determined from the roller (by Bomag)

F Shape factor

f Vibration frequency

Fs Drum force

FWD Falling weight deflectometer

g Acceleration of gravity

GPS Global positioning system

Gs Specific gravity

h Separation distance

ICMV Intelligent compaction measurement value

ks Roller-integrated stiffness (also expressed as kb, by Ammann/Case)

LL Liquid limit

LWD Light weight deflectometer

m Machine internal loss coefficient used in MDP calculation

md drum mass

MDP Machine drive power (by Caterpillar)

MDP40 See description in text

MDP80 See description in text

mere Eccentric moment of the unbalanced mass

n Number of test measurements

p Number of regression parameters

Pg Gross power needed to move the machine 

PI Plasticity index

PL Plastic limit

PLT Static Plate Load Test

r Radius of the plate

R Semivariogram range

R’ Radius of the roller drum

R2 Coefficient of determination

R2

(adjusted) Adjusted coefficient of determination (adjusted for number of parameters 

in multiple regression analysis)

v Roller velocity

W Roller weight 
12

w(H) Moisture content determined from Humboldt nuclear gauge

w(SDG) Moisture content determined from Transtech’s Soil Density Gauge (SDG)

w(T) Moisture content determined from Troxler nuclear gauge

wopt Optimum moisture content 

zd Drum displacement

α Slope angle (roller pitch from a sensor)

η Poisson’s ratio

φ Phase angle

γd(H) Dry unit weight determined from Humboldt nuclear gauge (NG)

γd(SDG) Dry unit weight determined from Transtech’s Soil Density Gauge (SDG)

γd(T) Dry unit weight determined from Troxler nuclear gauge (NG)

γdmax Maximum dry unit weight 

μ Statistical mean

σ Statistical standard deviation
13

Chapter 1 Introduction

Background

Compaction is one of the most important processes in roadway construction. It is needed to 

achieve high quality and uniformity of pavement materials, which in turn better ensure longlasting performance. Pavement materials often possess optimum densities that ensure adequate 

support, stability, and strength. Achieving the uniformity is a key for successful compaction. 

Generally, in-situ spot tests (with nuclear or non-nuclear gauge density devices) or cores tests are 

required for the quality control (QC) and/or quality assurance (QA). However, there are many 

issues associated with this conventional density control method, including but not limited to: 1) 

In-situ spot tests or cores are limited and often conducted at random locations, and thus those 

tests are not necessarily representative for the entire pavement area; 2) there may be weak or 

unqualified compaction areas unidentified by the limited spot tests; and 3) the measured density 

of top bound layer is limited to indicate the structural capacity of the entire pavement layers. As 

a result, non-uniform and unsatisfactory compaction may be outcomes, leading to premature 

failure and worse long-term life performance. Therefore, intelligent compaction (IC) has been 

developed to address these issues.

IC is a compaction technology used for materials including soils, aggregates, and asphalt 

mixtures, by using vibratory rollers equipped with the real-time kinematic (RTK) Global 

Positioning System (GPS), roller-integrated measurement system (normally accelerometer-based), 

feedback controls, and onboard real-time display of all IC measurements. IC rollers maintain a 

continuous record of measurements that include the number of roller passes, roller-integrated 

measurement value (ICMV), GPS locations of the roller, roller vibration amplitudes/frequencies, 

and HMA surface temperatures, etc. Based on the real time onboard color-coded display of the 

above measurements, roller operators can either manually or allow the IC rollers to automatically 

adjust the machine settings for optimum compaction. ICMV, while different from one vendor to 

another, is used to evaluate the level of compaction. With 100 percent coverage on the 

compacted area, the IC technology can be used to produce uniformly compacted pavement 

products that perform better and last longer.

While being implemented in Europe and Japan for years, the IC technologies have been 

introduced to the US recently (White et al. 2007). Therefore, there is still a lack of experiences, 

knowledge, and availability of IC equipment in the US. Meanwhile, the IC technology is still 

evolving, especially for the HMA compaction (Scherocman et al. 2007). This combination has 

resulted in difficulty in implementing IC by State Departments of Transportation (DOTs) and 

paving contractors.

With the above needs in mind, the Federal Highway Administration (FHWA) and transportation 

pooled fund (TPF) project, (TPF-5(128)), “Implementation of Intelligent Compaction Technology 

for Embankment Subgrade Soils, Aggregate Base, And Asphalt Pavement Materials” was 

initiated in 2007 to realize the FHWA’s IC strategic plan. This report includes the study for the 

FHWA/TPF IC demonstration projects conducted in 12 states: Georgia, Indiana, Kansas, 

Maryland, Minnesota, Mississippi, North Dakota, New York, Pennsylvania, Texas, Virginia, and 

Wisconsin. The purpose of this project is to accelerate the understanding and implementation of 

IC technology. This will be accomplished through a coordinated effort by both IC roller 

manufacturers and government agencies on various construction projects around the country.

IC is applicable to virtually all materials that require compaction during construction. In this 

project, the following material types are covered:
14

• Type I – Granular, non-cohesive subgrade soils

• Type II – Fine-grained, cohesive subgrade soils

• Type III – Aggregate base material

• Type IV – Asphalt pavement material

• Type V – Stabilized subbase material

The objectives of this project were short-term goals for introducing soil/subbase and HMA IC 

technology to DOTs and contractors who may not have prior experience with IC. The project 

was intended to demonstrate the benefits of IC for improving the compaction process by 

achieving more uniform density/modulus of HMA pavement and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality 

control.

Report Structure

This report is structured as follows: 

• Chapter 1 - Introduction: It provides an overview of this report.

• Chapter 2 - IC Roller Systems: It contains a detailed description of IC roller systems 

(including single drum and double drum rollers), GPS requirements, IC measurement 

types and associated correlation studies, verification of various IC models, and a 

proposed new IC model.

• Chapter 3 – Field Spot Tests for IC Implementation: It describes field spot tests 

recommended as verification tests for IC implementation for both soils/subbase and 

HMA.

• Chapter 4 - IC Analysis and Report: It tackles one of the critical steps for implementing 

IC by providing detailed guidelines for IC data storage/exchange, viewing of IC data, 

basic statistics and geostatistics analyses, developing compaction curves and performing 

univariate/multivariate correlation analyses.

• Chapter 5 - Demonstration Projects Overview: Field demonstration projects are the major 

work under the FHWA/TPF IC study. This chapter provides an overview for the field 

demonstration projects including demonstration activities (location, test bed, roller 

system, in-situ tests, etc.), and recommendations.

• Chapter 6 - Demonstration Projects for Soils IC: It contains the summary of soils/subbase 

field demonstration projects on diverse materials/structures/road types conducted under 

the FHWA/TPF IC project that serves as a rich knowledge base for soil IC 

implementation.

• Chapter 7 - Demonstration Projects for HMA IC: It contains the summary of hot mixture 

asphalt field demonstration projects on diverse materials/structures/road types conducted 

under the FHWA/TPF IC project that serves as a valuable knowledge base for HMA IC 

implementation.

• Chapter 8 - Guidelines for IC Implementation: It consists of a concise, clear road map for 

IC implementation, and guidelines for developing IC implementation plans, performing 

IC data management, developing IC specifications for both soils/subbase and HMA, and 

conducting IC training and workshops.

• Chapter 9 -Conclusions and Recommendations: It contains key conclusions from this IC 
15

study and recommendations for future IC improvements and research.

• References

• Appendix A - Understand Semivariogram

• Appendix B - Understand GPS

• Appendix C - How to Setup GPS for IC

• Appendix D - Generic Soils IC Specifications

• Appendix E - Generic HMA IC Specifications

• Appendix F - Generic Subbase IC Specifications
16

Chapter 2 IC Roller Systems

IC Rollers

Intelligent Compaction refers to the compaction of road materials, such as soils, aggregate bases, 

or asphalt pavement materials, using modern vibratory rollers equipped with an in-situ 

measurement system and feedback control. Global Positioning System (GPS) based mapping is 

included, as well as software that automates documentation of the results. By integrating 

measurement, documentation, and control systems, the IC rollers allow for real-time monitoring 

and correction of the compaction process. IC rollers also maintain a continuous record of 

(nominally) color-coded plots that indicate the number of roller passes, roller-generated material 

stiffness measurements, and precise location of the roller. An example of such IC roller system is 

illustrated in Figure 3.

(Courtesy of Bomag)

Figure 3. Bomag VarioControl System

How IC Rollers Work

IC rollers utilize the framework of a vibratory roller. IC rollers, if come with auto-feedback 

systems, are equipped with instrumentation which feeds information documentation and feedback 

control systems which process the data in real time for the roller operator to monitor. An 

example of such IC feedback system is illustrated in Figure 4.
17

Exciter Position

Phase Angle

Electronic

Device

Hydraulic Pump Valve

Differential Gear Box

Drum Acceleration

f opt.

Drum Acceleration

Exciter Position

f

Excitation

Display & Operation

Automatic Closed-Loop Control

A

Control

Unit

Excentricity %

Accelerationsensor

Rotationsensor

Sensors

(Courtesy of Case/Ammann)

Figure 4. Case/Ammann auto-feedback control (AFC) system.

The precise location of the roller, speed, and number of passes over a given location are mapped 

using GPS. These systems are commonly used to establish grade and to control other pieces of 

equipment.

Compaction meters or accelerometers are mounted in or about the drum to monitor applied 

compaction effort, frequency, and response from the material being compacted. The readings 

from this instrumentation determine the effectiveness of the compaction process. The 

methodology to calculate material response to compaction is often proprietary, resulting in 

various types of intelligent compaction measurement values (ICMV).

A calibration procedure is often used to correlate the ICMV to a material modulus or density 

measured by other (in-situ) test devices. Compaction curves from ICMVs and in-situ test results 

can be established to indicate the target ICMV and optimum roller passes (see an example in 

Figure 5).
18

Pass

0 2 4 6 8 10

Average ELWD-Z2 (MPa)

6

9

12

15

18

0 2 4 6 8 10

Average 

kSIPD (MN/m)

8

10

12

14

16

18

20

 

Section 1 - Dry 

Section 2 - Optimum

Section 3 - Wet

 

 

Figure 5. Calibration test – with compaction growth curves for Case/Ammann 

ICMV (kSΙPD) and in-situ point measurements (ICPF TXDOT demo).

For asphalt IC rollers, additional temperature instrumentation is used to monitor the surface 

temperature of the asphalt pavement material. This is critical as vibratory compaction within 

certain temperature ranges (such as too-cold-to-compact temperatures or tender zones for 

Superpave mixtures) can have adverse effects. An example of IC temperature instrumentation is 

illustrated in Figure 6 to measure asphalt surface temperatures by using an infrared sensor. There 

are on-going researches on measurement of asphalt internal temperature using embedded measure 

devices or “tags”. However, those devices are available for widespread use at this time.
19

Figure 6. Infra-red temperature sensor on a Sakai IC roller.

Single Drum IC Rollers

IC rollers used to compact natural subgrade and aggregate materials use a single drum, as shown 

in Figure 7 for the participating vendors under this study. The drums can be smooth or fitted with 

a padfoot shell kit. The Sakai single drum IC roller system, instead, is a padfoot with smooth 

drum shell kit. Being a more straightforward form of IC, there is currently more experience, 

available rollers, and construction history with soil and aggregate IC.

Bomag America

Caterpillar Caterpillar

Ammann/Case Dynapac Dynapac

Sakai America

Figure 7. Single smooth drum IC rollers.
20

A summary of available IC rollers for soil materials is presented below in Table 1:

Table 1. Summary for Single Drum IC Rollers

Vendor Ammann/Case Bomag Caterpillar Dynapac Sakai

Model ACEplus VarioControl NA DCA-S (GPS) CIS

Model Number SV BW213-4BVC NA CA 152–702 SV505/SV510

Auto-Feedback Y Y Y Y N

With measurement 

System Y Y Y Y Y

Measurement 

Value Kb Evib CMV CMV CCV

Measurement Unit MN/m MN/m2 Unitless Unitless Unitless

GPS Capability Yes Y Y Y Y

Documentation 

System ACEPlus

BCM 05 Office 

and Mobile AccuGrade DCA AithonMT-R

Development 

Status In production In production In production In production In production

Availability Current Current Current Current Current

• ACEplus: Ammann Compaction Expert-Plus

• DCA-S (GPS): Dynamic Compaction Analyzer for Soil with GPS

• Kb: Stiffness or ground bearing capacity (as related to the plate loading tests)

• Evib: Vibration modulus

• CMV: Compaction Meter Value

• CIS: Sakai Compaction Information System

• CCV: Sakai Compaction Control Value

• Availability: Refers to the availability in the US

Double Drum IC Rollers

IC rollers used to compact asphalt pavement materials use a dual-drum configuration, as shown in 

Figure 8 for the participating vendors under this study. Monitoring and automating feedback 

controls for two vibratory drums add complexity to the IC process for asphalt pavement materials. 

In addition, timing of rolling and mat temperature are critical when compacting asphalt pavement 

materials. For this reason, additional instrumentation and considerations are necessary when 

utilizing IC technology for these materials.
21

Bomag America

Caterpillar Caterpillar

Ammann/Case Dynapac Dynapac

Sakai America

Figure 8. Double drum IC rollers.

A summary of IC rollers for asphalt materials is presented below in Table 2:

Table 2. Summary for Asphalt IC Rollers

Vendor Ammann/Case Bomag Caterpillar Dynapac Sakai

Model NA Asphalt Manager

AccuGrade 

Compaction DCA-A CIS

Model Number NA BW190AD-4AM

CB534D,CB534DXW,CB564D CC 224 etcSW850/SW880

/SW890

Auto-Feedback NA Y N N N

With measurement 

System NA Y

Temperature and 

Pass Count N Y

Measurement 

Value NA Evib Temperature NA CCV

Measurement Unit NA MN/m^2 °C NA Unitless

GPS Capability NA Y Y Y Y

Documentation 

System NA

BCM 05 Office 

and Mobile AccuGrade DCA AithonMT-A

Development 

Status

Under 

development In production

Available as 

special order In production In production

Availability Future Current special order Future Current

• Evib: Vibration modulus

• CMV: Compaction Meter Value

• CCV: Compaction Control Value

• DCA-A: Dynamic Compaction Analyzer for Asphalt
22

• Dynapac CC 224 Etc.: The models include: CC 224-624, PTR's, CP 224, and 274.

• Availability: Refers to the availability in the US

GPS Requirements and Setup

It cannot be stressed enough for the GPS data collection in IC implementation. To ensure 

accurate and consistent data collection, the following capabilities for the roller GPS systems are 

required:

• RTK GPS (Real-Time Kinematic GPS) systems on IC rollers (see Figure 9 for the GPS

receiver setup on a Sakai roller),

• System reports and records values in Northing and Easting and vertical position in meters

in UTM coordinates (though State coordinates and county coordinates are commonly 

used in the US) for the project site, and

• If an offset is necessary between GPS antenna and center of front drum it must have been 

input and validated.

Technical assistance by roller vendors or GPS equipment manufacturers is often recommended:

• On-site staff with sufficient technical knowledge to set up roller-mounted GPS equipment 

and provide input for equipment operation during the first day of the field operation. 

• Contact information for personnel with sufficient technical knowledge to assist the 

research team with technical questions during field testing when on-site technical 

assistance is not available.

Use of a GPS base station radio operating at 900MHz is recommended (see Figure 13) from 

vendors such as Trimble, TopCon, Leica, etc. In addition to setting up GPS base stations, there 

can also be other options such as virtual reference station (VRS) and internet-based correction 

signals. Prior to the beginning of IC data collection during the compaction operation, the GPS 

setup must be validated using a survey grade handheld GPS “rover” (e.g., placed on top of the 

roller-mounted GPS receiver, see Figure 14) unit to ensure that the roller-mounted GPS is 

providing accurate positioning data.

Figure 9. RTK GPS receiver and antenna on a Sakai roller (ICPF MnDOT demo).
23

GPS Precision

There are four types of GPS position data with various precisions: Autonomous (10 to 15 m 

horizontal); DGPS (0.5 to 4 m horizontal); Float (1 meter to sub-meter); and Fixed (1 to 3 cm), 

where the fixed GPS is recommended to be used with IC.

Real-time kinematic (RTK) GPS system, a “fixed” type of GPS, is recommended for use on 

moving IC rollers or hand-held rovers to measure locations in real time with high accuracy 

(within 1 to 3 cm). This high level of precision is achieved by receiving correction signals 

transmitting from one (single station RTK) or multiple (network RTK) onsite GPS base stations 

or virtual reference stations (VRS).

The effective range of a GPS station, depending on position of setup and its antenna, can be 

approximately 2 miles in radius. Currently, only the horizontal precision is of major interest for 

the IC application. However, vertical precision is crucial for applications such as intelligent 

construction using systems such as the Trimble Total Station system.

Even with GPS base stations or VRS, the GPS precision on an IC roller may degrade to “float” or 

even “autonomous” when the radio line of sight between the base station and the GPS receivers is 

blocked by obstacles such as hills, horizontal/vertical curves, trees, etc.

(courtesy of Laica)

Figure 10. RTK using a Handheld rover/receiver with a single base/reference station.
24

(courtesy of Laica)

Figure 11. RTK using a Handheld rover/receiver with a reference network.

Datum Coordinate System

Different GPS coordinate references may be used in the construction, including the geographic 

(degree-minute-second), the state plane, the county plane, and UTM (east and north, meter/feet). 

The UTM zone system is recommended to be used in the IC systems.

The UTM (Universal Transverse Mercator) coordinate system zone is designated when the UTM 

grids are produced based on the geodetic GPS data, longitudes and latitudes. The conversion is 

based on The World Geodetic System 84 (WGS84) and North American Datum of 1983 

(NAD83). See Figure 12 for the UTM zones in the US. Users can normally select the desired 

UTM zone in the settings of a vendor’s IC field software program.
25

Figure 12. UTM Zones in the US.

GPS Equipment Setup for IC

The Real-Time Kinematic (RTK) Global Position Systems (GPS) can be used for IC rollers. The 

GPS receiver and radio/antenna are installed on the top of the IC machine which may or may not 

position to the center of the front or rear drums (if not, an offset needs to be applied). The GPS 

receiver collects signals from both the GPS base station and satellites. With the connection to the 

GPS station, the GPS position is the “fixed” mode which may result in 1-3 cm precision. 

Otherwise, it is in a “float” mode with a precision of meter or sub-meter. The precise roller 

position for recording the GPS coordinates (e.g. geographic and UTM) and roller pass data are 

recorded to the computer system.

To establish an RTK fix in a state plane, UTM, or a local coordinate system, the base station must 

be started on a known position with established NEZ coordinates. A typical setup procedure is as 

follows (courtesy of TopCon):

• Use a network rover and dial up a specific State reference station network. Set 

up GPS software and apply the desired datum (e.g., UTM) for a projection. 

Set up a tripod, and install the receiver on top of the tripod. Measure the point 

using a 2-minute occupation time and save it as the base station location.

• Set up the GPS geodetic receiver on the tripod and start the base station using 

the measured coordinates to broadcast the NEZ coordinates of the selected 

datum (e.g., UTM) for that location. If the NEZ values are not being 

broadcasted from the base, the GPS data can have a horizontal error up to 12 

to 15 feet. This is called an autonomous start, which only broadcasts the 

Latitude and Longitude of the base station and does not apply the datum (e.g., 

UTM) correction.
26

• Caution: If one base station is started autonomously and then restarted with 

the UTM correction applied, the measurement of the same point will have 

different NEZ values, depending on the UTM grid factor that is applied by the 

software.

• While the use of an autonomous start provides relative reference points, the 

use of a state plane or UTM base station start is strongly recommended to 

ensure GPS measurements at any time (e.g., after the project is complete) can 

be at the exact locations.

Figure 13. A Trimble GPS base station (ICPF MnDOT demo).

Figure 14. Validation of roller mounted GPS with a hand-held rover.
27

IC Measurement Types

Introduction

Development and evaluation of continuous compaction control (CCC) measurement technologies 

was initiated over three decades ago in Europe for use on vibratory rollers compacting granular 

soils (Forssblad 1980 and Thurner and Sandström 1980). Since its inception, the concept has been 

expanded to different measurement technologies and materials and is available commercially for 

different roller configurations. For vibratory roller configurations, CCC involves measurement 

and analysis of output from an accelerometer mounted to the roller drum and can provide a spatial 

record of compaction quality when linked to position measurements and a documentation system.

Figure 15. Overview of ICMV compaction monitoring systems.

When the measurement system provides automatic feedback control (AFC) for roller vibration 

amplitude and/or frequency, it is referred to as “intelligent” compaction (IC). Roller 

measurement values calculated based on accelerometer measurements use one of two different 

approaches: 

• calculate a ratio of selected frequency harmonics for a set time interval, or 

• calculate ground stiffness or elastic modulus based on a drum-ground interaction model

and some assumptions.

An alternative to accelerometer-based vibratory measurements is the measurement of rolling 

resistance/machine drive power which can be applied to both vibratory and non-vibratory roller 

operations. Regardless of the technology, the premise of ICMV is that the measurement values 

are related to traditional compaction measurements and will be useful as part of effective 

earthwork or pavement compaction operations and QC/QA practices. The purpose of this section 

is to provide an overview of the literature in areas of: 

• Developmental history of ICMV;

• ICMV technologies, manufacturers, and documentation systems; 
28

• Factors influencing the ICMV measurements; 

• Field correlation studies and factors influencing the correlations; and 

• Specification attributes and concepts.

Model testing, discussions of disadvantages of existing models, and recommendation for future 

model development will also be presented.

Development of IC Measurements

The concept of ICMV was first investigated by Dr. Heinz Thurner of the Swedish Road 

Administration in 1974 by relating drum harmonics to soil compaction properties. Field trials 

were conducted by Dr. Thurner by instrumenting Dynapac vibratory smooth drum rollers using 

accelerometers on granular soils. Results from these field trials provided evidence that the ratio of 

the acceleration amplitude of the first harmonic and the acceleration amplitude of the 

fundamental frequency of the vibrating drum was an indicator of soil stiffness/modulus. In 1975, 

Dr. Thurner partnered with Åke Sandström and founded Geodynamik to continue research and 

development on ICMV. In 1976, CompactometerTM Compaction Meter Value (CMV) 

measurement was developed by Geodynamik in cooperation with the Dynapac Research 

Department. In 1980, five technical articles were presented on the CMV measurement 

technology and its applications (Thurner 1980, Thurner and Sandström 1980, Forssblad 1980, 

Hansbo and Pramborg 1980, and Machet 1980) at the First International Conference on 

Compaction held in Paris.

In 1983, Geodynamik introduced the Oscillometer Value (OMV) for oscillatory rollers which is a 

dimensionless value obtained from the amplitude of the horizontal acceleration of the drum. 

HAMM AG adopted the OMV measurement technology (Thurner and Sandström 2000) for use 

on their smooth drum oscillatory rollers, but virtually no published information is available in the 

English literature on OMV relationships with soil properties.

In the early 1980s, BOMAG developed the Terrameter® system measuring the Omega value 

(BTM 1983). The Omega value provides a measure of the compaction energy transmitted to the 

soil using accelerometer data. Hoover (1985) published a research report from a field study 

evaluating the Omega value on three different types of granular soils which showed encouraging 

results. Later in 2000, BOMAG replaced the Omega value by introducing the Vibratory Modulus 

(EVIB) value which uses acceleration data to determine drum displacement, an estimated applied 

force, and a dynamic roller-soil model (Kröber et al. 2001).

In the late 1990s, Ammann introduced the roller-integrated stiffness (kb) measurement value, 

which provides a measure of quasi-static stiffness using the measured drum displacement, 

estimated applied force, and a spring-dashpot model representing roller-soil interaction 

(Anderegg and Kauffmann 2004).

Currently, Dynapac, Trimble, and Caterpillar use the CMV measurement technology as part of 

their ICMV systems by linking CMV data with GPS measurements for on-board real time display. 

Trimble offers a retrofit CCC system for smooth drum vibratory rollers (White and Vennapusa 

2009). In 2004, Sakai introduced Compaction Control Value (CCV) which is also a 

dimensionless parameter similar to CMV (Scherocman et al. 2007), but considers both the 

fundamental and sub-harmonic frequencies for determining CCV.

In 2003, a research collaboration project between the Iowa Department of Transportation, Federal 

Highway Administration (FHWA), and Caterpillar was initiated to evaluate Caterpillar’s Machine 

Drive Power (MDP) system for use on granular and cohesive soils. The MDP system is based on 

the principle of rolling resistance due to drum sinkage, and the approach works in both the 
29

vibratory and static modes. The measurement system has been investigated in field trials since 

2003 (Tehrani and Meehan 2009, White et al. 2004, 2005, White and Thompson 2008, Thompson 

and White 2008) and has recently been used on a full-scale earthwork compaction project in 

Minnesota (White et al. 2009a, b).

Currently, Bomag, Ammann, and Dynapac offer AFC/IC systems, wherein the vibration 

amplitude, and/or frequency are automatically adjusted when drum jumping is determined or 

when a preset threshold roller measurement value is reached. Some of the potential advantages 

cited in the literature (e.g., Adam and Kopf 2004) for using AFC for soil compaction are

increased chance of more rapid compaction (i.e., less passes) and improved uniformity of soil 

properties. Based on this literature review, these benefits are not well quantified in the technical 

literature.

IC Measurement Types

Currently, at least six manufacturers (Ammann, Bomag, Case/Ammann, Caterpillar, Dynapac, 

and Sakai) offer ICMV on their machines (note that Case uses Ammann ICMV technology on 

their rollers). All the manufacturers employ proprietary data filtering, recording, and display 

methods using proprietary software. A summary of key features from each manufacturer is 

presented in Table 3. Figure 16 to Figure 21 show different roller configurations and display 

software by different manufacturers. 
30

Table 3. Key features of different ICMV systems

Feature

Ammann/ 

Case Bomag Caterpillar Dynapac Sakai Trimble

ICMV Value ks (MN/m) EVIB 

(MPa)

MDP40 (shown 

as CCV in the 

output) and 

CMV

CMV CCV CMV

Single Drum 

Configuratio

n

Padfoot, 

Smooth 

Drum

Smooth 

Drum

Padfoot and 

Smooth Drum

Padfoot 

and 

Smooth 

Drum

Smooth 

Drum

(Retrofit) 

Smooth Drum

Display 

Software

ACE-Plus 

®

BCM05

® AccuGrade® DCA® Aithon 

MT® AccuGrade®

Output

Documentati

on

Date/Time, 

Location 

(Latitude/ 

Longitude/ 

Elevation), 

Machine 

length/widt

h, 

Direction 

(forward/ 

backward), 

Vibration 

(On/Off), 

Stiffness 

(ks), 

Amplitude 

(actual), 

Speed, 

Frequency

Date/Tim

e, 

Location 

(Northing

/ Easting/ 

Elevation 

at center 

of the 

roller 

drum), 

EVIB, 

Frequenc

y, 

Amplitud

e 

(actual), 

Speed, 

Jump

Date/Time, 

Location 

(Northing/Easti

ng/ Elevation of 

left and right 

ends of the 

roller drum), 

Speed, CCV, 

CMV, ICMV, 

Frequency, 

Amplitude, 

Direction 

(forward/ 

backward), 

Vibration 

(On/Off)

Location 

(Latitude

/ 

Longitud

e/ 

Elevation

), 

Direction 

(forward/ 

backward

), CMV, 

Bouncing

, 

Frequenc

y, Speed, 

Amplitud

e

Date/Time, 

Location 

(Northing/ 

Easting/ 

Elevation), 

CCV, 

Temperatur

e, 

Frequency, 

Direction 

(forward/ 

backward), 

Vibration 

(On/Off), 

GPS 

Quality

Date/Time, 

Location 

(Northing/Easti

ng/ Elevation of 

left and right 

ends of the 

roller drum), 

Speed, CCV, 

CMV, ICMV, 

Frequency, 

Amplitude, 

Direction 

(forward/ 

backward), 

Vibration 

(On/Off)

Output 

Export File *.txt *.csv *.csv *.txt *.csv *.csv

Automatic 

Feedback 

Control 

(AFC)

Yes Yes No Yes No No
31

Figure 16. Ammann and Case rollers (padfoot and smooth drum) equipped with 

roller-integrated ks measurement system on-board display units with ACE-Plus®

software.

 

Figure 17. A single-drum Bomag roller equipped with roller-integrated EVIB

measurement system on-board display units with BCM-05® software. 
32

 

Figure 18. Caterpillar rollers (padfoot and smooth drum) equipped with rollerintegrated MDP and CMV measurement systems on-board display unit with 

AcuGrade® software. 

Figure 19. A single-drum Dynapac roller equipped with roller-integrated CMV 

measurement system and on-board display unit with DCA® software.
33

 

 

Figure 20. Sakai rollers (padfoot and smooth drum) equipped with roller-integrated 

CCV measurement system and on-board display unit with Aithon-MT® software.

Figure 21. Trimble CB430 on-board display unit with roller-integrated CMV 

measurement system.
34

The commonly used IC measurement values (ICMV) in the US are summarized in Table 4. 

These measurements are either based on vibration frequency analysis or mechanical modeling. 

More details on those IC measurements are described in the following sections.

Table 4 Summary of IC Measurements

IC Measurements Units IC Systems Model Definition

Compaction Meter 

Value - CMV

None Caterpillar, 

Dynapac

Ω

Ω = A

A C 2 CMV

Machine Drive 

Power - MDP

None Caterpillar

(mv b)

g

A' MDP Pg Wv Sin − + 













 = − α +

Compaction Control 

values - CCV

None Sakai

CCV 100

0.5

0.5 1.5 2 2.5 3

×



 





+

+ + + + =

Ω Ω

Ω Ω Ω Ω Ω

A A

A A A A A

Stiffness - Kb

MN/m Ammann/Case





 



 = +

d

b d

z

m e

m cos( ) k 2 0 0 φ ω

Vibration Modulus -

Evib

MN/m2 Bomag

Compaction Meter Value (CMV)

Compaction meter value (CMV) is a dimensionless compaction parameter developed by 

Geodynamik that depends on roller dimensions, (i.e., drum diameter and weight) and roller 

operation parameters (e.g., frequency, amplitude, speed), and is determined using the dynamic 

roller response (Sandström 1994). The concept of development of different harmonic 

components of drum vibration with increasing ground stiffness is illustrated in (Figure 22). CMV 

is calculated using Equation 1, where C is a constant (i.e. 300), A2Ω = the acceleration of the first 

harmonic component of the vibration, and AΩ = the acceleration of the fundamental component of 

the vibration (Sandström and Pettersson 2004).

Ω

Ω = ⋅

A

A CMV C 2

(1)

The Geodynamik system also measures the resonant meter value (ICMV) which provides an 

indication of the drum behavior (e.g. continuous contact, partial uplift, double jump, rocking 

motion, and chaotic motion) and is calculated using Equation 2, where A0.5Ω = subharmonic 

acceleration amplitude caused by jumping (the drum skips every other cycle). Dynapac reports 

the value as bouncing value (BV). It is important to note that the drum behavior affects the CMV 

measurements (Brandl and Adam 1997) and therefore must be interpreted in conjunction with the 

ICMV or BV measurements (Vennapusa et al. 2010). 

Ω

Ω = ⋅ A

A RMV or BV C 0.5

(2)
35

Dynapac uses a preselected threshold BV as an indicator of roller jumping to adjust the amplitude 

in AFC mode compaction. Similarly, Caterpillar uses ICMV to adjust amplitude on a smooth 

drum vibratory roller used on a project (White et al. 2008b).

Figure 22. Illustration of changes in drum harmonics with increasing ground 

stiffness (modified from Thurner and Sandström 1980). 

It was found that CMV increases monotonously with the stiffness of soil. Caterpillar, Dynapac, 

and Volvo/Ingersoll Rand IC systems have made use of the Geodynamic CMV.

Machine Drive Power (MDP)

Machine drive power (MDP) technology relates mechanical performance of the roller during 

compaction to the properties of the compacted soil. MDP is calculated as follows:

(mv b)

g

A' MDP P Wv Sin g − + 













 = − α + (3)

where MDP = machine drive power (kJ/s), Pg = gross power needed to move the machine (kJ/s), 

W = roller weight (kN), A’ = machine acceleration (m/s2

), g = acceleration of gravity (m/s2), α = 

slope angle (roller pitch from a sensor), v = roller velocity (m/s), and m (kJ/m) and b (kJ/s) = 

machine internal loss coefficients specific to a particular machine (White et al. 2005). MDP is a 

relative value referencing the material properties of the calibration surface, which is generally a 

hard compacted surface (MDP = 0 kJ/s). Positive MDP values therefore indicate material that is 

less compact than the calibration surface, while negative MDP values indicate material that is 

more compacted than the calibration surface (i.e. less roller drum sinkage).

In a recent field study documented by White et al. (2009b) and field studies conducted as part of 

this pooled fund research project (Kansas and Mississippi field project from this research study), 

the MDP values are referred to as MDP80 or MDP40 depending on the modified settings. These 

modified values were recalculated to range between 1 and 150 as follows: 

MDP 150 1.37(MDP) 80 = − (4)
36

MDP 150 2.75(MDP) 40 = − (5)

For MDP80 calculation, the calibration surface with MDP = 0 kJ/s was scaled to MDP80 = 150, 

and a soft surface with MDP = 108.47 kJ/s (80000 lb-ft/s) was scaled to MDP80 = 1. For MDP40 

calculation, the calibration surface with MDP = 0 kJ/s was scaled to MDP40 = 150 and a soft 

surface with MDP = 54.23 kJ/s (40000 lb-ft/s) was scaled to MDP40 = 1.

Compaction Control Value (CCV)

The Sakai CCV is a relative stiffness index determined from the measured acceleration data based 

on the harmonic frequency. Sakai IC rollers make use of an accelerometer mounted to the roller 

drum to create a record of machine-ground interaction data. The concept behind the CCV is that 

as the ground stiffness increases, the roller drum starts to enter into a “jumping” motion which 

results in vibration accelerations at various frequency components, as illustrated in Figure 23. 

The CCV is computed based on the equation below.

100

A A

A A A A A CCV

0.5

0.5 1.5 2 2.5 3

 ×



 





+

+ + + + =

Ω Ω

Ω Ω Ω Ω Ω (6)

Figure 23. Changes in amplitude spectrum with increasing ground stiffness 

(modified from Scherocman et al. 2007).

Roller-Integrated Stiffness (kb)

The stiffness of soil, kb (or ks), is determined from a one-dimensional nonlinear mechanical 

model based on the theory of chaotic vibration (Anderegg et al. 2006, Anderegg and Kaufmann 

2004). Figure 24 shows the force-displacement relationship for the roller-soil interaction.

Figure 24. Force-displacement diagram for kb.
37

In this model, the soil is simulated as an elastic-plastic material. Both the loading and unloading 

procedures are considered, and the latter induces partial unrecoverable deformation. The plastic 

deformation of the soil is simulated in the compaction zone (Sandstrom 1994).

The elastic stiffness of kb value can be derived from the slope of the unloading curve as shown in 

Figure 24 A simple model consists of two mechanical parts: one part for the roller which is 

simulated as a rigid body with acceleration toward the soil; another part is for the soil foundation 

which is simulated with one spring and one damping dashpot in parallel. The machine above the 

roller may also be considered to consist of three parts together with the roller and soil foundation, 

as shown in Figure 25.

Some other research has also considered the damping effect of the roller system, such as the 

simulation done by Tawfik (2006). The force balance equations were established for both the 

rigid roller and the soil body. The roller-soil interaction force can be derived from the 

acceleration data such as vibration frequencies and displacement amplitudes, and then the soil 

stiffness is back-calculated.

In this model the drum displacement is expressed as a cosine function. Similar to Figure 23, the 

vibration frequency has a series that is dependent on the roller vibration modes at different stages 

including: (1) permanent drum-ground contact, when i =1Ω (i.e., natural frequency); (2) periodic 

loss of contact when roller lifts off the ground, when i=1Ω, 2Ω, 3Ω; and (3) bouncing/rocking 

during the subharmonic stage, when i=1/2Ω, 1Ω, 3/2Ω, 2Ω, 5/2Ω, and 3Ω.

This model has been implemented in the Ammann IC machines, and has been primarily used for 

the soil/base compaction projects including the Transportation Pooled Funding (TPF) intelligent 

compaction study in the USA.

zd

ks cs

mf

ksusp

csusp

fe(t)

zf

Equivalent frame 

weight

Suspension stiffness

and damping

Drum weight

and dynamic

force generated

Soil stiffness

and damping

md

Fs

Figure 25. Lumped parameter two-degree-of-freedom spring dashpot model 

representing vibratory compactor and soil behavior (reproduced from Yoo and 

Selig 1980).

The kb value is determined using Equation 7, where f is the excitation frequency, md is the drum 

mass, mere is the eccentric moment of the unbalanced mass, φ is the phase angle, and a is 

vibration amplitude. The kb value represents a quasi-static stiffness value and is reportedly 

independent of the excitation frequency between 25 to 40 Hz (Anderegg and Kaufmann 2004).





 



 + = a

m m r k f d e e

b

φ πcos 4 2 2 (7)
38

The kb measurement system has the capability to perform compaction in a manual mode and in an 

automatic feedback control (AFC) mode. The AFC operations in the Case roller are controlled by 

the Ammann Compaction Expert (ACE) plus system. Three AFC operation settings are possible 

using the ACE plus system (Anderegg et al. 2006): 

• Low performance setting: Maximum applied force = 14 kN with vibration amplitude (a*) 

varying from 0.4 to 1.5 mm.

• Medium performance setting: Maximum applied force = 20 kN with vibration amplitude 

(a*) varying from 1.0 to 2.0 mm.

• High performance setting: Maximum applied force > 25 kN with vibration amplitude (a*) 

varying from 2.0 to 3.0 mm.

When operated in AFC mode, as sub-harmonic vibrations occur, the roller automatically adjusts 

the eccentric mass moment to adjust the vibration amplitude and excitation frequency (Anderegg 

et al. 2006). Correlation studies relating ks to soil dry unit weight, strength, and stiffness are 

documented in the literature (Anderegg and Kaufmann 2004).

Figure 26. Ammann two-piece eccentric mass assembly and AFC of vibration

amplitude and frequency (picture courtesy of Ammann).

Vibratory Modulus (EVIB)

The vibratory modulus (EVIB) value is calculated using the one-degree-of-freedom lumped 

parameter model and Lundeberg’s theoretical solution (Lundberg 1939) for a rigid cylinder on an 

elastic half-space. A detailed description of the EVIB measurement technology is provided by 

Kröber et al. (2001). Previous studies (Krober 1998 and Krober et al. 2001) reported that the EVIB

value is related to the modulus determined from a static plate load test. The drum force (Fs) and 

displacement (zd) behavior is related to EVIB (see Equation 8) using Lundberg’s analytical solution. 

According to Hertz (1895), the contact width of a cylindrical drum (B) can be calculated using 

the geometry of the drum, applied force, and the material properties (see Equation 9). The two 

equations (Equations 8 and 9) are numerically solved to determine the EVIB value.

( ) 



 





⋅ +

π ⋅ ⋅ η = B

L 1.8864 ln 2

L

F

E

1- z s

VIB

2

d (8)

where 

L

F

E

16 R'(1 ) B s

VIB

2

⋅ − η ⋅ π = (9)
39

Where, η = Poisson’s ratio of the material, L = length of the drum, B = contact width of the drum, 

and R’ = radius of the drum.

The automatic feedback control (AFC) system developed by Bomag uses a concept of counterrotating eccentric mass assembly that is directionally vectored to vary the vertical excitation force 

on the soil (see Figure 27). If the counter-rotating masses are opposite each other in their rotation 

cycles, the eccentric force is zero. On the other hand, when the counter-rotating masses pass each 

other, the eccentric force is at maximum. The AFC system automatically adjusts the amplitude 

(by adjusting the vectors) depending on the pre-selected settings or the drum behavior (see Figure 

27). Two different AFC settings are available as described below. 

Pre-selected target EVIB and a maximum amplitude amax value: In this setting, the vibration 

amplitude is reduced below the amax value when EVIB ≥ target EVIB, and the amplitude is at the amax

value when EVIB < target EVIB.

Pre-selected vibration amplitude amax value: In this setting, the vibration amplitude is controlled 

based on the drum double jump behavior (described in more detail below) as measured by the 

jump value. When the jump value increases above 0, the amplitude is lowered to 0.6 mm. 

(courtesy of Bomag)

Figure 27. Bomag roller eccentric mass assembly and vectoring to vary the vertical 

excitation force (left) and principle of Bomag’s automatic feedback control (AFC) 

system (right). 
40

Review of Field Correlation Studies

Correlation studies relating different ICMV measurements to soil dry unit weight, strength, and 

stiffness/modulus properties in-situ are documented in the technical literature. A variety of in-situ 

test QC/QA devices have been used in the documented correlation studies (Figure 28):

• Density and moisture content — nuclear gauge (Figure 28a), electrical density gauge 

(Figure 28b), water balloon method, sand cone replacement method, radio isotope 

method, and drive core method.

• Stiffness or Modulus — light weight deflectometer (LWD) (Figure 28c), soil stiffness 

gauge (SSG) (Figure 28d), static plate load test (PLT) (Figure 28e), falling weight 

deflectometer (FWD) (Figure 28f), Briaud compaction device (Figure 28g), dynamic 

seismic pavement analyzer (D-SPA) (Figure 28h), Clegg hammer (Figure 28i), pressure 

meter, and screw plate. 

• Strength or California bearing ratio — shelby tube sampling and laboratory testing 

(Figure 28j), dynamic cone penetrometer (DCP) (Figure 28k), cone penetration testing 

(CPT) (Figure 28l), rut depth measurements under heavy test rolling (Figure 28m).

• Other — surface settlement monitoring under compaction passes using GPS or total 

station surveying, etc. 

A summary of field correlation studies documented in the literature is provided in Table 4.

A comprehensive correlation study involving field evaluation of five different ICMV 

technologies on 17 different soil types from multiple project sites is documented in NCHRP 21-

09 (2010). A list of factors that commonly affected the correlations between ICMV and in-situ 

test measurements identified from that study are as follows:

• Heterogeneity in underlying layer support conditions

• High moisture content variation

• Narrow range of measurements

• Machine operation setting variation (e.g., amplitude, frequency, speed) and roller 

“jumping”

• Non-uniform drum/soil contact conditions

• Uncertainty in spatial pairing of point measurements and ICMV

• Limited number of measurements

• Not enough information to interpret the results

• Intrinsic measurement errors associated with the ICMV and in-situ point measurements 

Of all the factors above, heterogeneity in support conditions of layers underlying the compaction 

layer is identified as the major factor that affects the correlations (NCHRP 21-09 2010). This is 

largely due to differences in measurement influence depths between the roller and the in-situ 

point-MVs (see Figure 29). An approach of using the underlying layer ICMV measurements and 

in-situ point-MV information, and incorporating those into multiple regression analysis is 

described in NCHRP 21-09 (2010).
41

(a) (b) (c) (d)

(e) (f) (g)

(h) (i) (j)

(k) (l) (m)

Figure 28. Various in-situ QC/QA test measurements: (a) nuclear moisture-density 

gauge; (b) electrical density gauge; (c) light weight deflectometers; (d) soil stiffness 

gauge; (e) static plate load test; (f) falling weight deflectometer; (g) Briaud 

compaction device; (h) seismic pavement analyzer; (i) Clegg hammer; (j) shelby 

tube sampling; (k) dynamic cone penetrometer; (l) static cone penetrometer; and (m) 

heavy test rolling.
42

Table 5. Summary of field correlation studies

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

Forssblad 

(1980) Sweden Dynapac 

SD; CMV

Morainic 

soil, fine 

rock fill, and 

coarse rock 

fill

Water balloon 

volumeter (for 

density), PLT, 

FWD, and 

surface 

settlement

Linear correlations have been 

observed between CMV and PointMVs. Moisture content should be 

considered in correlations for fine 

grained soils. Roller results in a 

composite value in layered soil 

condition. CMV measurements are 

affected by roller speed (higher 

speeds result in lower CMV).

Hansbo and 

Pramborg 

(1980)

Stockhol

m, 

Sweden

Dynapac 

SD; CMV

Gravelly 

sand, silty 

sand, and 

fine sand

Sand cone (for 

moisture and 

density), 

pressuremeter, 

PLT, screwplate, CPT, and 

DCP

Compaction growth curves showed 

improvement in CMV and other 

mechanical properties (i.e., modulus 

and cone resistance) with increasing 

pass. Relative compaction 

measurement was not sensitive to 

changes in compaction.

Floss et al. 

(1983)

Munich II 

airport, 

Munich, 

Germany

Dynapac 

Dual SD; 

CMV

Sandy to 

silty gravel 

fill

Bentonite 

displacement, 

water balloon 

method, and 

sand cone (for 

moisture and 

density), PLT, 

and DCP

Correlations generally showed 

increasing CMV with increasing 

density, modulus, and DCP 

penetration blows (per 0.6 m 

penetration). Correlations with 

modulus and penetration blows were 

generally better than density. CMV 

measurements are dependent on 

speed, vibration frequency and

amplitude, type of soil, grain 

composition, water content, and 

strength of subsoil.

Brandl and 

Adam (1997) — BOMAG 

SD; CMV — PLT

Correlation between CMV and PLT 

modulus (initial) showed different 

regression trends for partial uplift 

and double jump operating 

conditions. Regressions in partial 

uplift and double jump conditions 

yielded R2 = 0.9 and 0.6, 

respectively. 

Nohse et al. 

(1999)

2nd 

Tomei 

Exp. way, 

Japan

Sakai SD; 

CMV

Clayey 

gravel

Radio-isotope 

(for density)

On calibration test strips, average

dry density and CMV increased with 

increasing roller passes. Linear 

regression relationships with R2 > 

0.9 were observed for correlations 

between dry density and CMV. 

Kröber et al. 

(2001) — BOMAG 

SD; EVIB Silty gravel PLT

Correlations from calibration test 

strips between EVIB and PLT initial 

and reload modulus (EV1 and EV2) 

showed strong correlation with R2 > 

0.9. 
43

Table 4. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

Gorman and 

Mooney 

(2003); 

Mooney et al. 

(2003)

Oklahoma

Ingersoll 

Rand SD;

Total 

harmonic 

distortion 

(THD)

Well-graded 

sand DCP

Trends in relationship between THD 

and DPI generally showed 

decreasing penetration resistance 

with increasing THD. Strength of 

the correlations improve 

significantly if the sub-lift material 

was stiffer.

Preisig et al. 

(2003); 

Anderegg and 

Kaufmann 

(2004)

Bern, 

Switzerlan

d

Ammann 

dual SD; 

ks

Eight project 

sites with 

sandy gravel 

materials

PLT

Correlations between ks and PLT 

initial and reload (EV1 and EV2) 

modulus showed R2 = 0.68 and 0.56, 

respectively. If only data points with 

EV2/EV1 ratio < 3.5 (i.e., areas with 

less plastic deformation during 

reloading) are considered, the R2

values improve to about 0.80. 

White et al. 

(2004, 2005)

Edwards, 

Illinois

Caterpillar 

PD; MDP Lean clay 

NG, Drive core, 

DCP, and Clegg 

hammer

Correlations between MDP and insitu test measurements using simple 

and multiple regression analyses are 

presented. MDP correlated 

relatively better with dry unit weight 

(R2 = 0.86) than with DCP (R2 = 

0.38) or CIV (R2 = 0.46). Including 

moisture content via multiple 

regression analysis greatly improves 

the R2 values for DCP and CIV (R2

> 0.9). These results were developed 

by averaging data over 20m long 

strip per pass.

Camargo et 

al. (2006)

Atwater 

test site, 

Minnesota

BOMAG 

SD; EVIB

Select 

granular 

subbase 

LWD, DCP, and 

SSG

No statistically significant 

correlation between Point-MVs and 

EVIB values, however, the COV 

observed in LWD and SSG 

measurements were similar to COV 

in EVIB measurements. Narrow range 

of measurements contributed to 

weak correlations. 

Hossain et al. 

(2006)

US65 and 

I-70, 

Kansas

BOMAG 

SD; EVIB

Well-graded 

silty sand NG and DCP

EVIB measurements are sensitive to 

soil moisture content. Weak 

correlation was observed between 

EVIB and CBR determined from 

DCP, and NG. (EVIB measurements 

obtained in AFC mode which are 

influenced by changing amplitude).

Peterson et al. 

(2006)

MnRoad, 

Albertvill

e, 

Minnesota

BOMAG 

SD; EVIB

Silty sand, 

railroad 

ballast, and 

well-graded 

granite

DCP, SSG, 

LWD, PLT, 

FWD, NG, and 

sand cone (for 

moisture and 

density)

Influence of applied stress on soil 

modulus determined by different 

test methods, and influence of 

measurement influence depth and 

differences between different test 

methods are discussed in this paper.
44

Table 1. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

Petersen and 

Peterson 

(2006)

TH53, 

Duluth, 

Minnesota

Caterpillar 

SD; CMV 

and MDP

NA LWD, DCP and 

SSG

Weak correlations were obtained on 

a point-by-point basis comparison 

between in-situ test measurements 

and roller measurements, likely due 

to the depth and stress dependency 

of soil modulus, and the 

heterogeneity of the soils. Good 

correlations were obtained between 

CMV values and DCP 

measurements for depths between 

200 and 40 mm depth. 

White et al. 

(2006a, b)

Edwards, 

Illinois

Caterpillar 

PD; MDP

Well-graded 

silty sand NG and DCP

Average machine power values 

showed a decreasing (logarithmic) 

trend, dry unit weight values 

showed an asymptotic increase, and 

DCP index showed an asymptotic 

decrease with increasing roller pass. 

Correlations between MDP and 

Point-MVs showed good 

correlations (R2 = 0.5 to 0.9). 

Incorporating moisture content into 

analysis is critical to improve 

correlations for dry unit weight.

White et al. 

(2006b); 

Thompson 

and White 

(2008)

Edwards, 

Illinois

Caterpillar 

PD; MDP

Silt and lean 

clay

NG, DCP, Clegg 

Hammer, and 

LWD

Correlations between MDP and 

Point-MVs are presented using 

simple and multiple regression 

analysis. Averaging the data along 

the full length of the test strip (per 

pass) improved the regressions. 

Multiple regression analysis by 

incorporating moisture content as a 

regression parameter further 

improved the correlations.

Thompson et 

al. (2008) and 

White et al. 

(2007a)

US14, 

Janesville, 

Minnesota

Ammann 

SD; ks

Sandy lean 

clay 

subgrade, 

and poorly 

graded sand 

base layer

LWD, PLT, 

DCP, Clegg 

Hammer, and 

NG

ks correlated well with PLT with R2

= 0.80 and the R2 values with other 

measurements ranged from 0.30 to 

0.61 on subgrade. Poor R2 values 

were observed on the base layer due 

to narrow range of measurements. 
45

Table 4. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

White et al. 

(2007a); 

White et al. 

(2008b)

TH64, 

Ackley, 

MN

Caterpillar 

SD, CMV

Poorly 

graded sand 

and wellgraded sand 

with silt

LWD, DCP, and 

NG

Project scale correlations by 

averaging data from different areas 

on the project are presented, which 

showed R2 values ranging from 0.52 

for density and 0.79 for DPI. 

Correlations with LWD showed 

poor correlations due to the effect of 

loose surficial material. The COV

observed in the CMV data was 

similar to DCP and LWD 

measurements but not to density 

measurements.

White et al. 

(2007b); 

White and 

Thompson 

(2008)

Edwards, 

Illinois

Caterpillar 

SD; MDP 

and CMV

Reclaimed 

asphalt, 

clayey 

gravel, silty 

gravel, 

poorly 

graded 

gravel, and 

silt

NG, DCP, Clegg 

hammer, and 

LWD

Averaging the data along the full 

length of the test strip (per pass) 

improved the correlations between 

MDP, CMV, and Point-MVs. The 

relationships were independent of 

soil type. Compaction curves of 

MDP, CMV, and Point-MVs 

showed improvement in soil 

compaction with increasing roller 

pass. CMV measurements reflected 

the properties of the underlying 

subgrade layer.

White et al. 

(2007b)

Edwards, 

Illinois

Caterpillar 

PD; MDP

Sandy lean 

clay NG and DCP

Based on average measurements 

over the length of the test strip (~20 

m); correlations between MDP and 

Point-MVs (R2 = 0.87 for density 

and 0.96 for DCP)

White et al. 

(2008c); 

Vennapusa et 

al. (2009)

Edwards, 

Illinois

Caterpillar 

PD; MDP

Crushed 

gravel base DCP and LWD

Correlations were obtained on a test 

bed with multiple lifts placed on a 

concrete base and a soft subgrade 

base. Correlations between MDP 

and Point-MVs yielded R2 = 0.66 to 

0.85 for spatially nearest point data, 

and R2 = 0.74 to 0.92 for averaged 

data (over the length of concrete or 

soft subgrade).
46

Table 4. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

White et al. 

(2009a, b)

TH60, 

Bigelow, 

Minnesota

Caterpillar 

PD; 

MDP80 

and 

Caterpillar 

SD; CMV

Sandy lean 

clay to lean 

clay with 

sand

Heavy test roller, 

DCP, LWD, and 

PLT

Correlations were obtained on 

multiple calibration test strips and 

production areas from the project. 

MDP80 and LWD modulus 

correlation showed two different 

trends (R2 = 0.35 and 0.65) over the 

range of measurements as the 

MDP80 reached an asymptotic value 

of about 150 which is the maximum 

value on the calibration hard 

surface. CMV correlation with 

LWD modulus produced R2 = 0.70, 

and with rut depth produced R2 = 

0.64. 

White et al. 

(2009b)

TH36, 

North St. 

Paul, 

Minnesota

Caterpillar 

SD; CMV

Granular 

subbase and 

select 

granular 

base

DCP, SSG, 

Clegg Hammer, 

LWD, PLT, 

FWD, and CPT

Correlations between CMV and 

Point-MVs from calibration and 

production test areas based on 

spatially nearest point data are 

presented. Positive trends are

generally observed with R2 > 0.5 

(for LWD, FWD, PLT, SSG, and 

Clegg) with exception of one test 

bed (FWD, LWD, and CPT) with 

limited/narrow range of 

measurements. 

White et al. 

(2009b)

US10, 

Staples, 

Minnesota

Caterpillar 

SD; CMV

Poorly 

graded sand 

with silt to 

silty sand

LWD, PLT, and 

DCP

Correlations between CMV and 

Point-MVs from calibration and 

production test areas based on 

spatially nearest point data are 

presented. Correlations between 

CMV and Point-MVs showed R2 

value ranging from 0.2 to 0.9. The 

primary factors contributing to 

scatter are attributed to differences 

in measurement influence depths, 

applied stresses, and the loose 

surface of the sandy soils on the 

project. Correlations between CMV 

and LWD or DCP measurements 

improved using measurements at 

about 150-mm below the 

compaction surface. 
47

Table 4. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

White et al. 

(2009b)

CSAH 2, 

Olmsted 

County, 

Minnesota 

Caterpillar 

PD; 

MDP80

Sandy lean 

clay LWD

MDP80 values were influenced by 

the travel direction of the roller due 

to localized slope changes and roller 

speed. Correlations between MDP80 

and LWD generally showed R2 > 

0.6 (with exception of one case) 

when regressions were performed 

by separating data sets with different 

travel directions and speed. Data 

was combined by performing 

multiple regression analysis 

incorporating travel speed and 

direction which showed correlations 

with R2 = 0.93. 
48

Table 4. Summary of field correlation studies (continued)

Reference

Project 

location

Roller 

Type; 

ICMV Soil type(s) 

QC/QA 

Point-MVs

Key findings/ 

Comments

NCHRP 21-

09 (2010)

Minnesota

, 

Colorado, 

North 

Carolina, 

Florida, 

and 

Maryland

Caterpillar 

(PD and 

SD; MDP 

and 

CMV), 

Dynapac 

(PD and 

SD; 

CMV), 

Sakai 

(SD, 

CCV), 

BOMAG 

(PD, SD, 

EVIB), 

Case/ 

Ammann 

(SD, ks)

Two types 

of cohesive 

subgrade 

materials, 

five types of 

granular 

subgrades, 

and six 

types of 

granular 

base 

materiasl

NG, DCP, LWD, 

FWD, PLT, 

Clegg hammer, 

SSG

Simple linear correlations between 

ICMV and compaction layer pointMVs are possible for a compaction 

layer underlain by relatively 

homogenous and stiff/stable 

supporting layer. Heterogeneous 

conditions in the underlying layers, 

however, can adversely affect the 

relationships. A multiple regression 

analysis approach is described that 

includes parameter values to 

represent underlying layer 

conditions to improve correlations. 

Modulus measurements generally 

capture the variation in ICMV

values better than traditional dry 

unit weight measurements. DCP 

tests are effective in detecting 

deeper “weak” areas (at depths > 

300 mm) that are commonly 

identified by ICMV values and not 

by compaction layer point-MVs. 

High variability in soil properties 

across the drum width and soil 

moisture content contribute to 

scatter in relationships. Averaging 

measurements across the drum 

width, and incorporating moisture 

content into multiple regression 

analysis, when statistically 

significant, can help mitigate the 

scatter to some extent. Relatively 

constant machine operation settings 

are critical for calibration strips (i.e., 

constant amplitude, frequency, and 

speed) and correlations are generally 

better for low amplitude settings 

(e.g., 0.7 to 1.1 mm).
49

Width (m)

0 1 2 3 4 5 6 7

Depth (m)

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

2.7

3.0

300 mm φ

LWD/FWDa

Nuclear 

Density Gaugec

Dynamic

Cone 

Penetrometer

(DCP)

Notes:

a Influence depths for LWD/FWD are assumed ~ 1 x B (width)

b Influence depth of soil stiffness gauge ~ 230 mm (Florida DOT , 2003)

c Maximum penetration depth for Nuclear density gauge = 0.3 m

dAccording to Method of Equivalent Thickness by Odemark (1949)

Soil Stiffness

Gaugeb

Typical penetration 

depth ~ 1m

Extension

up to ~ 3m

B = 2.1 m

B = 0.3 m Compaction layer

Influence depth 

= 1.0 to 1.2m

Impact Force

From Rollers

X 2.1 m X X X X X X

In-situ point test measurements

Area over which the 

roller MV’s are averaged

Distance = Roller travel in approx. 0.1 to 0.5 sec.

1.5 m (ISSMGE 2005)

X

0.3 m spacing

Geophones

Deflection 

Basind

Figure 29. Illustration of differences in measurement influence depths for different 

measurements (modified from White 2008).

Examine IC Measurements and Correlation Analysis

Influence of Drum Behavior on ICMVs

Previous experimental and numerical investigations (e.g., Adam and Kopf 2004) on roller drumsoil interaction identified five different drum behavior modes which are dependent on the soil 

stiffness and roller operational settings (i.e., amplitude, frequency, and speed). These five modes 

include: continuous contact, partial uplift, double jump, rocking motion, and chaotic motion (see 

Figure 30). The column “application of CCC” means whether continuous compaction control is 

applicable. The accelerometer-based ICMV values (i.e., CMV, ICMV, Omega, EVIB, and ks) are 

influenced by these different drum modes (see Figure 31).

For CMV measurement technology the drum jump behavior is assessed using the ICMV or BV 

measurements. According to Adam and Kopf (2004), ICMV or BV = 0 indicates that the drum is 

in a continuous contact or partial uplift mode. For ICMV or BV > 0, the drum enters double 

jump mode and transitions into rocking and chaotic modes with increasing soil stiffness. Based 

on numerical studies, Adam and Kopf (2004) demonstrated the change in CMV relative to soil 

stiffness and drum behavior as shown in Figure 31. For EVIB measurement technology, the drum 
50

behavior is assessed using Jump value. Jump = 0 indicates the drum is in continuous contact or 

partial uplift mode and Jump > 0 (1 or 2) indicates the drum is in either in double jump, rocking, 

or chaotic mode. AFC systems should help control the drum behavior to prevent the drum 

jumping by automatically adjusting the vibration amplitude and/or frequency. 

Figure 30. Influence of soil modulus and drum behavior on ICMVs (from Adam and 

Kopf 2004).

Figure 31. Influence of drum behavior on ICMVs relative to soil modulus (based on 

numerical simulations from Adam and Kopf 2004) (Note kB = ks).

Correlation between in-situ point tests and IC indices

At different time series and rolling points during compaction, the moisture of soil and 

temperature of HMA may be different from that of the in-situ spot tests. However, the existing 

ICMV models are unable to convert the ICMVs to that at a uniform moisture or temperature 

condition due to the technical challenges. Meanwhile, the existing ICMV models are based on a

multilayered pavement system or a single soil foundation with a certain influence depth, thus they 

are unable to capture the stiffness of a specific pavement layer. As a result, these two issues may 
51

raise problems when correlating the ICMV to the in-situ measurements of a respective soil or 

pavement layer.

Auto-feedback Control (AFC) mode

AFC mode was used for the soil/subbase compaction with Case/Ammann and Bomag IC rollers 

for the Texas, Mississippi, and New York demonstration projects, and conclusions are as follows:

• AFC system is expected to help control the drum behavior to prevent the drum jumping

by automatically adjusting the vibration amplitude and/or frequency. However, results 

show that AFC mode does not necessarily prevent roller jumping (e.g. the Texas 

demonstration using Ammann/Case, and the New York demonstration using Bomag).

• Under AFC mode machine amplitude/frequency adjusts dependent on the settings. E.g. 1: 

when operated in high performance setting, the vibration amplitude was decreased and 

the excitation frequency was increased with increase in ks (Mississippi demonstration 

using Case/Ammann); E.g. 2: when EVIB < target EVIB, the amplitude is at the amax, and 

when EVIB > target EVIB the amplitude is effectively reduced to 0.60 mm (Bomag Evib in 

New York demonstration).

• No consistent proof shows that AFC mode will improve the ICMVs. E.g. the New York 

soil compaction (TB5) shows that AFC mode (with amax = 1.10mm, and target 

EVIB=150MPa) results in higher EVIB at the first pass, but similar results after eight passes 

compared to the manual model with a constant amplitude setting (with a = 0.70mm).

• No proof shows significant difference of in-situ measurements (e.g. dry density, modulus, 

and CBR) after a certain pass count between the AFC mode and manual mode. 

• No consistent proof shows that AFC mode has improved the compaction uniformity 

based on the semivariogram parameters (e.g. the New York soil compaction using the 

Bomag IC roller).

Correlation among Different IC Measurements

Mostly, more than one IC vendor is used for the same highway project in these demonstrations. 

For some of the same test strips, more than one IC vendor is used (e.g. both are used for mapping, 

one is used as breakdown roller and another one is used as intermediate roller, etc.). Results 

indicate that generally different ICMV models may result in similar trends in the same 

compaction area (e.g. the mapping of existing HMA layer using both Sakai and Bomag resulted 

in similar trends for the Maryland HMA IC demonstration). All rollers can identify the weak or 

strong compaction areas. However, the issues of different ICMV models include:

• There is no consistent linear relationship between different ICMVs of different IC 

vendors due to their different computation algorithm and definition, e.g. the 

harmonicfrequency-based unitless CMV or CCV vs. the roller-soil interaction based 

mechanical models of EVIB (MN/m2

) or kb (kN/m);

• Inconsistent ICMV definition could be one of the main obstacles for the industry 

standardization; and thus

• A harmonized model or more standardized definition of ICMV model may help industry 

standardization, better evaluation, and more broad application of IC technology.
52

Verification of the IC models

In this research a prototype is developed to verify the ICMV models from different IC vendors for 

a test purpose. Readers can also refer to similar work in the NCHRP soils IC report (Mooney, et 

al., 2011).

Methodologies

The simulation is based on the model algorithms for different vendors as discussed above. 

Fortran® was used for the core computation and Excel Visual Basic Application® (VBA) was 

used for the interface user inputs/outputs (Figure 32). Different IC models can be simulated, 

including the Case/Ammann Kb, Bomag Evib, Sakai CCV, and Caterpillar CMV.

Four signal types are defined, including the “Harmonic excitation” (pavement stress/displacement 

follows a sinusoidal format), “Measurement” (use the measured roller acceleration data if 

available), “Free generation” (free generated signals), and “Constant” (pavement 

stress/displacement is assumed constant). The general model parameters are defined for all IC 

vendors, e.g. “Debug” (debug or not), “Time point” (time series length), “Time interval”, “Max 

Ite. No.” (maximum allowed iteration number for numerical solution), and “Tolerance” 

(computation error for solution such as for Evib). Roller parameters are also essential information 

for computation of ICMV, such as the “Drum weight” and “Add. mass weight” (the additional 

mass for roller vibration), etc.

Inputs

General Model Parameters

Debug 1 1: Yes;2: No

File path of IC data

Time point 200 times

Time interval 0.033 s

Max Ite. No. 500 times

Tolerance 0.10%

Roller Parameters

Bomag Case/Ammann Caterpillar CMV Sakai CCV Unit

Frame weight 100000 100000 kg

Drum weight 2000 2000 kg

Add. mass weight 100 100 rad/s

Drum width 2 2.2 m

Drum Diameter 1 0.75 m

Add. mass radius 0.25 0.25 m

Vibration frequency 2100 2100 vpm

Force Amp. 146000 146000 N

Deflection Amp. 0.0007 0.0007 m

Phase lag 0 0 rad

Mat. Poisson's ratio 0.35 0.35 N/A

C:\_DOC\207012 - Intelligent Co 

Signal type

IC Model Type

Figure 32. Interface for ICMV simulation.

Verification Results

Figure 33 shows the simulation results of Case/Ammann kb for a time period of 200 minutes 

(12000 seconds). Figure 34 shows the simulation results vs. measurements, which show some 
53

discrepancies and even reverse trends at some time periods. This discrepancy could be due to the 

reasons summarized as follows:

• The IC exported data (measurements) has only limited information with limited time 

points (e.g. sampling rate of 60 seconds) which cannot be directly used to re-produce the 

results through simulation;

• Some information, e.g. deflection of soils and soil-roller interaction force, are unknown 

to the simulation test, which can only be guessed or assumed in the simulation.

RMV Simulation

19.6

19.7

19.8

19.9

20

20.1

20.2

20.3

20.4

20.5

20.6

0 2000 4000 6000 8000 10000 12000 14000

Time (second)

Kb (MN/m)

Single time point

Moving average

Figure 33. Simulated Case/Ammann Kb.

19.6

19.7

19.8

19.9

20.0

20.1

20.2

20.3

20.4

20.5

0 1000 2000 3000 4000 5000 6000 7000

Time (s)

Simulated Kb(MN/m)

10

11

12

13

14

15

16

17

18

Simulation

Measurement

Measureed Kb(MN/m)

Figure 34. Simulated vs. measured Case/Ammann Kb.

Figure 35 shows the simulated Bomag Evib under the signal type of Harmonic excitation, in which 

the deflection and soil-roller interaction force are assumed to follow the sinusoidal function. As 

results, the simulated Evib also roughly follows a harmonic format. It should be noted that this 

result may not represent the IC exported results from field compaction since the deflection/force 
54

may not exactly follow the harmonic function. Meanwhile in real production, sampling will be 

used to “smoothen” the data (e.g. using the average smooth). These may explain the discrepancy 

between the simulation results and measurements from IC vendors.

RMV Simulation

600

610

620

630

640

650

660

670

680

690

0 1 2 3 4 5 6 7

Time (second)

Evib (MP)

Figure 35. Simulated Bomag Evib.

The simulation intends to check the possibility of harmonizing the IC models for the uniform 

industry standards. As shown here one of the main obstacles may be from the available but 

insufficient data/information offered by the production of roller vendors.

A Proposed New IC Model

As discussed before there are some key issues with existing IC models, including that the ICMV 

is not considering each respective pavement layer, and it is not converted to that at a reference 

temperature of HMA. Thus, a more advanced IC model is needed to address these issues. Here a 

new IC model was proposed as a future research. This new IC model will account for the 

following characteristics to address the main issues of existing IC models:

• Decoupled stiffness of each pavement layer, including that of asphalt mixture, 

base/subbase, and soil, respectively;

• The viscoelastic property of asphalt mixture and temperature;

• This proposed model is a mechanical model based on the roller-pavement dynamic 

interaction as shown in Figure 36.
55

Figure 36. Proposed Roller-pavement Interaction model.

The asphalt mixture is regarded as a viscoelastic material effect (Xu and Solaimanian 2009), 

while the soil and base/subbase materials are regarded elastic for numerical simplicity. Some 

critical steps are summarized as follows:

1. The IC mapping would be performed on the soil and subbase/base before paving the fresh 

asphalt layer in order to capture the stiffness of each layer of the pavement from a 

“ground up” method;

2. With the measured displacements of the top HMA layer, the roller-pavement interaction 

force can be derived from the stress/force equilibrium of the roller-soil interaction 

system;

3. With the determined roller-pavement interaction force, the material property like the 

stiffness (k) value can be determined from the stress/force equilibrium of pavement 

structure;

4. With the k-E relationship, the E value can be determined from the computed k value; or

5. If a 2-Dimensional (2-D) analytical or numerical model such as a FE model is used for 

the pavement structure, the back-calculation can be performed to directly determine the 

moduli of pavement layer. 

For the step 5 of 2-D model, a multi-layered pavement analysis program (e.g. ELSYM5) can be 

used, or a finite element (FE) model can be developed, to back-calculate pavement moduli. In 

addition, for step 1 if the IC mapping is not performed on the soil foundation and unbound layers 

underneath HMA, the back-calculation technique (e.g. optimization method) would be needed to 

determine the multi of the multi layers.
56

Chapter 3 Field Spot Tests for IC Implementation

Field spot tests are essential to IC Implementation to provide correlation to IC measurements.

Various IC specifications have mandated specific field spot tests for verification tests, obtaining 

IC target values, and etc. The in-situ tests for soil/subbase and HMA recommended to be used in 

conjunction with ICMVs are described in the following sections. Note that the only “lab tests” 

that we’ll include are Proctor (for soils) and bulk density tests of cores (for HMA).

In-Situ Spot Tests for Soils and Subbase

Several different in-situ testing methods were employed in this study to evaluate the in-situ 

soil physical and mechanical properties (see Figure 37):

• Zorn Light Weight Deflectometer (LWD) with a 200-mm diameter plate using 50 mm 

drop height to determine elastic modulus (ELWD-Z2),

• DCP to determine California bearing Ratio (CBR),

• Calibrated nuclear gauge (NG) to determine moisture-density,

• Falling Weight Deflectometer (FWD) with a 300-mm diameter plate to determine elastic 

modulus (EFWD),

• Static Plate Loading Test (PLT) with a 300-mm diameter plate to determine initial (EV1)

and re-load modulus (EV2), and,

• D-SPA to determine low-strain elastic modulus (ED-SPA).
57

Figure 37. Soil and Subbase in-situ test devices subgrade and flex base material.

Lightweight Deflectomer (LWD)

LWD tests were performed following manufacturer recommendations and the ELWD-Z2 value 

was determined using Equation 10 (Zorn 2003). When padfoot roller was used for compaction, 

the material was carefully excavated down to the bottom of the pad to create a level surface for 

LWD testing.

( ) s f d

E × − =

0

0

2 1 υ σ γ (10)
58

Where E = elastic modulus (MPa), d0 = measured deflection (mm), v = Poisson’s ratio, σ0 = 

applied stress (MPa), r = radius of the plate (mm), fs = shape factor depending on stress 

distribution (assumed as 8/3 for flex base and π/2 for subgrade and lime stabilized subgrade 

materials).

Dynamic Cone Penetrometer (DCP)

DCP test was performed in accordance with ASTM D6951-03 to determine dynamic cone 

penetration index (DPI) and calculate CBR using Equation 11 (Webster et al., 1992, 1994). The 

DCP test results are presented in this report as CBR point values or CBR profiles. When the data 

is presented as point values, the data represents an average CBR of the compaction layer or the 

depth specified (e.g., CBR0-250 represents 0-250 mm depth and CBR250-500 represents 250-500 mm 

depth).

1.12

292 CBR

DPI = (11)

Falling Weight Deflectometer (FWD)

The FWD data were collected using a Dynatest. The test settings were as follows:

• Platen Size: 5.9” in radius (rigid plate)

• Geophone positions: 0, 12, 24, 36, 48, 60, 72 inches (7 sensors)

• Drops/Loads: 2 drops at targeting 6000 and 9000 lbs

• Field program: Dynatest R80

• File format: Dynatest F20.

EFWD-D3 values were determined from the stiffness values using Equation 4 (f values were 

assumed as stated above).

Plate Loading Test (PLT)

Static PLT’s were conducted by applying a static load on 300 mm diameter plate against a 

6.2kN capacity reaction force. The applied load was measured using a 90-kN load cell and 

deformations were measured using three 50-mm linear voltage displacement transducers 

(LVDTs). The load and deformation readings were continuously recorded during the test using a 

data logger. The EV1 and EV2 values were determined from Equation 10, using appropriate stress 

and deflection values as illustrated in Figure 38 depending on the material/layer type. The DSPA test developed by Nazarian et al. (1993) was used on the project. The resulting modulus 

values were determined by D-SPA field program.
59

σ0 (MN/m2)

σ0 (MN/m2)

subgrade base and subbase

0.0

0.1

0.2

0.0

0.2

0.4

Deflection Deflection

EV1 EV2

EV1 EV2

Figure 38. EV1 and EV2 determination procedure from static PLT for subgrade and 

flex base material.

In-Situ Spot Tests for HMA

Several different in-situ testing methods were employed in this study to evaluate the in-situ 

HMA physical and mechanical properties (see FIGURE 39):

Zorn and Dynatest Light Weight Deflectometers (LWD) to determine deflection, CBR, 

and elastic modulus, etc.,

Calibrated nuclear gauge (NG) to determine HMA NG density,

Non nuclear gauge (NNG) to determine HMA NNG density,

Falling Weight Deflectometer (FWD) with a 300-mm diameter plate to determine the 

deflection and back-calculate elastic modulus of pavement layers, 

Portable Seismic Pavement Analyzer (PSPA) to determine low-strain elastic modulus;

Cores to determine the bulk density of HMA at laboratory.

Subgrade and lime 

stabilized subgrade

Flex Base
60

Detector

HMA pavement

Base or subbase layer

Retractable rod

Hander

Shielding

Source

Frame

Source

Sensor 1

Laptop

a) PSPA View

Sensor 2

a) Nuclear gauge

b) Non Nuclear 

gauge

c) FWD

d) LWD

e) PSPA

KUAB Dynatest

Figure 39. HMA in-situ test devices and mechanisms.
61

Falling Weight Deflectometer (FWD)

Primarily two machines are used in this project: the Dynatest FWD and the KUAB FWD. 

The Dynatest FWD test settings were as follows:

• Platen Size: 5.9” radius (rigid plate)

• Geophone positions: 0, 8, 12, 18, 24, 36, 48, 60, 72 inches (7 sensors)

• Drops/Loads: 4 drops each targeting 9,000 and 12,000 lbs

• Field program: Dynatest

• File format: F25, DDX, and MDB.

The KUAB 2m-FWD test settings were as follows:

• Platen Size: 5.9” radius (rigid plate)

• Geophone positions: -12, 0, 12, 18, 24, 36, 48 inches (7 sensors)

• Drops/Loads: 2 drops targeting 9,000 and 13,000 lbs

• Field program: KUAB 2m-FWD

• File format: FWD.

Deflections were measured at these sensors to achieve a deflection basin, and the moduli of 

pavement layers could be back-calculated from measurements using the FWD back-calculation 

programs.

Lightweight Deflectometer (LWD)

Primarily two LWD devices are used in these demonstration projects: Zorn 2000A and 

Dynatest LWD. ZFG 2000A was originally designed for testing existing HMA layers at ambient 

condition, but the most recent version is used for the testing on the hot HMA material. Further 

development is underway for testing on freshly paved HMA layers. The test settings were as 

follows:

• Drop weight: 10 kg or 15 kg;

• Drop height: 70 cm;

• Force: 10.6 kN;

• Pulse time: 17 ms.

The collected data for each drop includes the deflections with time series, the drop speed, etc. 

By using the deflection data collected from these sensors, the CBR of pavement layers were backcalculated by its own program. 

For the Dynatest LWD, the electronics are interfaced to a handheld PDA via a wireless 

Bluetooth connection to record and store data. The test settings were as follows:

• Drop weight: 10 kg;

• Drop height: adjustable;

• Plate diameter: 300 mm.
62

The collected data for each drop includes the centre forces and deflections with time series, 

the centre peaking loading, etc. By using the deflection data collected from the geophone, the 

moduli of pavement layer were back-calculated by its own program. 

Nuclear Density Gauge (NG)

The nuclear density gauge (NG) was used to measure the densities of HMA materials. The 

nuclear density gauge measures the in-place material density based on the gamma radiation. NG 

usually contain a small gamma source (about 10 mCi) such as Cesium-137 on the end of a 

retractable rod (University of Washington website, see reference).

The device consists of a hander, a retractable rod, the frame, a shielding, a source, and a 

Geiger-Mueller detector. The source emits gamma rays that interact with electrons in the HMA 

pavement through absorption, Compton scattering, and the photoelectric effect. The detector 

(situated in the gauge opposite from the handle) counts gamma rays that reach it from the 

source. Then, the received number of gamma rays by the detector is correlated to the density of 

HMA materials.

Non-Nuclear Density Gauge (NNG)

The Pavement Quality Indicator (PQI) developed by Transtech was used to measure in-situ 

HMA density. This technology is based on sensing field to changes in electrical impedance of 

the material matrix. The changes in electrical impedance are a function of the composite 

dielectric constant of the paving material and the air trapped in the voids of the material. Since 

the dielectric constant of air is much lower than that of the paving material, the combined 

dielectric constant increases because the percentage of air in the mix decreases as compaction 

increases. (Transtech, 2003) The PQI has been independently evaluated to correlate its readings 

to other devices.

Portable Seismic Property Analyzer (PSPA)

The seismic methods are based on generating and detecting stress waves in the pavement. A

Portable Seismic Property Analyzer (PSPA) was used in this study for that purpose. The PSPA 

measures the average modulus of the exposed surface layers within a few seconds in the field

(Nazarian et al., 2004).

The PSPA (see Figure 39) consists of two transducers (accelerometers in this case) and a 

source packaged into a hand-portable system, which can perform high-frequency (1 kHz to 50 

kHz) seismic tests. The source package is also equipped with a transducer for consistency in 

triggering. The device is operable from a computer tethered to the hand-carried transducer unit 

through a cable that carries operational commands to the PSPA and returns the measured signals 

to the computer.

To collect data with the PSPA, the testing sequence is initiated and all the data acquisition 

tasks are handled automatically by the computer. The source, which is a computer-controlled 

solenoid, is activated several times to adjust the amplifiers and the dynamic range of the 

electronics. The voltage outputs (time records) of the three transducers from the final three 

impacts are saved and averaged for more reliability. These time records are used to determine the 

surface or Rayleigh wave velocity of propagation (VR) within the HMA layer. The Ultrasonic 

Surface Wave (USW) interpretation method is used to determine the Young’s modulus, E, of the 

material through (Nazarian and Desai, 1993):

2 E= 2 (1+ ) [V (1.13 - 0.16 )] ν ρ R ν (12)

where ν is Poisson's ratio, and ρ is the density of the material.
63

In the absence of mix-specific dynamic modulus test results, empirical relationships can be 

used to adjust the E with temperature effect. This ratio at a temperature of 77o

F is assumed to be 

equal to 3.2 (Aouad et al., 1993). The temperature varies from location to location during testing. 

The relationship suggested by Li and Nazarian (1994) can be used for adjusting the modulus of 

AC to a reference temperature of 77 °F in the absence of mix-specific master curve. That 

relationship is in the form of

E77° = Et / (1.60 - 0.0078 t) (13)

where E77 and Et are the moduli at 77 °F and measured temperature (in Fahrenheit). 

Chapter 4 IC Analysis and Report

IC Data

IC data are often massive and new to DOTs and industries. Thus, it requires practical guidelines 

and protocol to assist DOTs and industries to properly manage the IC data in order to provide 

support for decision-making and quality control/acceptance (QC/QA). Therefore, there is an 

immediate need to develop IC data management protocol to fulfill the needs. The following 

sections describe the recommended IC data elements in vendors’ exported data and methods for 

viewing, analyses, and report of IC data.

Required IC Data Elements

IC data are often stored in proprietary formats. Therefore, it is very important to understand each 

vendor’s IC software program to export IC data in ASCII or text format for post processing. The 

export would need to include “essential data information” (or meta-data) as described in Table 6 

and “essential data elements” (for each measurement location) as described in Table 7.

Table 6: Essential IC Data Information

Items No. Description

1 Section Title

2 Machine trade name

3 Machine type

4 Machine model

5 Drum width (m)

6 Drum diameter (m)

7 Machine weight (metric ton)

8 Name index of intelligent compaction measurement values (ICMV)

9 Unit index for ICMV

10 Reporting resolution for independent ICMVs – 90 degrees to the 

roller moving direction (mm)

11 Reporting resolution for independent ICMVs – in the roller moving 

direction (mm)

12 UTM Zone

13 Offset to UTC (hrs)

14 Number of IC data points

Coordinated Universal Time (UTC) is based on a twenty four hour clock. UTC is also known as 

GMT, or Greenwich Mean Time.
64

Table 7: Essential IC Data Elements. 

Items 

No.

Data Field Name Examples / Notes

1 Date Stamp (YYYYMMDD) e.g. 20080701

2 Time Stamp (HHMMSS.S -

military format)

e.g. 090504.0 (9 hr 5 min. 4.0 s.)

3 Longitude (decimal degrees) e.g. 94.85920403

4 Latitude (decimal degrees) e.g. 45.22777335

5 Easting (m) e.g. 354048.3

6 Northing (m) e.g. 5009934.9

7 Height (m) e.g. 339.9450

8 Roller pass number e.g. 2

9 Direction index e.g., 1 forward, 2 reverse

10 Roller speed (kph) e.g. 4.0

11 Vibration on e.g., 1 for yes, 2 for no

12 Frequency (vpm) e.g. 3500.0

13 Amplitude (mm) e.g. 0.6

14 Surface temperature (oC) e.g. 120

15 Intelligent compaction

measurement values

e.g. 20.0

IC Data Storage Types 

IC vendors often store IC data in two different forms: Time History Data and Post-Processed 

Data. 

• Time History Data: Time history data record the raw IC data during compaction 

operations. They normally include one data point at the bottom center of the drum at a 

point in time, usually at 10 Hz or approximately 1 ft/sec.

• Post-Processed Data: The Time History Data can be post-processed (sometimes in realtime for some vendors’ solution) to produce data in finer meshes, often by duplicating 

data points over the drum width at a 1 ft. interval. Some vendors also provide users 

options to store the post-processed data in all-passes or proofing form. 
65

..............

..............

N-E coordinates of *.gps are here which is center of drum.

N-E coordinates of *.pln are here which is center of mesh.

These coordinates are calculated by AithonMT using *.gps data and 

setting data. 

N

E

(Courtesy of Sakai)

Figure 40. Time history data vs. post-processed data (Sakai America).

The post-processed data can be in two sub-forms: All-Passes Data and Proofing Data. These two 

forms can be imported to Veda. 

• All-Passes Data: As this term indicates, All-Passes Data include all passes within a given 

mesh (e.g., passes I through X in Figure 41). 

• Proofing Data: The Proofing Data contain only the last passes with a given mesh (e.g., 

passes VIII and IX in Figure 41).

..............

..............

N

E

Pass No. =1

Pass No.=2

Pass No.=3

3

2

1

Legend of Pass No.

IX X

VII VIII

V VI

III IV

I II

•I, V and IX are same coordinate.

•II, VI and X are same coordinate.

•III and VII are same coordinate.

•IV and VIII are same coordinate.

All passes data Proofing data only

(Courtesy of Sakai)

Figure 41. All passes data and proofing data (Sakai America). 
66

IC Data Comparisons 

As of the time of writing, the following is a summary of the features for exported IC data from 

various vendors: 

Table 8. Comparison of Exported IC Data. 

Features Ammann Bomag Caterpillar Trimble Sakai

Do not require export 

procedures



Filename extension(s) of 

exported data

log and xml csv csv

(dBase option)

csv

(dBase option)

*.pln and 

*.plns

Contains Geographic GPS

(Long./Lat./Elev.)



Contains UTM or state 

plane grid data 

(Northing/Easting/Height)

   

Contains UTM zone or 

state plane references



(in .xml)

Contains all-passes data     

Contains proof data ? ? ? ? 

Contains pass count 

information



Default data mesh size (h 

x v)

1.25m X 

0.5m

0.3m X 

0.3m

1.0m X 0.15m 1.0m X 0.15m 0.3m X 0.3m

Notes:

• Whether the exported data from various vendors contains “proofing data” is not yet 

confirmed by the above vendors except Sakai.

• The data mesh size is the horizontal and vertical scale of a data point (assuming a square 

area) that is represented on IC maps. The default data meshes were determined by 

comparing the IC maps by vendors’ implementation with those from Veda. Confirmation 

of the actual data mesh sizes is still needed from the vendors. 

Due to evolving nature of IC vendors’ IC systems and software, this document will be updated as 

needed. The goal is to provide exact procedures for users to follow in order to produce 

compatible formats of Veda. Therefore, the subsequent viewing, analysis, and reporting can be 

standardized in national standards (e.g., AASHTO) and state specifications.

Viewing of IC Data 

The Sakai AithonMT®, Bomag BCM Office®, Case/Ammann ACEplus®, Dynapac Dynamic 

Compaction Analyzer (DCA) ® software were used to extract the raw IC data. The Veda tool, 

developed by the Transtec Group, was also used to view, analyze, and report the IC geospatial 

data. Figure 42. shows a screen shot of the Veda results of the demonstration site and color–

coded ICMV map for the Georgia HMA demonstration project. The Veda can also be used to 

perform data analysis and produce reports, including basic statistics and geostatistics, correlation 

of in-situ tests, and compaction curves, etc.
67

Figure 42. A Screen Shot of the Veda tool (GDOT IC demonstration). 

Basic Statistics and Geostatistics 

Basic Statistics

The conventional statistics, including the mean, standard deviation, and coefficient of variance 

(COV) are used to evaluate the roller measurements like ICMV, temperature, frequency, etc.

Figure 43. Basic univariate statistics and histogram report in Veda.
68

Geostatistics - Semi-variogram 

Spatially referenced ICMVs can be used to quantify “uniformity” of compacted materials. 

Vennapusa and White (2010) demonstrated the use of semivariogram analysis in combination 

with conventional statistical analysis to effectively evaluate uniformity during earthwork 

construction. Figure 44. describes a typical semivariogram, as a plot of the average squared 

differences between data values as a function of separation distance (Isaaks and Srivastava 1989). 

The exponential model is used in this research for fitting the semivariogram results. Three 

important semivariogram parameters are: sill (C+C0), range (R), and nugget (C0) (Figure 44.). A 

low “sill” and longer “range of influence” represent best conditions for uniformity, while the 

opposite represents an increasingly non-uniform condition (Isaaks and Srivastava 1989, Clark and 

Harper 2002).

Separation Distance (m)

Semivariogram 

Range, R

Effective Range

R* (3R to 5R)

(For Exponential or 

Gaussian models)

Scale, C

Nugget, C0

Sill

C + C0

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Exponential Semivariogram

Experimental

Semivariogram

Figure 44. Semivariogram and the Fitted Parameters. 

Figure 45. Semi-variogram report in Veda. 
69

Compaction Curves 

Generally, roller operators perform a certain number of roller pass based on experiences and 

measured NG densities at discrete locations. In this way, over or under compaction may occur 

without knowing the material property during compaction. With the IC technology, the 

compaction pattern can be easily reviewed and evaluated. The mean ICMV of a compacted area 

can be computed for each roller pass with the Veda. Then, the optimum roller pass number based 

on the target ICMV can be readily identified.

Figure 46. Compaction Curve Report in Veda. 

Correlation Analysis 

Univariate linear regression

ICMVs are correlated with in-situ measurements using the univariate linear regression method. 

To correlate with the in-situ test spot measurements, ICMV at any specific spot is a mean value of 

those data points fallen within a circled area with a diameter of roller width and the center at the 

spot, rather than a single point value at that test spot. Using a mean value will reduce the 

influence of variability.
70

Figure 47. Correlation Report in Veda

Multivariate linear regression

The multivariate linear regression is also performed on a few demo samples for both soil/subbase 

and HMA demonstration projects, to evaluate the influence of multiple factors on ICMVs. In this 

analysis, for the soil/subbase compaction the influences of soil moisture content and vibration 

amplitude are evaluated, while for HMA compaction the influences of HMA temperature, 

frequency, underlying layer ICMV and frequency are evaluated. E.g. for the soil/subbase and 

HMA compaction the multi lineal models are presented as follows: 

Soil/subbase: RMV ⋅+⋅+⋅+= wbabMVbb 10 2 3 (14) 

HMA: RMVsub Fb sub RMV 10 2 4 bFbTbMVbb 3 4 ⋅+⋅+⋅+⋅+⋅+= (15) 

Where,

ICMV = roller measurement value,

b0 = intercept, 

b1, b2, b3, and b4= regression coefficients, 

a = amplitude (mm), 

w = moisture content (%), 

T = HMA temperature (o

F),

F = vibration frequency on HMA (vpm),

Fsub = vibration frequency on subbase (vpm). 
71

Further information on the IC data analysis and reporting can be found from the 

MnDOT/FHWA IC Data Analysis Tool project.
72

Chapter 5 Demonstration Projects Overview

Field demonstration projects are the major work under the FHWA/TPF IC study. Key elements of the 

field demonstration include on-site training of TPF DOT and contractor personnel, comparison of IC 

roller technologies to traditional compaction equipment and practices, correlating IC roller measurements 

to in-situ spot test measurements, mapping the existing support to understand the influence of underlying 

layer support, selecting the appropriate machine operation parameters (e.g., speed, amplitude, frequency, 

etc.), and managing and analyzing the IC and in-situ test data.

The goals of the field demonstration project are to: 

• Demonstration of soils/subbase and HMA IC technologies to TPF DOT personnel, 

contractors, etc.;

• Develop an experienced and knowledgeable IC expertise base within TPF DOTs;

• Assisting TPF DOT in the development of IC quality control (QC) specifications for the 

soils/subbase and HMA pavement materials, and

• Identification and prioritization of needed improvements and further research for IC 

equipment and data analysis.

The objectives of the field demonstration project are short-term goals for introducing soils and HMA IC 

technology to TPF DOT and contractors who may not have prior experience with IC technology. The 

project was intended to demonstrate the benefits of IC for improving the compaction process and quality 

by achieving more uniform density and modulus of the HMA material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality control. 

There are thirteen full scale field demonstrations and five smaller scale (“mini”) demonstration conducted 

under this study in 13 states (see Figure 48).

MN

KS

TX

MS

IN

NY

PA

VA

MD

ND

GA

WI

2008

2009

2010

Mini demo

2 demos

Figure 48. Distribution and Schedule of IC Field Demonstrations.
73

Soils and Subbase IC Demonstrations 

Demonstration Activities

Table 9 summarizes the FHWA/TPF Soil/Subbase IC demonstration activities, including the project 

locations, IC demonstration elements, machines, and in-situ tests. Detailed information is presented in the 

respective reports (http://www.intelligentcompation.com). For some demonstration sites, the in-situ tests 

were measured following each breakdown roller pass.
74

Table 9. FHWA/TPF Soil/Subbase IC Demonstration Activities.

Location Highway IC Demonstration Test beds Machine In-situ Tests

Fort Worth, TX FM 156

Compaction on

then the treated

flex base

embankment,

subbase and TB1,3: subgrade;TB2: flex base and lime stabilized

subgrade;TB4,TB6/7: flex base;TB5: lime stabilized subgrade.

Case/Ammann, Dynapac

smooth and padfoot drums

LWD,CP,NG,FWD,PL

T,D-SPA

Springville, NY 219 Compaction on

subgrade and subb

embankment

ase materials

TBs1-2: embankment with underling by tire fill;TB3,8:

embankment;TBs4-7,9: gravel subbase for production;TB10:1 or

2m trenches.

Caterpillar;Bomag AFC and

manual modes

LWD,DCP,NNG,FWD

,PLT,BCD

Waynesboro, MS US 84

Compaction on granular (sandy)

and cement treated subbase and

soil with AFC and manual model 

TB1,3:granular base;TB2,8: cement treated granular base;TB4:

granular subgrade;TBs5-7,9: cement-treated granular subgrade;

Caterpillar,Case/Ammann 

AFC and manual

LWD,DCP,NG,FWD,

PLT

Pleasanton, KS US 69 Compaction on the cohesive soil

(weathered shale and lean clay)

TBs1-5:calibration test strips; TB3: production construction with 7

lifts of weathered shale and lean clay fills on sg; TBs6-

7:production area with stiff weathered shale and clean clay sg

Caterpillar and Sakai singledrum padfoot rollersLWD,

FWD,PLT

DCP,NG,

Marmarth, ND

US 12 Compaction on

and salvage base

silty subgrade TBs1-4: silty subgrade: 5-7: salvage base Caterpillar padfoot

smooth drums

and LWD, DCP, CBR,

NG, FWD, and BST.

West Lafayette, 

IN SR 25 Compaction on cohesive soil andgranular materials TBs1-3,6: sandy embankment fill;TB4-5:silty clay Caterpillar single drum LWD, DCP, FWD
75

Hot Mix Asphalt (HMA) IC Demonstration 

Demonstration activity 

Table 10 summarizes the FHWA/TPF HMA IC demonstration activities, including the project 

locations, IC demonstration elements, and in-situ tests. Detailed information is presented in the 

respective reports (see http:www.intelligentcompaction.com). For the in-situ tests, the NG/NNG density 

may be measured following each breakdown roller pass or the finishing roller pass only. The ICMV and 

in-situ measurements following each breakdown roller pass is called data for “all passes”, and that 

following the last breakdown roller or finishing roller only is called “proof data”, this concept will be 

used in the following study. 
76

Table 10. FHWA/TPF HMA IC Demonstration activities.

Location Highway IC Demonstration IC Rollers In-situ Tests

Kandiyohi, MN Route 4 Mapping subbase, HMA base and wearing course paving Sakai LWD,NG,Core

Springville, NY 219 Mapping subbase, 1st and 2nd lift HMA base, and binder course paving Sakai NG

Waynesboro, MS US 84, Mapping the subbase, HMA base paving Sakai FWD, NG

Frederick, MD US 340 Mapping the milled existing HMA layer, SMA1

 overlay paving Sakai, Bomag FWD,NG/NNG,Core

Clayton, GA Park & side Mapping the GAB base, HMA intermediate and surface layer paving Sakai FWD, LWD,Core

West Lafayette, IN US 52 Mapping the milled HMA layer on PCC slab, HMA overlay paving Sakai, Bomag NG,NNG,Core

Mosinee, WI IH 39 Mapping the rubblized PCC base, HMA base, 2nd lift, and surface layer paving Sakai FWD, LWD, NG

El Paso, TX FM 1281 Mapping the milled HMA surface, HMA surface paving Sakai FWD, LWD,NNG

Summerhill, PA SR 53@SR 219 Mapping the milled HMA surface, paving HMA binder and wearing courses Sakai, Volvo LWD, NG

Markham, VA I-66 Mapping the milled HMA surface, paving HMA surface Sakai, Volvo LWD, NG

La Habra, CA residential area Compact HMA overlay Sakai NG

Note: 1. stone matrix asphalt; 2. gradated aggregate base. 
77

Recommendations

• Validation of the IC GPS setup prior to the compaction operation using a survey grade 

GPS hand-held unit on the same position of the IC roller GPS receiver is crucial to 

provide precise and correct measurements.

• In order for in-situ test locations to be accurately located and compared to IC data at 

precise locations, in-situ test locations must be established using a hand-held GPS 

“rover” unit that is tied into the project base station and offers survey grade accuracy.

• Obtaining IC measurements (mapping) of the underlying layers prior to the paving of 

upper layers is highly recommended in order to better identify possible weak spots and 

facilitate the interpretation of the measurements on the asphalt surface layers.

• Long term pavement performance monitoring is recommended in order to identify 

performance trends that may relate to ICMV values.

• Some data post-process should be conducted to evaluate the effectiveness of the IC data. 

For example, due to the uncertainty and acceleration of IC rollers, some measurement 

values maybe outliers and would be filtered out for data analysis purpose.

• A standardized IC data storage format, an independent software tool, and detailed data 

requirements to facilitate the management (viewing and analysis) of IC data (from all 

vendors) and related in-situ/lab test results are strongly recommended to be developed. 

The research team is currently developing guidelines for IC data collection, storage 

requirements, and data processing, a prototype of an independent software tool. An 

update on this will be published as part of the 2009 annual report.

• Further investigation on a global scale (e.g., segment-by-segment analysis of entire paved 

sections) is recommended to provide guidance of usage of IC mapping data on existing 

subbase and base with subsequent IC measurements during HMA paving (such as setting 

a target ICMV value from test trip data based on the onsite support condition and asphalt 

job mix). Nonetheless, the IC demonstration on this project site provides evidence that 

roller passes, temperatures, and measurement values (ICMV) can be tracked.

• To overcome GPS signal shadows at hilly areas, there are three alternatives: (1) Select a 

proper location for the GPS base station, then use signal repeaters to fill in the GPS 

“shadow areas”. (2) Use virtual reference station (VRS) as along as there are good 

cellular reception. (3) Use internet base station and make use of server/client systems to 

transmit the signals.

Chapter 6 Demonstration Projects for Soils IC

This chapter summarizes the results from all soils/subbase IC demonstrations under the 

FHWA/TPF IC project.
78

State Demonstration Results

Texas IC Demonstration

The field IC demonstration was performed in Fort Worth, Texas from July 20 to 25, 2008 and 

an open house was conducted on June 24, 2008. The project was to relocate the highway to 

accommodate Alliance Airport's runway extension on the north end. The Case/Ammann singledrum padfoot and smooth drum vibratory rollers and the Dynapac single-drum smooth drum 

vibratory roller are used for compaction. The demonstration site and material of interest 

(including Type II cohesive subgrade soil, Type III granular base – termed flex base at TxDOT, 

and Type V stabilized materials). Figure 49 displays the IC demonstration site and materials.

The objectives of this demonstration project are short-term goals for introducing the soil IC 

technology to the TxDOT and contractors who may not have prior experience with IC, in order to 

demonstrate the benefits of IC for improving the compaction process by achieving more uniform 

density/modulus of the soil and stabilized base material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality 

control.
79

Figure 49. Texas soil/subbase demonstration.

Major Findings

• It is the first time, based on published literature, that padfoot IC rollers were 

demonstrated to produce excellent compaction curves for clay materials, i.e. increasing 

compaction levels with increasing number of passes before leveling off. The compaction 

curves can be used to determine target stiffness values of compacted materials.

• IC rollers can effectively identify soft spots (e.g. wet zone that can not be compacted, see 

Lan

e 5

Lan 4 3 2 1

e 5

Lan 4 3 2 1

e 5

4 3 2 1

Clay subgrade

Flex Base

Lime Stabilized Subgrade

Flex Base

Flex base and lime stabilized subgrade

Ponding of lime slurry on the scarified 

subgrade A day after compaction on flex base
80

Figure 50a) and hard materials (e.g. box culvert, see Figure 50b) in underlying pavement 

layers.

Distance (m)

20 40 60 80 100 120 140 160 180

kSIPD(MN/m)

0

5

10

15

20

25

30

20 40 60 80 100 120 140 160 180

kSIPD(MN/m)

0

5

10

15

20

25

30

Pass 1

Pass 2

Pass 3

Pass 4

Pass 5

Pass 6

Pass 7

Pass 8

Lane 1

Lane 5

(a) Soft spots: kSΙPD measurement from different passes on TB1 lanes 1 and 5 (nominal a = 

0.8 mm, f = 35 Hz, and v = 3.5 km/h).

Distance (m)

30 40 50 60 70 80 90 100 110 120 130

ks (MN/m)

10

15

20

25

30

35

40

Pass 1

Pass 4

Pass 6

Pass 8

Pass 12

Box Culvert

(b) Hard spots: kSΙPD measurements from different passes on TB 5 lane 3 calibration test 

strip (nominal a = 1.0 mm, f = 35 Hz, and v = 3.5 km/h).

Figure 50. Soft and hard spots identified by IC rollers.

• Both Case/Ammann and Dynapac roller measurement values (ICMVs) captured the wide 

variation in stiffness of the compacted lime stabilized subgrade and flex base materials 

(see Figure 51).

Localized 

Wet Area

Localized Wet Area
81

Figure 51. Comparison between Case/Ammann kSΙSD and Dynapac CMV maps–

Test Bed 2 flex base and lime-stabilized subgrade.

• Roller measurement values by the Case/Ammann rollers are influenced by soft zones in 

the compaction layer as well as that below the compaction layer.

• In-situ measurements using the calibrated moisture-density nuclear gauge, dynamic cone 

penetrometer (DCP), and light-weight deflectometer (LWD) do not match well with 

ICMVs which were influenced by the underlying soft layers.

• Plate bearing test (PLT) and falling weight deflectometer (FWD) produce better 

correlation with the ICMVs.

• As a result of increasing flocculation and agglomeration in flex base, the laboratory 

moisture-density relationships indicate that the optimum moisture content increases and 

the maximum dry unit weight decreases with time (Figure 52). This would provide 

strong evidence by setting proper target compaction values based on the state of the 

material.

Moisture Content, w (%)

5 10 15 20 25

Dry Unit Weight, d (kN/m3

)

13

14

15

16

17

18 Standard Proctor (07/25/08)

Modified Proctor (07/25/08)

Standard Proctor (07/30/08)

Standard Proctor (09/30/08)

In-situ w-d (Pass 12-Lane 3)

In-situ w-d (Pass 8-Lane 4)

Gs = 2.70

ZAV line

Figure 52. Comparison of laboratory Proctor curves and in-situ w-γd

measurements – TB 5 lime-stabilized subgrade material.
82

• Test areas that indicate similar univariate statistics (e.g. coefficient of variation, COV) 

exhibit significantly different geostatistical semi-variograms. It demonstrates the 

advantage of using spatial statistics for a better characterization of non-uniform 

conditions than using univariate statistics.

• Lane 4 which was compacted in AFC mode with amax = 2.4 mm showed roller jumping 

within a 10 m zone as indicated on Figure 53. For this case the AFC mode did not 

necessarily prevent roller jumping or reduce the vibration amplitude. The CMV and BV 

measurement values on all lanes were repeatable (see Figure 53).

10 20 30 40 50 60 70 80 90 100

BV

0

5

10

15

20

25

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

Pass 1

Pass 2

Pass 3

Pass 4

Pass 5

Pass 6

Lane 4

Distance (m)

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Setting: Auto amax = 2.4 mm, f = 30 Hz, v = 3.5 km/h

Figure 53. CMV, BV, and a values for compaction passes on lane 4 of TB6 flex base 

material.

• Smooth drum and pad drum achieves similar compaction effect as shown in Figure 54.

Drum 

bouncing
83

kSIPD (MN/m)

0 10 20 30 40 50 60 70 80

kSISD (MN/m)

0

10

20

30

40

50

60

70

80

Padfoot a = 0.8 mm, f = 35 Hz

Smoothdrum a = 1.1 mm, f = 30 Hz

Padfoot a = 1.3 mm, f = 35 Hz

Smoothdrum a = 1.1 mm, f = 30 Hz

Padfoot a = 1.0 mm, f = 35 Hz

Smoothdrum a = 1.5 mm, f = 35 Hz

Figure 54. Relationship between kSΙPD and kSΙSD measurements.

• The ICMV resulting from auto control and manual control has similar trend, but the auto 

control results in higher CMV values as illustrated in Figure 55.
84

.

Figure 55. Comparison between CMV measurements in manual and automatic 

settings (nominal f = 30 Hz, v = 3.5 km/h).

Future research

• Relating full depth (up to 1 m) DCP-CPR profile with ICMVs should be further 

investigated.

• Based on the measurements from this demon, future in-situ testing should consider 

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

CMV (a = 0.9 mm)

CMV (Auto amax = 2.4 mm)

BV (Auto amax = 2.4 mm)

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

Distance (m)

10 20 30 40 50 60 70 80 90 100

CMV

0

20

40

60

80

Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5 a = 0.9 mm

Auto amax = 2.4 mm

Distance (m)

10 20 30 40 50 60 70 80 90 100

a (mm)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Lane 1

Lane 2

Lane 3

Lane 4

Lane 5

Drum Bouncing

CMV = 150
85

multiple measurements over the drum width for conditions where this type of 

heterogeneity exists. Further study on the averaging scheme in correlation and 

calibration process using geostatistical approach is recommended.

• Better correlations were observed with EFWD, EV1, EV2, and ELWD values (with R2 > 

0.5) compared to ED-SPA and γd. Relationships with ED-SPA show encouraging trends 

in the data, however. The Case/Ammann roller integrated stiffness (kb) values were 

sensitive to moisture content of the compaction layer material. Further investigation on 

using D-SPA to correlate ICMVs is recommended.

• The Dynapac CMV measurements are influenced by the vibration amplitude and show 

that increasing amplitude generally causes an increase in CMV on the material studied in 

this report. But further investigation is warranted.

• Comparison between Case/Ammann padfoot roller measurements (kSΙSD) and padfoot 

roller measurements ( kSΙPD ) show that kSΙPD values are generally greater than kSΙSD. 

Note that the values were obtained at different amplitude settings. Future studies may 

focus on obtaining correlations from the two measurements at similar amplitude settings. 

Comparison padfoot penetration depth measurements in conjunction with kSΙSD and kSΙPD

measurements in future studies may help provide additional insights into the correlations 

between kSΙPD and kSΙSD values. Nevertheless, the trends observed between kSΙPD and kSΙSD

are encouraging and the padfoot roller measurements demonstrate similar advantages as 

the smooth drum roller measurements.

Kansas IC Demonstration

The IC demonstration site is a part of the US 69 expansion project in Pleasanton, north of 

Fort Scott, KS. The field IC demonstration was performed from August 16 to 22, 2008 and an 

open house was conducted on August 21, 2008. The materials include the Type II cohesive 

subgrade soils. Figure 56 summarizes the IC demonstration site and materials.

This is an IC field demonstration conducted on US 69 which is located near Pleasanton, 

Kansas from August 17 to 25, 2008 using Caterpillar and Sakai single drum IC rollers. Key 

attributes for this field demonstration included on-site training of DOT and contractor personnel, 

comparison of IC roller technologies to traditional compaction equipment and practices, 

correlating IC roller measurements to in-situ spot test measurements, evaluating machine 

operation parameters (e.g., speed, amplitude, frequency, etc.), and managing and analyzing the IC 

and in-situ test data.

The objectives of this demonstration project are short-term goals for introducing the soil IC 

technology to the KSDOT and contractors who may not have prior experience with IC, in order to 

demonstrate the benefits of IC for improving the compaction process by achieving more uniform 

density/modulus of the soil and stabilized base material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality 

control.
86

Lane 1

Lane 2

Weathered shale fill

Lean clay fill

Soil foundation

Stiff lean clay subgrade

Subgrade weathered shale fill material 

Foundation Subgrade Layer

Stiff lean clay subgrade

Figure 56. Kansas soil IC demonstration.

Major Findings

• Results indicate that the Sakai CCV and Caterpillar MDP measurement values are 

repeatable (see Figure 57).
87

Figure 57. Caterpillar MDP80 and elevation maps and Sakai CCVPD (TB1 subgrade 

clay material lanes 1 and 2)

• Linear regression analysis produced poor to good correlations between IC and point 

measurement values. Reasons for cases with poor correlations are attributed to influence 

of underlying support conditions, variations in moisture content, and narrow range of IC 

and point measurement values at the test locations. Multiple regression analysis 

indicated that IC measurements are influenced by amplitude and in some cases by 

moisture content and grade slope, in correlations with in-situ point measurements. For 

two cases in this project, compaction using high amplitude setting resulted in 

comparatively similar or higher relative compaction than using low amplitude or static 

settings. 

• Color-coded maps of IC data with 100% coverage information provided the opportunity 

to visualize compaction quality over a production area or at a given point location. This 

opportunity can be beneficial to make informed decisions on compaction process to 

promptly adjust process control measures.

• Geostatistical analysis methods (i.e., semi-variogram analysis) in combination with 

univariate statistics were applied to production area IC measurements to quantify spatial 

uniformity of the compacted materials. The results from these analysis methods showed 

interesting trends in change in compaction quality (in terms of spatial continuity and 

uniformity) with increasing roller passes (e.g. compaction uniformity may decrease with 

increasing roller pass). The use of such analysis methods in construction QC/QA 

procedures represent a paradigm shift in how compaction analysis and specifications 

could be implemented in the future.

• The Sakai CCV measurements obtained from the Sakai padfoot roller were well 

correlated with measurements obtained from the same roller with a smooth drum shell kit 

at this site. Although there was scatter in the relationships, the trends were quite 

North
88

encouraging. The CCV padfoot roller measurements demonstrate similar advantages as 

the smooth drum roller measurements (see Figure 58).

Figure 58. Sakai CCVPD (pad-foot drum, left) vs. CCVSD map (smooth drum, right)

• Production compaction using the Caterpillar padfoot IC roller successfully track 

compaction levels of various materials such as fat clay and lean clay subgrade. . The 

elevation data indicates that the area generally slopes down from north to south. The 

MDP80 data on the foundation layer appears sensitive to driving grade slope with 

relatively high MDP80 values driving downhill (north to south) and relatively low 

MDP80 values driving uphill (south to north), as shown in Figure 59.
89

MDP for Pass Number

1 2 3

Elevation 

(Pass #3)

Elev. (m)

>245.67

245.67

245.06

244.45

243.84

243.23

242.62

<242.62

Passes

>9

8

7

6

5

4

3

2

1

280 m

Pass Number

1 2 3

Rolling Direction

MDP80

>140

135

130

125

120

115

0

Pass 

0 1 2 3 4

Average MDP80

80

90

100

110

120

130

140

150

Direction North to South

Direction South to North1x

Figure 59. MDP80, elevation, pass coverage, and average MDP80 (a = 0.90 mm, f = 33 

Hz, v = 4 km/h) per pass on TB3 foundation subgrade layer

North
90

• An isolated soft/wet spot was identified with low MDP80 values on the map (Figure 60).

Figure 60. MDP80, elevation, pass coverage, and average MDP80 (a = 0.90 mm, f = 33 

Hz, v = 4 km/h) per pass on TB3 lift 1 clay fill and shale fill material 

North
91

• On average the MDP80 values increased with roller passes, and the weathered shale fill 

material was relatively stiffer compared to the lean clay fill material (see the compaction 

curve presented in Figure 60).

• The MDP80 and CCVPD values along the test bed generally track well with changes in insitu point measurements including FWD and LWD measured moduli. 

5 10 15 20 25 30 35 40 45 50 55 60

MDP80

132

136

140

144

148

EFWD-D4.5 (MPa)

0

40

80

120

160

Pass 1

Pass 2

EFWD-D4.5 @ F = 28 kN

Distance (m)

5 10 15 20 25 30 35 40 45 50 55 60

MDP80

132

136

140

144

148

EFWD-D4.5 (MPa)

0

40

80

120

160

Pass 1

Pass 2

EFWD-D4.5 @ F = 27 kN

EFWD-D4.5 @ F = 29 kN

Lane 1: Static, v = 4 km/h

Lane 2: Low amplitude setting

a = 0.90 mm, f = 30 Hz, v = 4 km/h

Figure 61. Comparison between MDP80 and EFWD-D4.5 point measurements – TB1 

subgrade clay material (note: passes 1 and 2 with opposite machine direction of 

travel)

Future research

Although influence of amplitude can be accounted for through multiple regression analysis, it 

is recommended that all measurements obtained from calibration areas and production areas 

during QA be obtained at a constant amplitude setting to avoid complication in data analysis and 

interpretation.

Mississippi IC Demonstration

The field IC demonstration was performed from July 13 to July 17, 2009 on US 84, Wayne 

county, MS. The materials include the subgrade embankment and untreated and treated granular 

(sandy) base (with 5% cement as the MSDOT 9C chemically treated granular material). 

The objectives of this demonstration project were short-term goals for introducing 

soil/subbase and HMA IC technology to MSDOT and contractors who may not have prior 

experience with IC. The project was intended to demonstrate the benefits of IC for improving the 

compaction process by achieving more uniform density/modulus of the HMA material and 

providing roller operators (and superintendents) better feedback tools to make right decisions, and 

ultimately real-time quality control.
92

Granular base 5-day cured 150mm thick cement treated 

granular base

Placement of cement on 

moit-conditioned subgrade

5.5% cement treated 

granular base

Figure 62. Mississippi Soil/subbase IC demonstration.
93

Major findings

• Empirical correlations between ICMVs and different in-situ measurements sometimes 

showed weak correlations when evaluated independently for each test bed, because of the 

narrow measurement range. The correlations improved when data are combined for sitewide correlations with a wide measurement range (see Figure 63).

Figure 63. Regression analyses between CCV and FWD measurements – TB 1 (left), 

TBs 1 and 2 (right)

• ICMVs generally correlated better with modulus based in-situ measurements (i.e., 

ELWD-Z3, EFWD-K3, EV1, and EV2) and CBR point MVs than with dry density in-situ 

measurements. Correlations between ICMVs and EFWD-K3, and ICMVs and EV1 

showed strongest correlation coefficients.
94

d (kN/m3

)

14 15 16 17 18 19 20

CCV

0

5

10

15

20

25

ELWD-Z3 (MPa)

0 50 100 150 200

CCV

0

5

10

15

20

25

EV1 and EV2 (MPa)

0 200 400 600 800

CCV

0

5

10

15

20

25

EV1

EV2

EFWD-D3 (MPa)

0 200 400 600 800

CCV

0

5

10

15

20

25

w (%)

0 5 10 15 20

CCV

0

5

10

15

20

25

CCV = 0.06 ELWD-Z3 + 3.1

R2

 = 0.31

n = 56

CCV = 0.03 EFWD-D3

R2

 = 0.50

n = 27

R2

 = 0.00

n = 62

R2

 = 0.00

n = 62

CCV = 0.02 EV1 + 6.2; R2

 = 0.40

CCV = 0.01 EV2 + 6.3; 

R2

 = 0.18

n = 56

TBs 1, 2, 4 TBs 1, 2, 4

TBs 1, 2 TBs 1, 2, 4

TBs 1, 2, 4

CBR (%)

0 50 100 150 200

CCV

0

5

10

15

20

25

TB2

TBs 1, 4

CCV = 0.04 CBR + 6.4

R2

 = 0.26

n = 60

TB2: Compaction layer ~150 mm

TBs 1, 4:Compaction layer ~300 mm

Figure 64. Regression analyses between CCV and LWD measurements.

• ICMV mapping operations were performed in AFC mode using low, medium, and high 

performance settings on TB8, TB7, and TB9, respectively. When operated in high 

performance setting, the vibration amplitude was decreased and the excitation frequency 

was increased with increase in ks (see Figure 65 a). When operated in low and medium 

performance settings, the vibration amplitude was decreased with increase in ks while the 

frequency remained relatively constant (see Figure 65 b).
95

North

a) TB5/6 (left) and TB9 (right, manual to AFC) b) TB3 (left) and TB8 (right, manual to AFC) 

Figure 65. Comparison of ks, a*, and f maps obtained shortly after compaction 

(TB3 and TB5/6) and after 2-day cure in manual and AFC modes (TB8 and TB9)

• IC data indicated that the response distance for altering the amplitude and/or frequency 

was about 1 to 2 m for a roller travel speed of about 4 km/h. Case/Ammann machine on 

this project reported ICMVs every 1 m. Denser sampling rate is required to accurately 

evaluate the response distance. 

• CCV and modulus/CBR on the cement treated base layer (TB2) are greater than on the 

untreated base layer (TB1). The average CCV on TB2 is about 2.1 times greater than on 

TB1. The average ELWD-Z3, EV1, EV2, EFWD-D3, and CBR in-situ measurements are about 

1.3, 2.6, 1.7, 1.8, and 1.8 times, respectively, greater on TB2 than on TB1. 

• Curing has improved the ks value (e.g. 33% after two day curing) when moisture content 

decreases (e.g. from 10.5% to 7.1%) as shown in Figure 65.

• Geostatistical analysis results indicated that the spatial non-uniformity is higher on the 

treated subgrade and base layers after two days of curing compared to shortly after 

compaction and untreated layers in contrary to the common presumption that stabilization 

creates a more “uniform” working platform. A number of construction related factors 

likely contribute to this increased non-uniformity which includes non-uniform application 

of cement, water content, compaction delay time, and compaction energy across the test 

bed area.
96

Separation Distance, h (m)

0 10 20 30 40 50 60 70

Semivariogram, 

(h) [MN/m]2

0

30

60

90

120

150

TB3 Manual

TB8 Manual

TB8 AFC

TB8 AFC:

Nugget = 40

Sill = 130

Range = 12

TB8 Manual:

Nugget = 20

Sill = 60

Range = 12

TB3 Manual:

Nugget = 20

Sill = 30

Range = 8

Figure 66. Semi-variograms of ks measurements obtained on TB3 (shortly after 

mixing and compaction) in manual mode, TB8 (after two days of curing) in manual 

mode, and TB8 in AFC mode

• The CCV semi-variograms show that treatment base has lower uniformity compared to 

the untreated base (.e.g. with a sill of 28 on TB2 compared to sill value of 6 on TB1).

• Different measurements including CMV, MDP and Ks all identify the weaker or stiffer 

areas with similar zones, which tracks well with in-situ measurements such as CBR (see

Figure 67).
97

Figure 67. Roller spatial maps, MDP40, CMV, and ks measurements along the 

middle lane, and DCP-CBR profiles at selected locations – TB7 granular subgrade 

material

New York IC Demonstration

The field IC demonstration was performed from May 17 to May 22, 2009 on US 219. A total 

of ten test beds including two different materials (i.e., embankment subgrade and subbase 

materials) were studied. The Caterpillar and Bomag IC machines are used in this demonstration 

project.

The objectives of this demonstration project were short-term goals for introducing 

soil/subbase and HMA IC technology to NYSDOT and contractors who may not have prior 

experience with IC. The project was intended to demonstrate the benefits of IC for improving the 

compaction process by achieving more uniform density/modulus of the HMA material and 
98

providing roller operators (and superintendents) better feedback tools to make right decisions, and 

ultimately real-time quality control.

FIGURE 68 New York soil/subbase IC Demonstration.

Embankment material

Gravel Subbase material

2

4 3

Lane 5

TB5 TB4
99

Major Findings

• One of the major findings is that good correlation between IC roller measurement values 

and in-situ point tests can be obtained as long as both tests are conducted properly. See 

the following example for the Bomag and FWD tests on test bed 3 embankment 

materials.

 

 

 

Distance (m)

10 20 30 40 50 60 70 80 90 100

EVIB (MPa)

0

50

100

150

200

250

300

350

EFWD-K3 (MPa)

-200

0

200

400

600

 

EFWD-K3 (MPa)

0 100 200 300 400 500 600

EVIB (MPa)

0

100

200

300

400

500

600

 

R2 = 0.68

Nominal Applied Contact 

Stress:1.02 MPa

Figure 69. Good correlation between Bomag EVIB (pass 3) and FWD tests on lane 3 

(TB3 embankment material).

• Performance of the Bomag automatic feedback control (AFC) compaction operations in 

comparison with conventional operations using the Bomag IC roller were evaluated as 

part of this study. Following are some key observations: 

• TB 3 (embankment): Amplitude measurements along the test strip indicated that when 

EVIB < target EVIB, the amplitude is at the amax, and the amplitude is effectively reduced to 

0.60 mm when EVIB > target EVIB. However, roller jumping was still observed at several 

short segmented sections along the test strip.
100

10 20 30 40 50 60 70 80 90 100

EVIB (MPa)

0

50

100

150

200

250

300

350

a (mm)

0.0

0.5

1.0

1.5

2.0

2.5

Jump

0

1

2

3 Pass 1: Manual mode - a = 0.70 mm 

10 20 30 40 50 60 70 80 90 100

EVIB (MPa)

0

50

100

150

200

250

300

350

a (mm)

0.0

0.5

1.0

1.5

2.0

2.5

Jump

0

1

2

3 Pass 2: AFC mode - amax = 1.90 mm 

Dist (m) vs EVIB 

10 20 30 40 50 60 70 80 90 100

EVIB (MPa)

0

50

100

150

200

250

300

350

a (mm)

0.0

0.5

1.0

1.5

2.0

2.5

Jump

0

1

2

3 Pass 3: Manual mode - a = 0.70 mm 

Dist (m) vs EVIB 

Distance (m)

10 20 30 40 50 60 70 80 90 100

EVIB (MPa)

0

50

100

150

200

250

300

350

a (mm)

0.0

0.5

1.0

1.5

2.0

2.5

Jump

0

1

2

3 Pass 4: Manual mode - a = 1.50 mm 

EVIB

a

Jump

EVIB

a

Jump

EVIB

a

Jump

Jump

a

EVIB

Figure 70. EVIB spatial maps with different machine amplitude settings in manual 

and AFC mode – TB3 embankment material, and jumping

• TB 5 (gravel subbase): In this test bed two side-by-side lanes were compacted in manual 

a = 0.70 mm mode and AFC mode (amax = 1.10 mm, target EVIB = 150 MPa). The average 

EVIB values obtained in AFC mode (a = 1.0 mm on average) are on-average about 1.3 

times higher than the average EVIB values obtained in manual mode operation. Average 

ELWD-Z3 values in the lane compacted in AFC mode were about 1.6 times higher than in 

the lane compacted in manual mode. However, the average relative compaction values,

average EFWD-K3 values, and CBR values are similar for the two lanes after pass 8.

Although the EVIB values showed low COV with AFC operations compared to COV with 

manual mode operations, In-situ measurements did not show considerable difference in 

the COV values. More testing is recommended in future projects with different amax and 

target EVIB settings to further evaluate AFC mode compaction operations with variable 

subsurface conditions.

• TB 10 (trench): In this test bed, deep trenches were excavated and compacted in side-byside lanes using manual and AFC settings. The compaction curves of average dry density, 

modulus, and CBR measurements generally increased with increasing passes up to pass 8 

in the trench areas. However, no significant difference was measured in terms of density 

of the material at the surface in the trenches between lanes compacted using AFC mode 

and manual mode (91% relative compaction in the 1m trench and 88 to 89% relative 

compaction in the 2m trench). Similarly, ELWD-Z3 and CBR300 measurements did not 
101

demonstrate considerable differences between AFC mode and manual mode compacted 

lanes. Analysis of incremental increases in CBR with depth relative to pass 1 in the 2 m 

wide x 1 m deep trench did not present considerable differences between AFC mode and 

manual mode compaction. 

• To study the influence of amplitude on Caterpillar MDP40 ICMVs, two side-by-side 

gravel subbase lanes were compacted in static and low amplitude modes. The average 

MDP40 values obtained in static mode are on average about 1.1 times higher than the 

MDP40 values obtained in low amplitude mode. The average ELWD-Z3 values obtained after 

pass 8 in the lane compacted in static mode is about 1.6 times higher than in the lane 

compacted in low amplitude mode. However, the average relative compaction and CBR 

values are similar for the two lanes after pass 8. 

• Geostatistical analysis methods were utilized to analyze spatially referenced ICMV data 

to assess spatial non-uniformity of compacted fill materials. Some ICMV data sets 

showed nested semi-variogram structures with short-range and long-range components. It 

is possible that the long-range components are because of differences in the underlying 

support conditions (i.e., shredded tire fill at depths < 1 m) while the short-range 

components are a result of spatial variations of soil properties close to surface. Additional 

studies are needed to better understand this finding.

• Five different test devices were used to determine elastic modulus: (a) 200 mm Zorn 

LWD; (b) 300 mm Zorn LWD; (c) BCD; (d) KUAB FWD; and (e) static PLT. 

Regression relationships between modulus measurements obtained from these five 

different devices are presented in this report. Some key findings from these relationships 

are as follows:

• Relationships between 200 mm and 300 mm LWD measurements indicated that ELWD-Z2 

measurements are on average 1.3 times greater than ELWD-Z3 measurements. Differences 

in modulus between ELWD-Z2 and ELWD-Z3 measurements are attributed to: (a) different 

plate diameters; (b) differences in measurement influence depths; and (c) differences in 

applied contact stresses.

• Relationship between FWD and LWD indicated that EFWD-K3 is on average about 3.4 

times greater than ELWD-Z3 measurements. Differences are attributed to: (a) type and 

location of deflection measurement sensors; and (b) applied contact stresses. 

• Relationships between ELWD-Z3 vs. EBCD, and EFWD-K3 vs. EV1 and EV2 produced weak 

correlations (R2 = 0.10 to 0.13). Weak correlations between ELWD-Z3 and EBCD are 

attributed to the difference in the applied contact stresses. ELWD is determined using 

contact stresses that are about10 times higher than contact stresses applied to determine 

EBCD. Weak correlations between EFWD-K3 and EV1 or EV2 are in part attributed to the 

limited number of measurements (n = 23) and the limited measurement range over which 

the tests were performed (all the tests were performed on very stiff material EV1 > 250 

MPa). 

• Three different test devices were used to determine dry unit weight and moisture content: 

(a) Troxler nuclear gauge; (b) Humboldt nuclear gauge; and (c) Transtech’s soil density 

gauge. Regression relationships between dry unit weight and moisture measurements 

obtained from these three different devices are presented in this report. Some key 

findings are as follows:

• Relationship between γd(H) and γd(T) produced strong correlation with R2 = 0.73. However, 

on average the γd(T) measurements were about 1.04 times greater than γd(H) measurements. 
102

Relationship between w(H) and w(T) yielded weak correlation with R2 = 0.10. On average 

the w(H) measurements were about 1.7 times greater than w(T) measurements.

• Relationship between γd(H) and γd(SDG) produced weak correlation with R2 = 0.41. On 

average the γd(SDG) measurements were about 1.02 times greater than γd(H) measurements.

No statistically significant relationship was identified between w(H) and w(SDG).

• Relationship between γd(SDG) and γd(T) also produced a weak correlation with R2 = 0.27. 

However, the measurements were scattered around the line of equality. Relationship 

between w(H) and w(T) similarly produced weak correlation with R2 = 0.10. On average 

the w(SDG) measurements were about 1.2 times greater than w(T) measurements. 

North Dakota IC Demonstration

This report presents results from a field investigation conducted on US Highway 12 in 

Marmarth, North Dakota. The machine configurations and roller-integrated measurement 

systems used on this project included: a Caterpillar CP56 smooth drum roller with a padfoot shell 

kit (here after referred to as padfoot roller) equipped with machine drive power (MDP), and a 

Caterpillar CS563E vibratory smooth drum roller equipped with MDP and compaction meter 

value (CMV) intelligent compaction (IC) measurement technologies. The machines were 

equipped with real time kinematic (RTK) global positioning system (GPS) and on-board display 

and documentation systems.

The project involved construction and testing of seven test beds. Four of these test beds 

included silty subgrade materials and the remaining three included salvage base materials. The 

test beds with salvage base materials varied in terms of their underlying support conditions. One 

test bed was reinforced with two layers of geogrid in the base layers, one test bed was partially 

treated with over excavation and replacement due to soft subgrade conditions, and the other test 

bed served as a control section with no special treatments. Six different in-situ testing methods 

were used in this study to evaluate the in-situ soil engineering properties: (a) Zorn light weight 

deflectometer setup with 200 and 300 mm diameter plates to determine elastic modulus (ELWD-Z2

for 200 mm plate diameter and ELWD-Z3 for 300 mm plate diameter), (b) dynamic cone 

penetrometer (DCP) to determine California bearing ratio (CBR), (c) calibrated Humboldt nuclear 

gauge (NG) to measure moisture content (w) and dry unit weight (γd), (d) Dynatest falling weight 

deflectometer (FWD) setup with 300 mm diameter plate to determine elastic modulus (EFWD-D3), 

(e) Kuab falling weight deflectometer (FWD) setup with 300 mm diameter plate to determine 

elastic modulus (EFWD-K3), and (f) in-situ bore hole shear test (BST) to determine soil drained 

shear strength properties. Figure 71 presents the demonstration site, materials, machines, and insitu tests.
103
104

Major findings

• MDP increases with pass count, and high-amplitude setting results in lower MDP than 

low-amplitude setting (see Figure 72).

Figure 72. MDP* and elevation maps of lanes 1 to 3, and MDP* spatial maps for 

multiple padfoot roller passes on lanes 1 to 3 – TB1
105

• MDP* semi-variogram on the mixed subgrade layer showed a nested spatial structure 

with both short-range and long-range components, while the semi-variograms on the base 

layers 1 and 2 did not.

Separation Distance, h (m)

0 5 10 15 20 25 30

Semi-variogram 

(h) of MDP* [MDP*]2

0

2

4

6

8

10 TB4-Mixed Subgrade

TB4-Base 1

TB4-Base 2

Spherical 

Variogram (line)

Experimental 

Variogram

(point)

Separation Distance, h (m)

0 5 10 15 20 25 30

Semi-variogram 

(h) of MDP* [MDP*]2

0

10

20

30

40

50 TB4-Base 2

TB5 Final Base 

TB6 Final Base

Spherical 

Variogram (line)

Experimental 

Variogram

(point)

Mixed Subgrade:

Nugget = 0

Sill1 = 3.0, Range1 = 9.1m

Sill2 = 8.0, Range2 = 36.6 m

Base 1:

Nugget = 0

Sill = 2.1, Range = 3.1 m

Base 2:

Nugget = 0

Sill = 3.5, Range = 7.6 m

TB4-Base 2:

Nugget = 0

Sill = 3.5, Range = 7.6 m

TB5-Final Base:

Nugget = 0

Sill = 13.5, Range = 2.4 m

TB6-Final Base:

Nugget = 0

Sill = 35.0, Range = 12.2 m

Figure 73. (a) Comparison of MDP semi-variograms on TB 4 mixed subgrade, base 

1, and base 2 layers, (b) Comparison of MDP semi-variograms on TB4, 5, and 6 base 

layers 

• The static MDP* ICMVs in TB3 production area showed more variability with high sill 

values compared to the vibratory MDP* ICMVs with low amplitude. This was also 

evident with a slightly higher standard deviation (σ) value for static MDP* over the 

vibratory low amplitude MDP*.

• The average ELWD-Z2 and CBR250 point-MVs were lower on low and high amplitude 

mode lanes, compared to the lanes compacted in static mode. In contrary, the average γd 

was greater on low and high amplitude mode lanes than on static mode lane.

• MDP* ICMVs obtained during compaction of salvage base layers indicated slightly 
106

different trends in the average MDP* compaction growth for forward (in low amplitude 

mode) and reverse (in static mode) passes. MDP* ICMVs were repeatable for forward 

passes but were affected by variable machine speed for reverse passes, and therefore were 

not repeatable. 

• MDP* ICMVs were slightly lower (by about 1.04 times) on TB5 control section than on 

the TB4 geogrid reinforced section. The EFWD-K3 point-MVs were also on average 

slightly lower (by about 1.1 times) on TB5 than on TB4, while the average ELWD-Z3 

point-MVs were about the same. The COV of MDP* ICMVs and point-MVs on TBs 4 

and 5 were quite similar. 

Figure 74. Photographs of TB4 during placement of TX5 geogrid and salvage base 

layers

Indiana IC Demonstration

The field IC demonstration was performed on the State Route 25 (SR-25), which is a new 

highway alignment extending from I-65 to CR750E north of Buck Creek, West Lafayette, Indiana,

from August 16 to 19, 2010 and an open house was conducted on August 19, 2010. The machine 

configurations and roller-integrated measurement systems used on this project included: a 

Caterpillar CS56 smooth drum with a padfoot shell kit for the cohesive soil and a Caterpillar 

CS563E smooth drum vibratory roller for the granular embankment, both equipped with machine 

drive power (MDP) IC measurement technology. 

The project involved constructing and testing six test beds consisting of cohesive and 

granular embankment fill materials. The intelligent compaction (IC) measurement values (MVs) 

were evaluated by conducting field testing in conjunction with a variety of in-situ testing devices 

measuring density, moisture content, California bearing ratio (CBR), and elastic modulus. These 

field testing devices and measurements included: ): (a) calibrated Humboldt nuclear gauge (NG) 

to measure moisture content (w) and dry unit weight (γd), (b) dynamic cone penetrometer (DCP) 

to determine California bearing Ratio (CBR), and (c) Zorn light weight deflectometer setup with 

300 mm plate diameter to determine elastic modulus. Assistance with field testing was provided 

by several INDOT personnel. 

The goals of this field investigation were similar to previous demonstration projects and 

included the following:

• document machine vibration amplitude influence on compaction efficiency,
107

• develop correlations between IC measurement values (ICMVs) to traditional insitu point measurements (point-MVs),

• compare IC results to traditional compaction operations,

• study IC measurement values in production compaction operations, and

• evaluate IC measurement values in terms of alternative specification options.

Figure 75. Construction site, materials and in-situ testing
108

Major findings

• Granular embankment fill MDP* generally increased with increasing number of passes, 

but no significant differences are noticeable using different amplitude settings (see Figure 

76 on lanes 1 and 2).

Figure 76. MDP* maps on lanes 1 and 2 – TB1 granular embankment fill

• MDP* values are repeatable in both forward and reverse passes though some differences 

are observed which are due to various operating conditions (see Figure 77).

0 5 10 15 20 25 30

MDP*

100

110

120

130

140

150

Pass 1

Pass 3

Pass 7

Pass 15

a = 0.90 mm 

Distance (m)

0 5 10 15 20 25 30

MDP*

100

110

120

130

140

150

Pass 2

Pass 4

Pass 8

Pass 16

a = 0.90 mm 

Machine Gear: Forward

Machine Gear: Reverse

Figure 77. Average MDP* with increasing roller passes in forward and reverse gears – TB2 

granular embankment fill
109

• Results indicate that for the silt clay subgrade on average, ELWD-Z3, DCP-CBR, and γd

values on lane 2 compacted in static mode were about 1.5, 1.03, and 1.3 times, 

respectively higher compared to the values on lane 1 compacted in low amplitude mode 

as shown in Figure 78. The reason for lower values on lane 1 is attributed to possible 

surface disturbance under vibration.

Figure 78. MDP* maps from passes 1 to 8 on lanes 1 and 2 – TB4 silty clay subgrade 

• The experimental semivariograms values showed a nested spatial structure with shortrange and long-range components, similar as observed before in the New York and North 

Dakota IPF projects. It is possible that the long-range spatial structure is linked to the 

spatial variation in underlying layer support conditions while the short-range spatial 

structure is a result of soil properties close to the surface.

MDP*

80 90 100 110 120 130 140 150 160

Frequency

0

1000

2000

3000

4000

Pass 1 

Pass 2 

Pass 1:  = 133.1,  = 5.9, COV =4%

Pass 2:  = 131.5,  = 6.0, COV = 5%

Separation Distance, h (m)

0 20 40 60 80 100

Semi-variogram (h) of MDP* [MDP*]2

0

20

40

60

80

100

120

140

Pass 1

Pass 2

Pass 1:

Nugget = 0

Sill1 = 29

Range1 = 9.1 m

Sill2 = 35

Range2 = 48.8 m

Pass 2:

Nugget = 0

Sill11 = 28

Range1 = 9.1 m

Sill2 = 34

Range2 = 53.3 m

Figure 79. Geostatistical semi-variograms and histograms of passes 1 and 2 on lift # 1 – TB 

3 granular embankment fill production area
110

• ELWD values were correlated best to the MDP values. Spatial analysis showed 

changes in semi-variance as a function of roller pass coverage and demonstrates 

an approach for characterizing spatial non-uniformity.

ELWD-Z3 (MPa)

0 50 100 150 200

MDP*

60

80

100

120

140

TB4

TB5

d (kN/m3

)

15 16 17 18 19 20

60

80

100

120

140

TB4

CBR (%)

0 10 20 30 40 50

MDP*

60

80

100

120

140

TB4

w (%)

6 8 10 12 14 16 18 20

60

80

100

120

140

TB4

MDP* = 32.2 (ELWD-Z3)

0.30

R2

 = 0.75, n =79

R2

 = 0.0, n = 7

MDP* = 122.5 - 1.87 (w)

R2

 = 0.13, n =7

R2

 = 0.0, n = 7

Figure 80. Correlations between MDP* and in-situ point-MVs – padfoot roller with static 

setting (TBs 4 and 5 cohesive embankment fill) 

Compaction Curves

Figure 81 to Figure 85 show the compaction curves for the Network, Texas, Kansas, and 

North Dakota demonstration projects, respectively. The main findings are summarized as follows:

• Generally ICMV and in-situ measurements increase with increasing pass number and the 

increase rate gradually decreases until close to be a constant. 

• At some cases, the ICMV drops from the 1st pass to the 2nd pass and then increases. The 

reduction of ICMV at the 2nd pass count is possibly due to knocking down the rough 

ground condition at the first pass.

• For some test strip and materials, ICMV may increase first and then decrease and the 

optimum pass number can be identified.

• The variation trends of ICMV and in-situ measurements are similar, but at some cases 

their trends may be inconsistent dependent on material type. E.g. for the Texas project, 

ELWD showed a reduction in the average value after 12 passes, likely because of surface 

de-compaction. The average ksΙPD value reached an asymptotic value by 8 passes and 

showed an increase from pass 11 to 12 while ICMV increases continuously.

• Compaction curve can show the development of material strength with time. E.g. for the 

TX TB2 flexible base material, strength gains significantly over time as the material dries 
111

and is further compacted under construction traffic.

• Compaction curve results are very dependent on material type under construction. E.g. 

compaction curves show higher values (more stiffness) for the weathered shale fill 

material compared to that of the lean clay fill material (KS projects).

• Compaction curve is affected by material moisture condition, and the dry and optimum 

moist result in higher ICMV for each roller pass (e.g. TX project).

• The sloping grade of compaction area also affects the compaction curve. E.g., for the KS 

TB3 subgrade the MDP80 compaction curves of driving downhill (north to south) are 

higher than that of driving uphill (south to north).
112

Pass

0 2 4 6 8

EBCD (MPa)

0

20

40

60

80

0 2 4 6 8

γd(SDG)(kN/m3

)

19

20

21

22

23

0 2 4 6 8

MDP40

80

90

100

110

120

130

140

0 2 4 6 8

CMV

0

20

40

60

80

100

0 2 4 6 8

γd(H)(kN/m3

)

19

20

21

22

23

Lane 2, Static

Lane 3, a = 0.90 mm

0 2 4 6 8

w(H) (%)

3

4

5

6

7

8

9

0 2 4 6 8

w(SDG) (%)

3

4

5

6

7

8

9

0 2 4 6 8

CBRBase (%)

0

5

10

15

20

25

30

95% standard Proctor density

95% standard Proctor density

Standard Proctor wopt

Standard Proctor wopt

0 2 4 6 8

ELWD-Z3 (MPa)

0

20

40

60

80

No data from Pass 0

No data from Lane 2

Pass

0 2 4 6 8

EFWD-K3 (MPa)

0

50

100

150

200

No data from Pass 0

No data from Pass 0

Based on 2 

Point-MVs

Figure 81. NY project: average ICMV and In-situ measurement compaction curves 

on lanes 2 and 3 – TB4 gravel subbase material
113

Pass

0 2 4 6 8 10

Average ELWD-Z2 (MPa)

6

9

12

15

18

Pass

0 2 4 6 8 10

Average d (kN/m3

)

13

14

15

16

17

18

19

Section 1 - Dry 

Section 2 - Optimum

Section 3 - Wet

0 2 4 6 8 10

Average w (%)

5

10

15

20

25

30

35

Pass

0 2 4 6 8 10

Average CBR (%)

3

6

9

12

15

18

0 2 4 6 8 10

Average kS PD I (MN/m)

8

10

12

14

16

18

20

Std. Proctor wopt = 17.8%

Std. Proctor dmax = 16.8 kN/m3

TX: TB 1 lane 1 calibration test strip, nominal a = 0.8 mm, f = 35 Hz, and v = 3.5 km/h 

0 2 4 6 8 10 12 14

Average kS PD I (MN/m)

18

19

20

21

22

23

24

25

0 2 4 6 8 10 12 14

Average ELWD-Z2 (MPa)

20

25

30

35

40

45

50

Pass

0 2 4 6 8 10 12 14

Average d (kN/m3

)

8

10

12

14

16

18

20

Pass

0 2 4 6 8 10 12 14

 Average w (%)

18

20

22

24

26

28 Pass

0 2 4 6 8 10 12 14

Average CBR (%)

0

5

10

15

20

TX: TB 5 lane 3 calibration test strip, nominal a = 1.0 mm, f = 35 Hz, and v = 3.5 km/h 

0 2 4 6 8

Average CMV

0

10

20

30

40

50

P

0 2 4 6 8

Average CMV

0

10

20

30

40

50

Lane 3 Lane 4

0 2 4 6 8

Average CMV

0

10

20

30

40

50

0 2 4 6 8

Average CMV

0

10

20

30

40

50

Lane 1 Lane 2

TX: TB 6 lane 1 to 4 Pass

Figure 82. TX project: average ICMV and In-situ measurement compaction curves
114

Pass

Pass

0 2 4 6 8 10 12 14

d (kN/m3

)

16

17

18

19

20

21

22

Pass

0 2 4 6 8 10 12 14

w (%)

5

10

15

20

Low readings due to loose 

material at the surface (Pass 0)

Values corrected using 

w% values after Pass 1

Pass

0 2 4 6 8 10 12 14

CCVPD

1

2

3

4

5

6

7

Lane 1

Lane 2

Lane 3

Lane 2 (Low amp setting): 

a = 0.93 mm, v = 6 km/h, f = 33 Hz

Lane 3 (High amp setting): 

a = 2.19 mm, v = 6 km/h, f = 26 Hz 

Lane 1 (High amp setting): 

a = 2.19 mm, v = 6 km/h, f = 26 Hz 

TB3 lift 4 calibration test strip weathered shale fill (a = 2.19 mm, f = 26 Hz,v = 6 km/h)

KS: TB3 foundation subgrade layer KS: TB3 lift 1 clay and shale fills KS: TB3 lift 3 clay and shale fills

KS: TB3 lift 4 clay and shale fills KS: TB4 subgrade clay material lanes 1, 2, and 3 

(a = 0. 90 mm, f = 33 Hz, v = 4 km/h)

Pass

0 2 4 6 8 10 12 14

CCVPD

4

6

8

10

12

Pass

0 2 4 6 8 10 12 14

ELWD-Z2 (MPa)

0

10

20

30

40

50

60

 

 

 

 

Figure 83. KS project: average ICMV and In-situ measurement compaction curves
115

Pass Number

0 2 4 6 8 10 12 14 16

ELWD-Z2 (MPa)

0

20

40

60

80

Pass Number

0 2 4 6 8 10 12 14 16

d (kN/m3

)

13

14

15

16

17

18

Pass Number

0 2 4 6 8 10 12 14 16

w (%)

8

10

12

14

16

Pass Number

0 2 4 6 8 10 12 14 16

CBR250 (%)

0

5

10

15

20

25

30

Pass Number

0 2 4 6 8 10 12 14 16

MDP*

80

100

120

140

Lane 1 - Static

Lane 2 - Low Amp (a = 0.90 mm)

Lane 3 - High Amp (a = 1.80 mm)

1 kN/m3

 = 6.37 pcf

90% AASHTO T-180 dmax

Figure 84. ND average MDP* and in-situ point measurement values with increasing 

roller passes on lanes 1 to 3 – TB1
116

Pass Number

0 1 2 3 4 5

MDP*

110

115

120

125

130

135

140

a = 0.90 mm

a = 1.80 mm

Pass Number

0 1 2 3 4 5

ELWD-Z3 (MPa)

0

10

20

30

40

50

a = 0.90 mm

a = 1.80 mm

Pass Number

0 1 2 3 4 5

CBR (%)

0

5

10

15

20

a = 0.90 mm

a = 1.80 mm

Pass Number

0 1 2 3 4 5

d (kN/m3

)

14

15

16

17

18

19

20

a = 0.90 mm

a = 1.80 mm

Pass Number

0 1 2 3 4 5

w (%)

0

2

4

6

8

10

a = 0.90 mm

a = 1.80 mm

dmax = 18.98 kN/m3 (120.8 pcf)

95%dmax = 18.03 kN/m3

 (114.8 pcf)

Figure 85. Indiana MDP* and in-situ point-MV compaction curves lane 1 (a = 0.90 mm) and 

lane 2 (a = 1.80 mm) – TB1 granular embankment fill

Compaction Uniformity

Compaction uniformity is evaluated using the Geostatistical semi-variogram knowledge. 

Kriging (e.g. ordinary Kriging) is used to pre-preprocess the geospatial data before doing the 

geostatistical analysis. Kriging is a method to estimate a value at a point of a region for which a 

variogram is known, using data in neighborhood of the estimation location (Wackernagel, 1998). 

Basically, Kriging normally provide better interpolation of a spatial data points. Consequently, 

semi-variograms are computed with the parameters presented. Figure 86 to Figure 90 show the 
117

semi-variograms of the Texas, Kansas, New York, Mississippi, and North Dakota, respectively. 

The main findings are summarized as follows:

• Results show that usually a higher vibration amplitude results in lower compaction 

uniformity (e.g. for the TX demonstration, a higher sill is achieved when using an 

amplitude a = 1.1 mm than using a = 0.9 mm). The reasons for a difference in sill values 

of CMV for different machine amplitude settings are attributed to the materials’ response 

to different stress conditions and possibly different influence depths. Increasing 

amplitude increases the contact stresses under the roller drum and is also believed to 

increase the depth of influence under the drum. 

• Geostatistical studies have shown the advantage of using spatial geostatistics for a better 

characterization of non-uniform conditions than using univariate statistics. E.g. for the 

Texas TB7 flexible base, the ICMVs for vibration amplitudes of 1.1 mm and 0.9 mm 

have similar COV values though with very different semi-variogram sill values, which 

differentiate the uniformity of compacted area with different machine settings. 

• Results also show that the uniformity variation is not consistent with pass number. With 

more pass number, uniformity may increase (e.g. semi-variogram sill value decreased for 

caterpillar MDP80 of KS and NY demonstration projects), or decrease (e.g. sill value 

increased for Sakai CCV of lift 6 of NY project, see Figure 88). The range values may 

decrease slightly with more pass counts (e.g. range decreases from 2 m to 1.6 m as seen 

in Figure 87), which suggests a decrease in spatial continuity. 

• Geostatical semi-variograms also differentiate the uniformity of different materials or 

with various treatments. E.g. for the KS project (see Figure 87), MDP80 on weathered 

shale fill was more uniform compared to MDP80 on lean clay fill with lower sill values. 

Their range values are also different indicating their different spatial continuity.

• No consistent proof to show whether manual mode or AFC mode achieves a higher 

uniformity. E.g. for the NY project TB8, the manual model shows higher uniformity 

(with lower sill value), while for the TB3 the manual model shows lower uniformity 

(with higher sill value).

• Curing seems to increase non-uniformity of both ICMV and in-situ measurements (e.g. 

KS TB8 2-day curing results in higher non-uniformity compared to TB3 no curing). This 

increasing non-uniformity is attributed to various factors such as non-uniform application 

of cement, water content, mixing, compaction delay time, and compaction energy across 

the test bed area, etc.
118

Separation Distance (m)

0 5 10 15 20 25 30 35

Semi-Variogram of CMV

0

30

60

90

120

150

CMV (a = 0.9 mm) Experimental

CMV (a = 0.9 mm) Exponential

CMV (a = 1.1 mm) Experimental

CMV (a = 1.1 mm) Exponential

Separation Distance (m)

0 5 10 15 20 25 30 35

Semi-Variogram of EFWD-D3 (MPa)2

0

200

400

600

800

1000

EFWD-D3 Experimental

EFWD-D3 Exponential

CMV (a = 0.9 mm)

Sill: 90 (MN/m)2

Effective Range: 6 m 

CMV (a = 1.1 mm)

Sill: 49 (MN/m)2

Effective Range: 6 m 

Sill: 750 (MPa)2

Effective Range: 4.5 m 

Figure 86. Texas Kriged contour maps and semi-variograms of CMV and EFWD on 

TB 7 flex base material.
119

Separation Distance (m)

0 10 20 30 40 50

Semivariogam (h) (MDP80)

2

0

50

100

150

200

250

Pass 1, Experimental

Pass 2, Experimental

Pass 3, Experimental

Pass 4, Experimental

Pass 1, Exponential

Pass 2, Exponential

Pass 3, Exponential

Pass 4, Exponential

Lean Clay Fill

Separation Distance (m)

0 10 20 30 40 50

Semivariogam (h) (MDP80)

2

0

50

100

150

200

250

Pass 1, Experimental

Pass 2, Experimental

Pass 3, Experimental

Pass 4, Experimental

Pass 1, Exponential

Pass 2, Exponential

Pass 3, Exponential

Pass 4, Exponential

Weathered Shale Fill

Separation Distance (m)

0 10 20 30 40 50

Semivariogam (h) (CCVPD)

2

0.0

0.4

0.8

1.2

1.6

Pass 1, Experimental

Pass 2, Experimental

Pass 3, Experimental

Pass 4, Experimental

Pass 1, Exponential

Pass 2, Exponential

Pass 3, Exponential

Pass 4, Exponential

Lean Clay Fill

Separation Distance (m)

0 10 20 30 40 50

Semivariogam (h) (CCVPD)

2

0.0

0.4

0.8

1.2

1.6

Pass 1, Experimental

Pass 2, Experimental

Pass 3, Experimental

Pass 4, Experimental

Pass 1, Exponential

Pass 2, Exponential

Pass 3, Exponential

Pass 4, Exponential

Weathered Shale Fill

KS: TB3 – lift 6 lean clay and weathered shale fill materials, Sakai CCV 

KS: TB3 – lift 3 lean clay and weathered shale fill materials, Caterpillar MPD

Figure 87. KS project: change in semi-variograms, spatial statistics of Caterpillar 

MDP and Sakai CCV with pass.
120

NY: TB2 embankment material

Separation Distance, h (m)

0 10 20 30 40 50 60 70 80

Semi-Variogram, γ(h) (MDP40)

2

0

20

40

60

80

100

120

Pass 1, a = 0.90 mm

Pass 2, a = 0.90 mm

Experimental Nested 

Spherical

 

 

 

Separation Distance, h (m)

0 10 20 30 40 50 60 70 80

Semi-Variogram, γ(h) (MPa)2

0

100

200

300

400

500

600

Pass 3

Pass 4

Pass 5

Pass 6

Experimental Nested 

Spherical

NY: TBs6/7 gravel base material

Separation Distance, h (m)

0 20 40 60 80

Semi-Variogram, (h) (MDP40)

2

0

20

40

60

80

100

120

Pass 1, Static

Pass 2, a = 0.90 mm

Pass 3, a = 0.90 mm

Nested Experimental

Spherical

Separation Distance, h (m)

0 20 40 60 80

Semi-Variogram, (h) (CMV)2

0

100

200

300

400

500

600

Pass 2, a = 0.90 mm

Pass 3, a = 0.90 mm

Experimental

Spherical

NY: TB1 embankment material

Figure 88. NY project: semi-variograms of ICMV for different passes.
121

 

 

 

Separation Distance, h (m)

Semivariogram, 

0 20 40 60 80

(h) [CCV]2

0

10

20

30

40

50

Pass 1

Separation Distance, h (m)

Semivariogram, 

0 20 40 60 80

(h) [MDP40]

2

0

30

60

90

120

150

Pass 2

Pass 3

Pass 4

Pass 5

Pass 6

Pass 7

Separation Distance, h (m)

Semivariogram, 

0 20 40 60 80

(h) [CMV]2

0

20

40

60

80

Pass 2

Pass 3

Pass 4

Pass 5

Pass 6

Pass 7

Separation Distance, h (m)

0 10 20 30 40 50 60 70 80 90 100

Semivariogram, (h) [MN/m]2

0

30

60

90

120

150

TB5/6 Manual

TB9 Manual

TB9 AFC

Separation Distance, h (m)

0 10 20 30 40 50 60 70

Semivariogram, (h) [MN/m]2

0

30

60

90

120

150

TB3 Manual

TB8 Manual

TB8 AFC

TB8 AFC:

Nugget = 40

Sill = 130

Range = 12

TB8 Manual:

Nugget = 20

Sill = 60

Range = 12

TB3 Manual:

Nugget = 20

Sill = 30

Range = 8

MS: TB1 granular base material 

MS: TB3,TB8 Cement treated granular base;TB9, cement treated granular subgrade

Figure 89. MS project: semi-variograms of ICMV for different passes and 

manual/AFC controls.
122

Separation Distance, h (m)

0 20 40 60 80 100 120 140

Semi-variogram (h) of MDP* [MDP*]2

0

20

40

60

80

100 Experimental

Nested Spherical

Nugget = 45

Sill1 = 63

Range1 = 45.7 m

Sill2 = 89

Range2 = 167.6 m

COV = 8%

MDP*mapping pass – TB7 

Separation Distance, h (m)

0 20 40 60 80 100

Semi-variogram (h) of MDP* [MDP*]2

0

20

40

60

80

100

Experimental - Static 

Experimental - Low Amp

Nested Spherical - Static

Nested Spherical - Low Amp

Static

Low Amplitude

Static Mode:

Nugget = 55

Sill1 = 67

Range1 = 30 m

Sill2 = 73

Range2 = 140 m

COV = 8%

Low Amplitude Mode

Nugget = 20

Sill1 = 40

Range1 = 40 m

Sill2 = 47

Range2 = 140 m

COV = 7%

MDP* from static and low amplitude 

mapping passes – TB3 

Separation Distance, h (m)

0 5 10 15 20 25 30

Semi-variogram (h) of MDP* [MDP*]2

0

2

4

6

8

10 TB4-Mixed Subgrade

TB4-Base 1

TB4-Base 2

Spherical 

Variogram (line)

Experimental 

Variogram

(point)

 

 

 

Mixed Subgrade:

Nugget = 0

Sill1 = 3.0, Range1 = 9.1m

Sill2 = 8.0, Range2 = 36.6 m

Base 1:

Nugget = 0

Sill = 2.1, Range = 3.1 m

Base 2:

Nugget = 0

Sill = 3.5, Range = 7.6 m

 

 

 

 

 

 

 

Separation Distance, h (m)

0 5 10 15 20 25 30

Semi-variogram (h) of MDP* [MDP*]2

0

10

20

30

40

50 TB4-Base 2

TB5 Final Base 

TB6 Final Base

Spherical 

Variogram (line)

Experimental 

Variogram

(point)

 

 

 

 

 

 

 

TB4-Base 2:

Nugget = 0

Sill = 3.5, Range = 7.6 m

TB5-Final Base:

Nugget = 0

Sill = 13.5, Range = 2.4 m

TB6-Final Base:

Nugget = 0

Sill = 35.0, Range = 12.2 m

Comparison of MDP semivariograms on TB 4 

mixed subgrade, base 1, and base 2 layers, 

Comparison of MDP semivariograms on 

TB4, 5, and 6 base layers 

Figure 90. ND project: semi-variograms of roller MDPs.

 

 

 

 

 

 

 

Separation Distance, h (m)

0 20 40 60 80 100

Semi-variogram (h) of MDP* [MDP*]2

0

20

40

60

80

100

120

140

Pass 1

Pass 2

Pass 3

Pass 4

Pass 1:

Nugget = 0

Sill = 100

Range = 9.4 m

Pass 2:

Nugget = 0

Sill = 115

Range = 10.7 m

Pass 3:

Nugget = 0

Sill = 75

Range = 10.7 m

Pass 4:

Nugget = 0

Sill = 80

Range = 10.7 m

 

 

 

 

Separation Distance, h (m)

0 20 40 60 80 100

Semi-variogram (h) of MDP* [MDP*]2

0

20

40

60

80

100

120

140

Pass 1

Pass 2

Pass 1:

Nugget = 0

Sill1 = 29

Range1 = 9.1 m

Sill2 = 35

Range2 = 48.8 m

Pass 2:

Nugget = 0

Sill11 = 28

Range1 = 9.1 m

Sill2 = 34

Range2 = 53.3 m

Granular embankment fill base lift 1 Granular embankment fill base lift 2

Figure 91. INDOT project: semi-variograms of roller MDPs (granular embankment 

fill).
123

Correlation Study

Univariate correlation

The univariate linear or logarithmic-scaled linear regression method is used to correlate 

ICMVs with in-situ measurements, which includes four types: 1) direct linear to linear correlation; 

2) linear correlation of ICMV vs. logarithmical scaled in-situ measurements, presenting in a log 

function of ICMV vs. in-situ measurement; 3) linear correlation of logarithmic scaled ICMV vs. 

in-situ measurements, presenting in a exponential function of ICMV vs. in-situ measurement; and 

4) linear correlation of logarithmic scaled both ICMV and in-situ measurements, presenting in a 

power function of ICMV vs. in-situ measurement.

Correlation of ICMV to LWD Moduli

Figure 92 present the correlation results between ICMVs and ELWD from LWD measurements. 

The main conclusions are summarized as follows:

• ICMV increases with increasing ELWD as expected, and generally they have a good linear 

correlation (with R2 > 0.60);

• Dependent on the specific test strip and materials used, either the direct linear, or the 

logarithmic scaled linear function may better represent their linear correlation; 

• ICMVs under the machine static mode also shows good linear correlation with in-situ 

measurements (e.g. NY and KS MDP40);

• For some cases, the relatively poor correlation is obtained (e.g. NY TBs1 and 2, CMV), 

which could be due to the more narrow range of ICMVs and limited in-situ test points;

• No obvious evidence shows that the machine settings such as frequency, amplitude and 

speed will affect the correlation quality (e.g. TX TB1);

• The correlation and variation trend of ICMV with in-situ measurement are very 

dependent on materials used (e.g. KS TB3 foundation shale and clay materials; TX TB2 

stabilized subgrade and flexible base); separate trends of the same project but with 

different materials could be a result of differences in the underlying support, material, 

and moisture conditions.
124

ELWD-Z2 (MPa)

0 50 100 150 200 250

MDP40

0

50

100

150

200

ELWD-Z3 (MPa)

0 50 100 150 200

0

50

100

150

200 MDP40 = 11.40 ln(ELWD-Z2) + 75.5

R2

 = 0.62, n = 49

MDP40 = 26.99 ln(ELWD-Z3) + 5.2

R2

 = 0.63, n = 112

 

 

 

 

 

 

TBs 1, 2, 3: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h TBs 3, 4, 9, 10: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

 

 

 

 

 

 

 

 

 

NY (a = 0.90 mm, f = 30 Hz, and v = 4 km/h)

ELWD-Z3 (MPa)

0 50 100 150 200

0

50

100

150

200 MDP40 = 9.71 ln(ELWD-Z3) + 89.0

R2

 = 0.73, n = 46

 

 

 Static, v = 4 km/h

NY, static

MPD40

ELWD-Z2 (MPa)

0 50 100 150 200 250

CMV

0

20

40

60

80

100 CMV = 6.22 (ELWD-Z2)

0.39

R2 = 0.39, n = 15

TBs 1 and 2: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

 

 

 

 

NY (a = 0.90 mm, f = 30 Hz, 

and v = 4 km/h)

ELWD-Z3 (MPa)

0 50 100 150 200

CCV

0

5

10

15

20

25

 

CCV = 0.06 ELWD-Z3 + 3.1

R2

 = 0.31

n = 56

 

 

 

 

 

 

 

 

 

 

 

TBs 1, 2, 4 

 

 

 

 

 

 

 

 

MS

ELWD-Z3 (MPa)

0 50 100 150 200

MDP40

80

100

120

140

160

 

MDP

TBs 4,7

MDP40 = 148.8 (1-e(-0.04 ELWD-Z3))

R2

 = 0.75

n = 42

 

 

 

 

 

 

 

 

 

 

 

 

MS

ELWD-Z3 (MPa)

0 50 100 150 200

CMV

0

10

20

30

40

50

 

CMV = 0.12 ELWD-Z3 + 4.7

R2

 = 0.24

n = 42

 

 

 

 

 

 

 

 

TBs 4,7

 

 

 

MS

ELWD-Z3 (MPa)

0 50 100 150 200

ks (MN/m)

0

20

40

60

80

100

 

ks = 8.7 (ELWD-Z3)

0.35

R2

 = 0.49

n = 88

 

 

 

 

 

 

 

 

 

 

 

 

TBs 3, 4, 5, 7 

 

 

 

 

 

 

 

MS

ELWD-Z2 (MPa)

0 20 40 60 80 100

CCVPD

0

4

8

12

16

20

 

CCVPD = 1.76 + 0.14 ELWD-Z2

R2 = 0.75

n = 57

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KS (a = 2.19 mm, f = 26 Hz) 

ELWD-Z2 (MPa)

0 20 40 60 80 100

CCVPD

0

4

8

12

16

20

CCVPD = 2.46 + 0.05 ELWD-Z2

R2 = 0.82

n = 37

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KS (a = 0.93 mm, f = 33 Hz) 

CCV

ELWD-Z2 (MPa)

0 20 40 60 80 100

CCVSD

0

4

8

12

16

20

CCVSD = 0.92 + 0.11 ELWD-Z2

R2 = 0.84

n = 15

 

 

 

 

 

 

 

 

 

KS (a = 1.48 mm, f = 26 Hz) 

ELWD-Z2 (MPa)

0 10 20 30 40 50

MDP80

100

110

120

130

140

150 MDP80 = 115.9 + 0.46 ELWD-Z2

R2 = 0.60

n = 62

 

 

 

 

 

 

 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

KS (static, driving uphill ) 

ELWD-Z2 (MPa)

0 10 20 30 40 50

MDP80

110

120

130

140

150 MDP80 = 120.6 + 0.51 ELWD-Z2

R2

 = 0.67

n = 15

 

 

 

 

 

 

 

 

 

KS (a=0.9mm,

f=33Hz, driving uphill ) 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

ELWD-Z2 (MPa)

0 10 20 30 40

MDP40

30

60

90

120

150

TB3 Lift 3

TB5 Wet Organic

Clay

MDP40 = 70.3 + 17.06 ln (ELWD-Z2)

R2

 = 0.93

n = 11

 

 

 

KS (a=0.9mm, f=33Hz ) 

MPD40

KS (a = 2.19 mm, f = 26 Hz) 

ELWD-Z2 (MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = 1.59 + 0.054 ELWD-Z2

R2 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = 2.09 + 0.14 ELWD-Z2

R2 = 0.77, n = 8

 

 

 

 

 

 

 

 

 

 

 

TB3 Lift 1 (Shale)

TB3 Lift 6 (Shale)

TB3 Foundation Clay

TB3 Lift 1 (Clay)

TB3 Lift 2 (Clay)

TB3 Lift 6 (Clay)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELWD-Z2 (MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = 1.59 + 0.054 ELWD-Z2

R2 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = 2.09 + 0.14 ELWD-Z2

R2 = 0.77, n = 8

 

 

 

 

 

 

 

 

 

 

 

ELWD-Z2 (MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = 1.59 + 0.054 ELWD-Z2

R2 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = 2.09 + 0.14 ELWD-Z2

R2 = 0.77, n = 8

 

 

 

 

 

 

 

 

 

 

 

TB3 Lift 1 (Shale)

TB3 Lift 6 (Shale)

TB3 Foundation Clay

TB3 Lift 1 (Clay)

TB3 Lift 2 (Clay)

TB3 Lift 6 (Clay)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ELWD-Z2 (MPa)

0 20 40 60

kSIPD(MN/m)

0

5

10

15

20

25

30

R2

 = 0.30

n = 120 

 

 

 

 

 

 

 

 

 

 

 

TX (a = 0.8 mm, 

f = 35Hz, and v = 3.2 km/h)

TB1

ELWD-Z2 (MPa)

0 20 40 60

kSISD(MN/m)

0

5

10

15

20

25

30

R2

 = 0.10

n = 56 

 

 

 

 

 

 

 

 

 

 

 

 

 

TX (a = 1.1 mm, 

f = 30Hz, and v = 3.5 km/h) TX

ELWD-Z2 (MPa)

0 50 100 150 200 250 300 350

kSISD(MN/m)

0

20

40

60

80

Lime 

Fl 

R2 = 0.37 

n = 0.37

 

 

 

 

R2

 = 0.25 

n = 10

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

ELWD-Z2 (MPa)

0 50 100 150 200 250 300 350

kSISD(MN/m)

0

20

40

60

80

Lime 

Fl 

R2 = 0.37 

n = 0.37

 

 

 

 

R2

 = 0.25 

n = 10

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

ELWD-Z2 (MPa)

0 50 100 150 200 250 300 350

CMV

0

50

100

150

200

Lime 

R2 = 0.60

n = 15

 

 

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

ELWD-Z2 (MPa)

0 50 100 150 200 250 300 350

CMV

0

50

100

150

200

Lime 

R2 = 0.60

n = 15

 

 

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

TX

ELWD-Z2 (MPa)

0 20 40 60

kS PD I (MN/m)

10

15

20

25

30

35

40

R2 = 0.55

n = 63 

 

 

 

 

 

 

 

 

 

 

 

TB5

ELWD-Z2 (MPa)

0 20 40 60

kS PD I (MN/m)

10

15

20

25

30

35

40

R2 = 0.55

n = 63 

 

 

 

 

 

 

 

 

 

 

 

TB5

TX

TB1

ELWD-Z2 (MPa)

0 20 40 60 80 100 120

MDP*

60

80

100

120

140

160

MDP* = 0.49 ELWD-Z2 + 104.4

R2

 = 0.50

n = 6

ND (a = 0.90 mm and f = 30Hz) 

ELWD-Z2 (MPa)

0 20 40 60 80 100 120

MDP*

60

80

100

120

140

160

MDP* = 0.52 ELWD-Z2 + 97.1

R2

 = 0.54

n = 45

 

 

 

 

 

 

 

 ND (static) 

ELWD-Z3 (MPa)

0 50 100 150 200

MDP*

60

80

100

120

140

160

TB4

TB5

TB6

 

TB4: MDP* = 121.5 (ELWD-Z3)

0.04

R2 = 0.31, n = 75

TB5: MDP* = 97.35 (ELWD-Z3)

0.08

R2 = 0.23, n = 30

TB6: R2

 = 0.00, n = 8

Combined:

MDP* = 129.9 (ELWD-Z3)

0.02

R2

 = 0.10, n = 113

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ND, salvaged base 

(a = 0.90 mm and f = 30 Hz) 

Figure 92. ICMVs vs. LWD measurements.

Correlation of ICMV to FWD moduli

Figure 93 present the correlation results between ICMVs and LWD measurement of ELWD. 

The main conclusions are summarized as follows:

• ICMV increases with increasing EFWD as expected, and they have shown a good linear 

correlation (i.e. R2 > 0.66); 

• Dependent on the specific test strip and materials used, either the direct linear, or the 

logarithmic scaled linear function may achieve better correlation; 
125

• ICMVs under the machine static mode also shows good linear correlation with in-situ 

measurements (e.g. KS MDP80);

• There are no evidences to show the influences of machine settings including amplitude 

and frequency on their correlation relationship;

• Separate trends are observed for different materials (e.g. TX TB2 lime treated subgrade 

and flexible base), which could be a result of differences in the underlying support, 

material, and moisture conditions, etc.

0

EFWD-D3 (MPa)

0 200 400 600 800 100012001400

CMV

0

50

100

150

200

e Stablized Subgarde

 

 

R2

 = 0.15

n = 11

 

 

R2

 = 0.62

n = 15

R2

 = 0.94

n = 26

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

0

EFWD-D3 (MPa)

0 200 400 600 800 100012001400

CMV

0

50

100

150

200

e Stablized Subgarde

 

 

R2

 = 0.15

n = 11

 

 

R2

 = 0.62

n = 15

R2

 = 0.94

n = 26

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

 

 

 

 

EFWD-K3 (MPa)

0 100 200 300 400 500 600

0

50

100

150

200 MDP40 = 20.94 ln (EFWD-K3) + 13.1

R2

 = 0.72, n = 82

MDP40

 

 

 

 

 

 

 

TBs 4, 8, 10: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

 

 

 

 

 

 

 

NY (a = 0.90 mm, f = 30 Hz, and v = 4 km/h)

 

 

EFWD-K3 (MPa)

0 100 200 300 400 500 600

MDP40

0

50

100

150

200 MDP40 = 11.62 ln(EFWD-K3) + 67.4

R2

 = 0.73, n = 88

Static, v = 4 km/h 

NY, static

 

EFWD-D3 (MPa)

0 200 400 600 800

CCV

0

5

10

15

20

25

 

 

 

CCV = 0.03 EFWD-D3

R2

 = 0.50

n = 27

 

 

 

 

 

 

 

 

 

TBs 1, 2 

 

 

 

 

 

 

 

MS

 

EFWD-D3 (MPa)

0 200 400 600 800

ks (MN/m)

0

20

40

60

80

100

 

 

 

ks = 3.1 (EFWD-D3)

0.47

R2

 = 0.74

n = 77

 

 

 

 

 

 

 

 

 

 

TBs 3, 5, 8 

 

 

 

 

 

 

MS

KS (a = 2.19 mm, f = 26 Hz) 

CCV

0

EFWD-D4.5 (MPa)

0 50 100 150 200 250

CCVPD

0

4

8

12

16

20

 

 

 

CCVPD = 2.93 + 0.02 EFWD-D4.5

R2 = 0.69

n = 28 

 

 

 

 

 

 

 

 

 

 

 

KS (a = 0.93 mm, f = 33 Hz) 

EFWD-D4 5 (MPa)

0 50 100 150 200 250

CCVSD

0

4

8

12

16

20

0

TB6

TB7

 

 

 

CCVSD = 1.79 + 0.05 EFWD-D4.5

R2

 = 0.87

n = 15

 

 

 

 

 

 

KS (a = 1.48 mm, f =26 Hz) KS (static, driving uphill)

TB2

TB1

 

 

 

 

 

 

 

EFWD-D4.5 (MPa)

0 20 40 60 80 100 120

MDP80

110

120

130

140

150

MDP80= 131.5+ 0.08 EFWD-D4.5

R2 = 0.66

n = 6

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

TB2

TB1

 

 

 

 

 

 

 

EFWD-D4.5 (MPa)

0 20 40 60 80 100 120

MDP80

110

120

130

140

150

MDP80= 131.5+ 0.08 EFWD-D4.5

R2 = 0.66

n = 6

TB2

TB1

 

 

 

 

 

 

 

EFWD-D4.5 (MPa)

0 20 40 60 80 100 120

MDP80

110

120

130

140

150

MDP80= 131.5+ 0.08 EFWD-D4.5

R2 = 0.66

n = 6

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

KS (a=0.9mm, f=33Hz, uphill ) 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

TB2

TB1

 

 

 

 

 

 

EFWD-D4.5 (MPa)

0 20 40 60 80 100 120

MDP80

110

120

130

140

150

MDP80 = 132.7+ 0.08 EFWD-D4.5

R2 = 0.71

n = 6

 

 

 

TB2

TB1

 

 

 

 

 

 

EFWD-D4.5 (MPa)

0 20 40 60 80 100 120

MDP80

110

120

130

140

150

MDP80 = 132.7+ 0.08 EFWD-D4.5

R2 = 0.71

n = 6

 

 

 

CCVPD

0

 

EFWD-D4.5 (MPa)

0 50 100 150 200 250

CCVPD

0

4

8

12

16

20

 

 

 

CCVPD = 2.03 + 0.07 EFWD-D4.5

R2 = 0.82

n = 28

 

 

 

 

 

 

 

 

 

 

 

 

CCVPD

0

 

EFWD-D4.5 (MPa)

0 50 100 150 200 250

CCVPD

0

4

8

12

16

20

 

 

 

CCVPD = 2.03 + 0.07 EFWD-D4.5

R2 = 0.82

n = 28

 

 

 

 

 

 

 

 

 

 

 

 

TX (a = 0.8 mm,

f = 35Hz, and v = 3.2 km/h)

 

 

 

 

 

EFWD-D3 (MPa)

10 20 30 40 50 60 70

kSIPD(MN/m)

0

5

10

15

20

25

30

 

 

 

 

 

R2

 = 0.63

n = 53 

 

TB1

 

 

 

 

 

EFWD-D3 (MPa)

10 20 30 40 50 60 70

kSIPD(MN/m)

0

5

10

15

20

25

30

 

 

 

 

 

R2

 = 0.63

n = 53 

 

TB1

TX (a = 1.1 mm,

f = 30Hz, and v = 3.5 km/h) TX

EFWD-D3 (MPa)

0 200 400 600 800 100012001400

kSISD(MN/m)

0

20

40

60

80

 Stabilized Subgrade

 

 

R2

 = 0.37

n = 11

 

 

 

 

R2

 = 0.73

n = 15

R2

 = 0.91

n = 26

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

EFWD-D3 (MPa)

0 200 400 600 800 100012001400

kSISD(MN/m)

0

20

40

60

80

 Stabilized Subgrade

 

 

R2

 = 0.37

n = 11

 

 

 

 

R2

 = 0.73

n = 15

R2

 = 0.91

n = 26

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 EFWD-D3 (MPa)

0 200 400 600 800 100012001400

kSISD(MN/m)

0

20

40

60

80

 Stabilized Subgrade

 

 

R2

 = 0.37

n = 11

 

 

 

 

R2

 = 0.73

n = 15

R2

 = 0.91

n = 26

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

TX

 

 

EFWD-D3 (MPa)

40 80 120 160 200

kS PD I (MN/m)

10

15

20

25

30

35

40

 

 

 

 

 

 

 

R2

 = 0.64

n = 18

 

 

 

TB5

 

 

EFWD-D3 (MPa)

40 80 120 160 200

kS PD I (MN/m)

10

15

20

25

30

35

40

 

 

 

 

 

 

 

R2

 = 0.64

n = 18

 

 

 

TB5

TX

 

 

EFWD-D3 (MPa)

10 20 30 40 50 60 70

kSISD(MN/m)

0

5

10

15

20

25

30

 

 

 

 

 

 

 

 

 

R2

 = 0.75

n = 53

 

 

 

TB1

 

 

EFWD-D3 (MPa)

10 20 30 40 50 60 70

kSISD(MN/m)

0

5

10

15

20

25

30

 

 

 

 

 

 

 

 

 

R2

 = 0.75

n = 53

 

 

 

TB1

EFWD-D3 (MPa)

0 50 100 150 200 250

MDP*

60

80

100

120

140

160

TB4

TB6

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB4: MDP* = 125.4 (EFWD-D3)

0.03

R2 = 0.39, n = 27

TB6: MDP* = 97.35 (EFWD-D3)

0.09

R2 = 1.00, n = 2

Combined:

MDP* = 122.34 (EFWD-D3)

0.04

R2 = 0.45, n = 29

 

 

 

 

 

 

 

 

 

 

 

EFWD-K3 (MPa)

0 50 100 150 200 250

MDP*

60

80

100

120

140

160

TB4

TB5

TB6

TB4: MDP* = 133.0 (EFWD-K3)

0.02

R2

 = 0.19, n = 39

TB5: MDP* = 102.6 (EFWD-K3)

0.07

R2 = 0.31, n = 30

TB6: R2

 = 0.00, n = 4

Combined:

R2

 = 0.00, n = 73

 

 

 

 

 

 

ND, Salvage base (a = 0.90 mm and f = 30 Hz) 

Figure 93. ICMVs vs. FWD measurements.

Correlation of ICMV to Ev1 and Ev2 of Plate Loading Tests

• Figure 94 presents the correlation results between ICMVs and LPD measurement of Ev1 

and EV2. The main conclusions are summarized as follows:
126

• ICMV increases with increasing Ev1 and Ev2 as expected, but generally they show a 

poorer correlation than that between ICMVs and LWD and FWD measurements (e.g. for 

the MS TBs 1,2, 4 CCV with a R2 value of 0.18 only); 

• Dependent on the specific test strip and materials, either the direct linear, or the 

logarithmic scaled linear function may achieve better correlation; 

• There is no obvious evidence to show that the machine settings (vibration frequency, 

amplitude, operating speed) will affect the correlation quality;

• Ev1 and Ev2 have different correlation parameters with ICMVs.
127

EV1 and EV2 (MPa)

0 200 400 600 800

CCV

0

5

10

15

20

25

EV1

EV2

 

 

 

 

 

 

 

 

 

 

CCV = 0.02 EV1 + 6.2; R2

 = 0.40

CCV = 0.01 EV2 + 6.3; 

R2

 = 0.18

n = 56

 TBs 1, 2, 4

 

 

 

 

 

 

 

 

MS

EV1 and EV2 (MPa)

0 200 400 600 800

MDP40

80

100

120

140

160

EV1

EV2

TBs 4,7

 

 

 

MDP40 = 80.0 (EV1)

0.12

R2

 = 0.49

MDP40 = 146.9 (1-e(-0.02 EV2)

); 

R2

 = 0.78

n = 42

 

 

 

 

 

 

 

MS

EV1 and EV2 (MPa)

0 200 400 600 800

CMV

0

10

20

30

40

50

EV1

EV2

 

 

 

 

 

 

 

 

CMV = 0.12 EV1 + 3.5; R2

 = 0.64

CMV = 0.03 EV2 + 8.6; R2

 = 0.24

n = 42

TBs 4,7

 

 

 

MS

100

EV1 and EV2 (MPa)

0 200 400 600 800

ks (MN/m)

0

20

40

60

80

100

EV1

EV2

 

 

 

 

 

 

 

 

 

 

 

 

ks = 15.7 (EV1)

0.22; R2

 = 0.68

ks = 9.2 (EV2)

0.28 ; R2

 = 0.52

n = 63

 TBs 3, 4, 5, 7

 

 

 

 

 

 

 

MS

KS (a = 2.19 mm, f = 26 Hz) 

EV2(MPa)

0 40 80 120 160

CCVPD

0

4

8

12

16

20

EV1(MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

CCV

 

 

 

 

 

 

 

 

CCVPD = 2.80 + 0.06 EV1

R2 = 0.56

n = 15

CCVPD = 2.84 + 0.03 EV2

R2 = 0.58

n = 15

 

 

 

KS (a = 0.93 mm, f = 33 Hz) 

EV2(MPa)

0 40 80 120 160

CCVSD

0

4

8

12

16

20

EV1(MPa)

0 10 20 30 40 50 60

CCVSD

0

4

8

12

16

20

 

 

 

 

 

 

CCVSD = 0.57 + 0.17 EV1

R2

 = 0.73

n = 15

CCVSD = 1.05 + 0.08 EV2

R2 = 0.65

n = 15

KS (a = 1.48 mm, f = 26 Hz) 

EV2(MPa)

0 40 80 120 160

CCVPD

0

4

8

12

16

20

 

EV1(MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

CCVPD

 

 

 

 

 

 

 

 

 

CCVPD = -0.24 + 0.24 EV1

R2 = 0.80

n = 15

CCVPD = 0.41 + 0.11 EV2

R2 = 0.72

n = 15

 

 

 

EV2(MPa)

0 40 80 120 160

CCVPD

0

4

8

12

16

20

 

EV1(MPa)

0 10 20 30 40 50 60

CCVPD

0

4

8

12

16

20

CCVPD

 

 

 

 

 

 

 

 

 

CCVPD = -0.24 + 0.24 EV1

R2 = 0.80

n = 15

CCVPD = 0.41 + 0.11 EV2

R2 = 0.72

n = 15

 

 

 

TX (a = 0.8 mm, f = 35Hz, and v = 3.2 km/h) TX (a = 1.1 mm, f = 30Hz, and v = 3.5 km/h)

TX TX

EV1 (MPa)

0 100 200 300 400

kSISD(MN/m)

0

20

40

60

80

 

 

 

 

 

R2

 = 0.48

n = 8

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

EV1 (MPa)

0 100 200 300 400

CMV

0

50

100

150

200

 

Flex B

 

 

 

 

R2

 = 0.16

n = 8

 

 

 

 

 

 

TB2

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2

EV2 (MPa)

100 200 300 400 500 600 700

10

15

20

25

30

35

40

 

 

EV1 (MPa)

50 100 150 200 250

10

15

20

25

30

35

40

 

 

 

 

 

 

R2

 = 0.53

n = 16

 

R2

 = 0.53

n = 16

 

 

 

 

 

TB5

TB5

TX TX TX TX

EV1 (MPa)

0 100 200 300 400

kSISD(MN/m)

0

20

40

60

80

 

 

 

 

 

R2

 = 0.48

n = 8

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

EV1 (MPa)

0 100 200 300 400

kSISD(MN/m)

0

20

40

60

80

 

 

 

 

 

R2

 = 0.48

n = 8

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TB2

EV1 (MPa)

0 100 200 300 400

CMV

0

50

100

150

200

 

Flex B

 

 

 

 

R2

 = 0.16

n = 8

 

 

 

 

 

 

TB2

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

EV1 (MPa)

0 100 200 300 400

CMV

0

50

100

150

200

 

Flex B

 

 

 

 

R2

 = 0.16

n = 8

 

 

 

 

 

 

TB2

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2

EV2 (MPa)

100 200 300 400 500 600 700

10

15

20

25

30

35

40

 

 

EV1 (MPa)

50 100 150 200 250

10

15

20

25

30

35

40

 

 

 

 

 

 

R2

 = 0.53

n = 16

 

R2

 = 0.53

n = 16

 

 

 

 

 

TB5

TB5

2

EV2 (MPa)

100 200 300 400 500 600 700

10

15

20

25

30

35

40

 

 

EV1 (MPa)

50 100 150 200 250

10

15

20

25

30

35

40

 

 

 

 

 

 

R2

 = 0.53

n = 16

 

R2

 = 0.53

n = 16

 

 

 

 

 

TB5

TB5

TX TX

EV2 (MPa)

20 40 60 80 100

0

5

10

15

20

25

30

 

 

 

 

 

EV1 (MPa)

10 15 20 25 30 35 40

0

5

10

15

20

25

30

 

 

R2

 = 0.72

n = 18 R2

 = 0.50

n = 18

 

 

 

TB1 TB1

EV2 (MPa)

20 40 60 80 100

0

5

10

15

20

25

30

 

 

 

 

 

EV1 (MPa)

10 15 20 25 30 35 40

0

5

10

15

20

25

30

 

 

R2

 = 0.72

n = 18 R2

 = 0.50

n = 18

 

 

 

TB1 TB1

EV2 (MPa)

20 40 60 80 100

0

5

10

15

20

25

30 d

EV1 (MPa)

10 15 20 25 30 35 40

0

5

10

15

20

25

30

 

 

R2

 = 0.67

n = 18

 

R2

 = 0.45

n = 18

 

 

 

 

 

TB1 TB1

EV2 (MPa)

20 40 60 80 100

0

5

10

15

20

25

30 d

EV1 (MPa)

10 15 20 25 30 35 40

0

5

10

15

20

25

30

 

 

R2

 = 0.67

n = 18

 

R2

 = 0.45

n = 18

 

 

 

 

 

TB1 TB1

Figure 94. ICMVs vs. Plate Loading Tests.
128

Correlation of ICMV to DCP measured CBR

Figure 95 presents the correlation results between ICMVs and penetration measured CBR 

values. The main conclusions are summarized as follows:

• ICMV increases with increasing CBR as expected, and overall they show relatively 

poorer correlation than that between ICMVs and ELWD and EFWD. For some projects, 

relatively good correlations are achieved such as the MS TBs 4 and 7, while for some 

other projects relatively poorer correlations are achieved such as KS CCVPD (R2 of 

0.13); 

• Dependent on the specific test strip and materials, either the direct linear, or the 

logarithmic scaled linear function may achieve better correlation than the other.

NY (a = 0.90 mm, f = 30 Hz, and v = 4 km/h)

 

 

 

 

0

 

 

CBR (%)

0 50 100 150 200 250

0

50

100

150

200 MDP40 = 21.91 ln (CBR) + 34.8

R2

 = 0.62, n = 68

Data includes 

CBR300 and CBRBase

 

 

 

 

 

TBs 1, 2, 4, 10: 

a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

 

 

 

 

MPD

40

 

 

 

 

0

 

 

CBR (%)

0 50 100 150 200 250

0

50

100

150

200 MDP40 = 21.91 ln (CBR) + 34.8

R2

 = 0.62, n = 68

Data includes 

CBR300 and CBRBase

 

 

 

 

 

TBs 1, 2, 4, 10: 

a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

 

 

 

 

MPD

40

0

 

 

 

 

CBR (%)

0 50 100 150 200 250

0

20

40

60

80

100

CMV = 8.69 CBR)0.38

R2 = 0.37, n = 15

TBs 1 and 2: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

CMV

0

 

 

 

 

CBR (%)

0 50 100 150 200 250

0

20

40

60

80

100

CMV = 8.69 CBR)0.38

R2 = 0.37, n = 15

TBs 1 and 2: a = 0.90 mm, 

f = 30 Hz, v = 4 km/h

CMV

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CBR (%)

0 50 100 150 200

CCV

0

5

10

15

20

25

TB2

TBs 1, 4

CCV = 0.04 CBR + 6.4

R2

 = 0.26

n = 60

TB2: Compaction layer ~150 mm

TBs 1, 4:Compaction layer ~300 mm

MS

 

 

 

 

 

 

 

 

 

 

 

 

 

CBR300 (%)

0 50 100 150 200

MDP40

80

100

120

140

160

TBs 4,7

MDP40 = 147.9 (1-e(-0.16 CBR300))

R2

 = 0.76

n = 44

MS

d ( )

 

 

 

 

 

 

 

 

 

 

 

 

CBR300 (%)

0 50 100 150 200

CMV

0

10

20

30

40

50

TBs 4,7

CMV = 0.18 CBR300 + 6.1

R2

 = 0.65

n = 46

MS

d (/ )

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CBR (%)

0 50 100 150 200

CCV

0

20

40

60

80

100

TBs 3,5

TBs 4,7

ks = 25.3 (CBR)0.15

R2

 = 0.44

n = 95

TBs 3,5: Compaction layer ~150 mm

TBs 4,7:Compaction layer ~300 mm

MS

0

CBR (%)

0 10 20 30 40

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CCVPD = 3.28 + 0.02 CBR

R2

 = 0.13

n = 21

KS (a = 0.93 mm, f = 33 Hz) 

 

 

 

 

CBR (%)

0 10 20 30 40 50

MDP80

110

120

130

140

150 MDP80 = 123.0 + 0.35 CBR

R2

 = 0.30

n = 62

 

 

 

KS (static, driving uphill)

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

CBR (%)

0 5 10 15 20

0

5

10

15

20

25

30

 

 R2

 = 0.40

n = 81

 

 

 

 

 

 

 

 

 

 

TX (a = 0.8 mm, 

f = 35Hz, and v = 3.2 km/h)

TB1

CBR (%)

0 5 10 15 20

0

5

10

15

20

25

30

 

 R2

 = 0.37

n = 17 

 

 

 

 

 

 

 

 

 

 

 

 

TX (a = 1.1 mm, 

f = 30Hz, and v = 3.5 km/h)

CBR (%)

0 5 10 15 20

10

15

20

25

30

35

40

2

2

3

3

4

 

 R2

 = 0.59

n = 27

 

 

 

 

 

 

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 TB5 

CBR (%)

0 5 10 15 20

10

15

20

25

30

35

40

2

2

3

3

4

 

 R2

 = 0.59

n = 27

 

 

 

 

 

 

 

 

 

 

 

 

 

 

80

Lime Stabilized Subgrade

Flex Base

 

 

 

 

 

 

 

 

 

 

 

 

 TB5 

TX (a = 1.0mm,

f = 35Hz, and v = 3.2km/h)

CBR250 (%)

0 10 20 30 40 50 60

MDP*

60

80

100

120

140

160

 

 

 

MDP* = 0.84 CBR + 103.9

R2

 = 0.17

n = 45

 

 

 

 

 

ND (static)

Figure 95. ICMVs vs. DCP tests.
129

Correlation of ICMV to NG dry unit density

Figure 96 presents the correlation results between ICMVs and the NG dry unit density rd. 

The main conclusions are summarized as follows:

• ICMV increases with increasing rd as expected, and overall a poorer correlation is 

achieved than that of ELWD, EFWD, and EV1 and EV2;

• Dependent on the specific test strip and materials, either the direct linear, or the 

logarithmic scaled linear function may achieve better correlation; 

• For some cases, significant scatter in the relationships is shown (e.g. MS TBs1, 2, 4 

CCV, and KS TB1 and TB2 MDP80). These values are likely influenced by different 

material type encountered and narrow range of MDP80 values on each material type.

• Different materials show different correlation results and variation trends (e.g. KS TB3 

foundation shale and clay materials). These separate trends could be a result of 

differences in the underlying support, material, and moisture conditions. 
130

d (kN/m3

)

14 15 16 17 18 19 20

CCV

0

5

10

15

20

25

 

 

 

 

 

R2

 = 0.00

n = 62

 

 

 

 

 

 TBs 1, 2, 4

 

 

 

 

 

 

 

MS d (kN/m3

)

14 15 16 17 18 19 20

MDP40

80

100

120

140

160

 

TBs 4,7

 

 

 

 

 

 

 

 

MDP40 = 3.0 (d)

1.34

R2 = 0.49; n = 46

 

 

 

 

 

MS

d (kN/m3)

14 15 16 17 18 19 20

CMV

0

10

20

30

40

50

 

CMV = 3.91 d - 52.4

R2

 = 0.21

n = 46

 

 

TBs 4,7

 

 

 

MS

d (kN/m3

)

14 15 16 17 18 19 20

ks (MN/m)

0

20

40

60

80

100

V1 V2 ()

0

 

 

 

 

 

ks

 = 4.54 (d) - 36.0

R2

 = 0.30

n = 97

 

 

 

 

 

 TBs 3, 4, 5, 7

 

 

 

 

 

 

KS (a = 2.19 mm, f = 26 Hz) KS (a = 0.93 mm, f = 33 Hz) 

 

 

 

d (kN/m3

)

10 12 14 16 18 20

MDP80

110

120

130

140

150

n = 62

0

 

 

 

 

 

 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

KS (static, driving uphill ) KS (a=0.9mm,f=33Hz,driving uphill ) 

 

 

 

d (kN/m3

)

10 12 14 16 18 20

MDP80

110

120

130

140

150

MDP80 = 106.8 + 1.61 d

R2

 = 0.61

n = 15

0

 

 

 

 

 

 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

 

 

 

d (kN/m3

)

10 12 14 16 18 20

MDP80

110

120

130

140

150

MDP80 = 106.8 + 1.61 d

R2

 = 0.61

n = 15

0

 

 

 

 

 

 

50

TB2

TB1

 

 

 

 

 

 

 

0

 

 

 

 

 

d (kN/m3)

10 12 14 16 18 20

MDP40

30

60

90

120

150

 

 

 

MDP40 = -167.7 + 16.45 d

R2

 = 0.72

n = 11

KS (a=0.9mm, f=33Hz) 

MS

d (kN/m3)

14 16 18 20

CCVPD

0

4

8

12

16

20

TB1

TB4

TB6

TB7

50

 

 

 

 

 

 CCVPD = -4.30 + 0.47 d

R2 = 0.40

n = 21

 

 

 

 

 

 

 

 

 

d (kN/m3)

14 16 18 20

CCVPD

0

4

8

12

16

20

TB1

TB4

TB6

TB7

50

 

 

 

 

 

 CCVPD = -4.30 + 0.47 d

R2 = 0.40

n = 21

 

 

 

 

 

 

 

 

 

d (kN/m3

)

14 15 16 17 18 19 20 21

CCVPD

0

4

8

12

16

20

TB1

TB3 (Lift 4)

TB4

TB6

TB7

50

 

 

 

CCVPD = -13.52 + 1.09 d

R2

 = 0.61

n = 40

 

 

 

 

 

 

ccvPD

d (kN/m3

)

14 15 16 17 18 19 20 21

CCVPD

0

4

8

12

16

20

TB1

TB3 (Lift 4)

TB4

TB6

TB7

50

 

 

 

CCVPD = -13.52 + 1.09 d

R2

 = 0.61

n = 40

 

 

 

 

 

 

ccvPD

 

 

 lay

 

 

 

 

 

 

d (kN/m3)

10 12 14 16 18 20

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = -3.97 + 0.43 d

R2

 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = -45.84 + 2.90 d

R2 = 0.76, n = 8

 

 

 

 

TB3 Lift 1 (Shale)

TB3 Lift 6 (Shale)

TB3 Foundation Clay

TB3 Lift 1 (Clay)

TB3 Lift 2 (Clay)

TB3 Lift 6 (Clay)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 lay

 

 

 

 

 

 

d (kN/m3)

10 12 14 16 18 20

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = -3.97 + 0.43 d

R2

 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = -45.84 + 2.90 d

R2 = 0.76, n = 8

 

 

 

 

 

 

 lay

 

 

 

 

 

 

d (kN/m3)

10 12 14 16 18 20

CCVPD

0

4

8

12

16

20

 

 

 

 

 

 

 

TB3 Foundation Clay, 

Lifts 1, 2, and 6 (Clay):

CCVPD = -3.97 + 0.43 d

R2

 = 0.69, n = 16

 

 

 

 

 

 

 

 

 

 

 

 

 

TB3 Lifts 1 and 6 (Shale):

CCVPD = -45.84 + 2.90 d

R2 = 0.76, n = 8

 

 

 

 

TB3 Lift 1 (Shale)

TB3 Lift 6 (Shale)

TB3 Foundation Clay

TB3 Lift 1 (Clay)

TB3 Lift 2 (Clay)

TB3 Lift 6 (Clay)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

KS (a=2.19mm, f=26Hz) 

d (kN/m3)

12 13 14 15 16 17 18

0

5

10

15

20

25

30

 

R2

 = 0.33

n = 75

 

 

 

 

TX (a = 0.8 mm,

f = 35Hz, and v = 3.2 km/h)

TB1

40

0

d (kN/m3)

12 13 14 15 16 17 18

10

15

20

25

30

35

40

 

 

 

R2

 = 0.21

n = 45

 

 

 

 

 

 

 

 

 

 

 

 

TX (a = 1.0mm, 

f =35Hz, and v = 3.2 km/h)

 

 

 

 

 

 

d (kN/m3)

12 14 16 18

MDP*

60

80

100

120

140

160

MDP* = 4.74 d + 44.5

R2

 = 0.15

n = 45

 

 

ND (static)

γd (kN/m3)

18.0 18.5 19.0 19.5 20.0 20.5 21.0

MDP*

60

80

100

120

140

160

TB4

TB6

 

 

 

 

 

 

 

 

 

R2

 = 0.00

 

 

 

 

 

 

 

 

 

 

 

 

 

ND, salvaged base 

(a = 0.90 mm and f = 30 Hz) 

Figure 96. ICMVs vs. NG densities.

Multivariate Correlation

ICMV is potentially influenced by various factors, and therefore a multivariate analysis is 

warranted to study these effects. Here multiple regression analysis is performed to statistically 

assess the influence of soil moisture content and vibration amplitude.

The selected criteria for identifying the significance of a parameter included: p-value < 0.05 = 

significant, < 0.10 = possibly significant, > 0.10 = not significant, and t-value < -2 or > +2 = 

significant. The p-value indicates the significance of a parameter and the t-ratio value indicates 

the relative importance (i.e., higher the absolute value greater the significance).
131

Table 11 summarized the multiple regression analysis of CCVPD and MDP80 for the KS 

demonstration project. Analysis results show that amplitude is statistically significant in relating 

all in-situ point measurement values (ELWD-Z2, EFWD-D4.5, γd, CBR, EV1, and EV2). For CCVPD,

moisture content w is statistically significant for ELWD-Z2, γd, and CBR (note that limited w 

measurements were available at other point measurement locations). For MDP80 w is statistically 

significant for γd. Table 12 summarized multiple regression analysis of EVIB for the NY 

demonstration project. Results indicate that amplitude is significant for EVIB relationships with insitu measurements (ELWD-Z3, EBCD, and γd(H)) and also contribute to increased R2 values.

However, it should be noted that the intercept of multivariate regression was not always 

statistically significant. Considering the NY project EFWD-D4.5 for example, the R2 value with 

amplitude incorporated in the model showed an R2 = 0.68 which is lower than R2 values obtained 

from the univariate linear regression analysis separating results from different amplitudes (R2

= 

0.69 and 0.82). This is important to note and in such cases, it is appropriate to interpret the 

relationships separately for different amplitude settings, instead of combining the results.

Nevertheless, it is recommended that all measurements obtained from calibration areas and 

production areas during QA should be obtained at a constant amplitude setting to avoid 

complication in data analysis and interpretation.
132

Table 11. KS, results of multiple regression analysis for influence of amplitude and 

moisture content on CCVPD – TBs 1, 3(lift4), 4, 6, and 7 and MDP80 – TBs 1 and 2

b0 2.27 1.13 1.99 0.05

b1 0.1 0.02 5.97 <0.0001

b2 1.36 0.2 6.89 <0.0001

b3 -0.16 0.05 -3.09 0.0031

b0 -0.07 0.62 -0.11 0.92

b1 0.04 0.01 9.38 <0.0001

b2 1.64 0.33 4.94 <0.0001

b0 -10.47 3.16 -3.32 0.0016

b1 0.85 0.15 5.74 <0.0001

b2 0.86 0.2 4.31 <0.0001

b3 -0.06 0.06 -2 0.098

b0 3.6 1.36 2.64 0.012

b1 0.07 0.02 3.94 <0.0001

b2 1.15 0.22 5.27 <0.0001

b3 -0.13 0.08 -2.1 0.099

b0 -1.18 1.25 -0.95 0.35

b1 1.57 0.62 2.55 0.017

b2 0.15 0.02 5.95 <0.0001

b0 -0.84 1.29 -0.65 0.52

b1 1.58 0.65 2.44 0.0217

b2 0.07 0.01 5.47 <0.0001

b0 115.32 1.07 107.5 <0.0001

b1 0.51 0.05 9.99 <0.0001

b2 2.5 0.43 5.81 <0.0001

b0 89 9.21 9.67 <0.0001

b1 1.54 0.43 3.6 0.0006

b2 2.45 0.62 3.96 0.0002

b3 0.84 0.25 3.37 0.0011

b0 123.21 1.22 101 <0.0001

b1 0.34 0.1 3.57 0.0016

b2 4.45 1.75 2.55 0.0178

MDP80 

*

= b0 + b1 CBR + b2 a 0.5

MDP80

*

 = b0 + b1 ELWD-Z2 + b2 a 0.7

MDP80 = b0 + b1 γd + b2 a + b3 w 0.4

CCVPD

† = b0 + b1 EV1 + b2 a 0.6

Estimate

CCVPD = b0 + b1 CBR + b2 a + b3 w 0.6

CCVPD

† = b0 + b1 EV1 + b2 a 0.6

CCVPD

† = b0 + b1 EFWD-D4.5 + b2 a 0.7

CCVPD = b0 + b1 γd + b2 a + b3 w 0.8

P value R2

CCVPD = b0 + b1 ELWD-Z2 + b2 a + b3 w 0.8

Model Term Std Error t Ratio

a: amplitude, w: moisture content
133

Table 12. NY, Summary of multiple regression analysis – TB10 lane 2

b0 61.69 21.17 2.91 0.0051

b1 1.19 0.22 5.51 <0.0001 0.4

b2 -17.94 11.92 -3.51 0.0374

b0 283.02 46.15 6.13 <0.0001

b1 1.27 0.35 3.66 0.0008 0.7

b2 -238.72 41.55 -5.75 <0.0001

b0 -330.3 101.86 -3.24 0.0022

b1 24.72 5.42 4.56 <0.0001 0.4

b2 -29.64 11.7 -2.53 0.0146

b0 87.46 24.42 3.58 0.0009

b1 0.68 0.21 3.2 0.0027 0.3

b2 -16.86 15.71 -1.07 0.2899

b0 133.55 36.63 3.65 0.0065

b1 0.29 0.09 3.25 0.0117 0.6

b2 -47.35 33.51 -1.41 0.1954

Estimate Std Error t Ratio P value R2

EVIB = b0 + b1 ELWD-Z3 + b2 a

EVIB = b0 + b1 EBCD + b2 a

EVIB = b0 + b1 γd(H) + b2 a

EVIB = b0 + b1 CBR300 + b2 a

EVIB = b0 + b1 EFWD-K3 + b2 a

Model Term
134

Chapter 7 Demonstration Projects for HMA IC

This chapter summarizes the results from HMA IC demonstrations under the FHWA/TPF IC project.

State Demonstration Results

Minnesota IC Demonstration

This is the first demonstration and HMA compaction project of this FHWA TPF project. The Sakai 

SW880 double-drum vibratory roller was used for three highway construction projects conducted in June 

2008. The demonstration activities include mapping the subbase layer and paving new HMA base (see 

Figure 97). The following demonstrations of IC technology were conducted on these projects:

• TH 71 (Renville County) – mapping of asphalt overlay,

• Route 40 (Kandiyohi County) – mapping of asphalt overlay, and

• Route 4 (Kandiyohi County) – mapping of the subbase and asphalt base course and wearing 

course layers.

The goals of this demonstration project that were successfully accomplished included:

• Demonstration of Hot Mix Asphalt (HMA) IC technology (specifically, tracking rollers passes 

and HMA surface temperatures) to Mn/DOT personnel, contractors, etc.,

• Evaluation of the benefits and effectiveness of IC rollers vs. conventional rollers,

• Assisting Mn/DOT in the development of IC quality control (QC) specifications for HMA 

pavement materials, and

• Identification and prioritization of needed improvements and further research for IC equipment.
135

Subbase HMA base

Figure 97. Minnesota HMA IC demonstration.

Major Findings

• IC mapping of the existing base for new HMA construction was shown to be crucial in 

identifying weak areas prior to the compaction of the asphalt layers.

TH 71 Asphalt Overlay

• This project successfully demonstrated the capabilities of the Sakai IC roller to track roller passes 

and asphalt surface temperatures using the Sakai CIS system with the Trimble GPS base station 

for an asphalt overlay.

Route 40 Asphalt Overlay

• This project demonstrated how the roller operator was able to significantly improve the rolling 

pattern by monitoring the color-coded map on the Sakai CIS display, then adjusting the passes to 

meet the requirements. Figure 98 shows the rolling pattern before and after the roller operator 

was trained to make use of the Sakai CIS.
136

Before After

Number of roller passes

Figure 98. The rolling pattern before and after the roller operator made use of the Sakai 

CIS.

Route 4 New Asphalt Pavement Construction

This project successfully demonstrated the ability of the IC roller to map the compaction of the subbase, 

asphalt base course, and asphalt wearing course. The important findings of this project included:

• The Sakai Compaction Control Value (CCV) displayed on the screen allowed the operator to see 

in real time the relatively softer and stiffer areas of the entire roadway during compaction.

Figure 99. Comparison of CCVs of HMA base course layer and subbase layer mapping.

• The roller operator was able to identify changes in the asphalt mixture when noticing changes in 

the values shown on the display.

• CCV measurements obtained on the subbase layer at the 0.6 mm (0.024 in.) amplitude setting 

better distinguished the hard/soft spots compared to CCV measurements obtained at 0.3 mm

(0.012 in.) amplitude setting.
137

HMA Subbas

Subbase Layer 

Map, a = 0.6 mm

f = 2500 vpm

Subbase Layer 

Map, a = 0.3mm

f = 3000 vpm

Figure 100. CCVs of HMA base course layer and subbase layer mapping.

• The surface temperature measurements from the roller dot sensor and thermal camera are 

consistent.

• A pavement section with premature failure of the asphalt base course was verified to be within a 

weak spot identified during mapping of the subbase using lower frequency (2,500 vpm) and 

higher amplitude (0.6 mm). This area is located within the area where the subgrade layer

reportedly “failed” under the test rolling in summer 2007, and is an area identified on NRCS soil 

survey maps consisting of peat/muck soils.

Figure 101. Comparison of CCV measurements on HMA base course layer and subbase 

layer (5m moving average).

• The lower frequency and higher amplitude settings for mapping the existing base were found to 

be highly correlated (reflected) to the asphalt mapping on top of the asphalt base course layer.
138

Figure 102. Comparison of CCV maps of the HMA base course and subbase.

• A strong natural logarithmic relationship between CCV measurements of subbase and HMA base 

layers at a vibration amplitude of 0.6 mm with R2

-value of 0.69. CCV measurements obtained on 

the subbase layer at an amplitude of 0.3 mm show relatively poor correlation to CCV 

measurements on the HMA layer with a linear relationship; A strong linear correlation is found 

for the CCVs between the HMA base course and wearing course (in-situ test spots were used); 

y = 0.4374x + 2.5062

R2

 = 0.8583

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

CCV_WC

CCV_BC (%)

y = 0.3259x + 3.4585

R2

 = 0.8376

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

CCV_SB1

CCV_BC (%)

Figure 103. Correlation of CCVs between different layers.
139

• For the subbase layer, the machine setting at lower frequency and higher amplitude (i.e., 2,500 

vpm, 0.6 mm) produces a wider range of CCVs (thus covering a wider ranges of conditions, 

such as softer and harder spots) and higher correlation with LWD and DCP. Note that the 

correlation between LWD and DCP (R2 = 0.48) is at a similar level as those for CCV vs. LWD 

(R2 = 0.59) and CCV vs. DCP (R2 = 0.43). The correlations between DCP and CCV at high 

frequency and lower amplitude are particularly poor (the IC mapping was performed one day 

prior to the LWD and DCP tests, and the on-site moisture contents at the correlation test locations 

ranged from 4.3% to 6.1% which corresponded to soil moduli from 150 to 60 MPa).

y = 4.874x + 58.673

R2

 = 0.588

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25

CCV

E(LWD) (MPa)

y = 0.5275x - 16.028

R2

 = 0.4795

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

100 120 140 160 180 200 220 240 260 280 300

E(LWD) (MPa)

E(DCP) (MPa)

y = 5.4872x + 172.11

R2

 = 0.4239

0

50

100

150

200

250

300

0 5 10 15 20 25

CCV

E(DCP) (MPa)

y = 7.1857x + 12.509

R2

 = 0.4645

0

20

40

60

80

100

120

140

160

0 2 4 6 8 10 12 14 16 18 20

CCV

E(LWD) (MPa)

y = 4.851x + 159.47

R2

 = 0.1204

0

50

100

150

200

250

300

0 2 4 6 8 10 12 14 16 18 20

CCV

E(DCP) (MPa)

aggregate base mapping (3000 vpm, 0.3 mm)

aggregate base mapping (2500 vpm, 0.6 mm)

Figure 104. Correlation of CCVs with LWD and DCP tests on subbase.

• Both core density and lab density results (expressed in % Gmm) and the nuclear gauge readings 

correlate well (R2 = 0.94). Lab density (% Gmm) and nuclear gauge readings vs. CCV correlate 

well (R2 = 0.99 and 0.90 respectively) based on a 1 m (3.3 ft) radius averaging of the roller data 

(the latter is based on a second order polynomial fit). A linear or non-linear relationship may 

exist when correlating the material density with modulus (or modulus-type values). However, the 

above “correlation” is based on a fairly limited number of data points and one should be cautious 

to the interpretation of the results.
140

y = 1.5375x + 84.921

R2

 = 0.9979

90.0

91.0

92.0

93.0

94.0

95.0

96.0

3 3.5 4 4.5 5 5.5 6 6.5 7

CCV

%Gmm

y = -2.2024x2 + 26.313x + 68.054

R2

 = 0.904

100.00

110.00

120.00

130.00

140.00

150.00

160.00

3 4 5 6 7 8 9

CCV

Nuclear Gauge Density (pcf)

y = -0.0412x2 + 0.5633x + 91.717

R2

 = 0.143

90.0

91.0

92.0

93.0

94.0

95.0

96.0

3 4 5 6 7 8 9 10 11 12

CCV

%Gmm

 

 

y = 0.5475x + 15.56

R2

 = 0.9425

91.0

91.5

92.0

92.5

93.0

93.5

94.0

94.5

95.0

95.5

138.00 139.00 140.00 141.00 142.00 143.00 144.00 145.00 146.00

Nuclear Gauge Density (pcf)

Gmm (%)

 

 

Figure 105. Correlation between nuclear gauge test, lab core density results, and CCV for 

the HMA base course (Route 4, SB).

Mississippi IC Demonstration

The field IC demonstration was performed from July 13 to July 17, 2009 on US 84, Wayne County, MS 

(see Figure 106). The materials include the stabilized subbase and HMA base. 

The objectives of this demonstration project were short-term goals for introducing HMA IC technology to 

MSDOT and contractors who may not have prior experience with IC. The project was intended to 

demonstrate the benefits of IC for improving the compaction process by achieving more uniform 

density/modulus of the HMA material and providing roller operators (and superintendents) better 

feedback tools to make right decisions, and ultimately real-time quality control.
141

Figure 106. Mississippi HMA IC demonstration.

Main findings

• The Sakai IC double drum roller was used to successfully map the existing, tack-coated cementstabilized subbase at different cured stages. Relatively soft spots were easily identified with the 

IC maps.
142

TB 2A-2

N

TB 2B-1

TB 2C-1

TB 2A-3 TB 2A-1

TB 2A-1

TB 2A-2

TB 2A-3

TB 2B-2

TB 2C-2

TB 2B-1

TB 2C-1

TB 2B-2

TB 2C-2 0

5

10

15

20

25

TB02A (5-day cure) TB02B (6-day cure) TB02C (7-day cure)

CCVs

Figure 107. Mapping of tack-coated, stabilized subbase with the Sakai double-drum IC 

roller.

• The Sakai Compaction Control Value (CCV) displayed on the screen allowed the roller operator 

to observe real time information that identify relatively softer and stiffer areas of the roadway 

during compaction.
143

Roller pass

EB Lane 1 EB Lane 2

Sakai CCV

EB Lane 1 EB Lane 2

Figure 108. Roller pass No. and Sakai CCV of HMA base.

• The roller measurement values, ICMV, can be used to identify the weak or stiff areas of the 

pavement layers and the underneath support.
144

North

Softer spots

Harder spots

a) Sakai CCV for mapping TB2A subbase.

b) Kridging contour of surface temperature and Sakai CCV (TB24, HMA base course)

Figure 109. Sakai CCV of mapping subbase and paving HMA base course.

• Temperature plays an important role in determining the measurement values on HMA material.

Figure 110. Surface temperatures and Sakai CCVs of HMA base course (EB Lane 2).
145

• The ICMV of mapping subbase correlates well with that of fresh HMA base paving, which 

indicates that a stiff or soft layer underneath could significantly affect the compaction results for 

the upper layers and reflected by the ICMV values.

Figure 111. Comparison of Sakai ICMV among different layers (EB Lane 1, HMA base vs. 

Subbase).

• The FWD measured deflection and modulus correlate well with the ICMVs of subbase and HMA 

base course, indicating the reflecting effect of underneath layer on the stiffness of the upper layer.

y = -9206.8x + 308649

R2

 = 0.4498

00,000

50,000

100,000

150,000

200,000

250,000

300,000

350,000

0 5 10 15 20 25 30

Deflection at Sensor No.1

Back-calculated modulus of STB

y = 1E-05x + 7.1135

R2

 = 0.7457

6

7

8

9

10

11

12

00,000 50,000 100,000 150,000 200,000 250,000 300,000 350,000

STB modulus psi)

SAKAI CCV of EB LN2

CCV vs FWD deflection

Linear (CCV vs FWD deflection)

FWD deflection vs. Sakai CCV of subbase FWD modulis vs. Sakai CCV of HMA base

Figure 112. Sakai CCV vs FWD measurements.

• The ICMVs and nuclear gauge test results of asphalt density seem to have linear relationships 

while with relatively low correlation. It may be interpreted with multiple reasons including the 

influence zone of ICMV (the whole pavement system rather than a single HMA layer), effective 

temperature variation, roller passes, vibration amplitude and frequency, etc. Note that the factors 

affecting (achieving) HMA density may also include the mixture properties, construction 

machines, and etc., in additional to compaction.
146

• The Geostatistics of semi-variogram analysis indicates that the HMA base course has higher 

compaction uniformity than that of the subbase layer, which sounds reasonable since the 

compaction was improved from the lower subbase course to the upper HMA base course in order 

to achieve a more uniform compaction.

0 50 100 150 200 250 300

Lag Distance

0

0.5

1

1.5

2

2.5

Variogram

Direction: 0.0 Tolerance: 90.0

Column D

Exponential Model

Nugget=1.68

Sill = 2.2

Range = 30

0 20 40 60 80 100 120 140 160 180 200

Lag Distance

0

0.5

1

1.5

2

2.5

3

3.5

Variogram

Direction: 0.0 Tolerance: 90.0

Column D

Nugget=1.38

Sill = 2.2

Range = 35

Exponential Model

EB Lane 1 (400 to 582 m) 

Semi-variogram of CCV

EB Lane 1 (0 to 300 m) 

Sakai CCV

North 

Total length of 582 m

Figure 113. Geostatistic semi-variogram to access uniformity of asphalt pavement (US 84, 

EB Lane 1, Base course).

Findings confirming previous demo sites

The IC roller can effectively track the roller pass, HMA surface temperature, and the Roller Measurement 

Value (ICMV), which provide useful information for operators to control and adjust compaction efforts 

toward a more uniform compaction.

New York State IC Demonstration

The field IC demonstration was performed from May 17 to May 22, 2009 on US 219. The compaction 

layer and materials include the permeable HMA base, 2nd lift HMA base, and then the HMA binder 

course. The Sakai double-drum IC roller is used in this HMA demonstration project.

The objectives of this demonstration project were short-term goals for introducing HMA IC technology to 

NYSDOT and contractors who may not have prior experience with IC. The project was intended to 

demonstrate the benefits of IC for improving the compaction process by achieving more uniform 
147

density/modulus of the HMA material and providing roller operators (and superintendents) better 

feedback tools to make right decisions, and ultimately real-time quality control.

HMA permeable base

HMA 2nd lift base

Figure 114. New York State HMA IC demonstration.

Major Findings

• This project successfully demonstrated the ability of the IC roller to map the compaction of the 

subgrade, subbase, permeable asphalt base course, and asphalt binder course. The important 

findings of this project included:

• The roller measurement values, ICMV, can be used to identify the weak or stiff areas of the 

pavement layers and the underneath support.
148

Weaker areas

Stiffer areas

Roller pass Temperature Sakai CCV

Figure 115. Roller measurement values of HMA 2nd lift base.

• The ICMVs are affected by the vibration frequency, roller pass, amplitude, and HMA material 

temperatures.

• For the asphalt mixture compaction, the temperature plays an important role in determining the 

measurement values.
149

Figure 116. Sakai ICMVs and HMA temperatures of TB 15 - HMA base course (second 

lift).

• The measurement values between different pavement layers correlated well and have similar 

trends, which indicates that a stiff or soft layer underneath could significantly affect the 

compaction results for the upper layers and reflected by the ICMV values.
150

Figure 117. Comparison of Sakai ICMV among different layers.

• The ICMVs and nuclear/non-clear gauge test results of asphalt density have inconsistent trends. 

It may be interpreted with multiple reasons including the effective temperature variation, roller 

passes, vibration amplitude and frequency, and etc. Note that the factors affecting (achieving) 

HMA density may also include the mixture properties, construction machines, and etc. in 

additional to compaction.
151

2nd Lift Base

0

10

20

30

40

50

60

1EB2EB4EB6EB9EB

10EB11EB12EB13EB14EB16EB18EB19EB

Location

RMV

120

121

122

123

124

125

126

127

128

129

130

RMV

NNG

NNG density (pcf)

1st permeable HMA base course

2nd life HMA base course

Binder Course

5

5.5

6

6.5

7

7.5

8

8.5

9

EB8

EB10EB12EB14EB15EB18

Location

RMV

120

121

122

123

124

RMV

NNG

NNG density (pcf)

Binder Course

5

5.5

6

6.5

7

7.5

8

8.5

9

EB8

EB10EB12EB14EB15EB18

Location

RMV

121

122

123

124

125

126

127

128

129

130

131

132

133

134

RMV

NG

NG density (pcf)

HMA binder course

IC RMV vs NNG Density

0

2

4

6

8

10

12

1WB3WB4WB7WB8WB

10WB11WB12WB14WB16WB17WB20WB

Location

RMV

119.5

120

120.5

121

121.5

122

122.5

123

123.5

RMV

NNG density

NNG density (pcf)

Opposite direction of trends

IC RMV vs NNG Density

0

2

4

6

8

10

12

1WB3WB4WB7WB8WB

10WB11WB12WB14WB16WB17WB20WB

Location

RMV

119.5

120

120.5

121

121.5

122

122.5

123

123.5

RMV

NNG density

NNG density (pcf)

Opposite direction of trends

IC RMV vs NG Density

0

2

4

6

8

10

12

1WB3WB4WB7WB8WB

10WB11WB12WB14WB16WB17WB20WB

Location

RMV

128

130

132

134

136

138

140

142

144

RMV

NG density

Opposite direction of trends

NG density (pcf)

IC RMV vs NG Density

0

2

4

6

8

10

12

1WB3WB4WB7WB8WB

10WB11WB12WB14WB16WB17WB20WB

Location

RMV

128

130

132

134

136

138

140

142

144

RMV

NG density

Opposite direction of trends

NG density (pcf)

2nd Lift Base

0

4

8

12

16

20

24

28

32

36

1EB2EB4EB6EB9EB

10EB11EB12EB13EB14EB16EB18EB19EB

Location

RMV

129

133

137

141

145

149

RMV

NG

Figure 118. Sakai ICMV vs. NNG (left) and NG (right) density.

Maryland IC Demonstration

This test site is located on US 340 EB in Frederick County, MD (see Figure 119). This project is a 

grinding of existing HMA and then paving overlay with stone matrix asphalt (SMA). The objectives of 

this demonstration project were short-term goals for introducing HMA IC technology to MD SHA and 

contractors who may not have prior experience with IC technology. The project was intended to 

demonstrate the benefits of IC for improving the compaction process and quality by achieving more 

uniform density and modulus of the HMA material and providing roller operators (and superintendents) 

better feedback tools to make right decisions, and ultimately real-time quality control.
152

TB 03 TB 01

TB 02

Figure 119. Maryland HMA IC demonstration.

The unique features of this demonstration project included the following:

• The first night-paving project for the IC demonstration;

• Te first SMA overlay project for the IC demonstration;

• The first time to map the existing HMA pavements using IC rollers;

• The first time to use two IC rollers (Sakai and Bomag) to map the same existing HMA 

pavements;

• The first time to use Portable Seismic Pavement Analyzer (PSPA) to measure the seismic 

modulus of HMA pavement for correlation purpose;

• The first time to instrument the RFID device to measure the HMA overlay temperatures at 

different depths.

Major Findings
153

• This project has successfully demonstrated the ability of the IC roller to map the compaction of 

HMA overlay on the existing HMA pavements. The important findings of this project included:

• Night paving has been successfully implemented. IC technology helps the night paving by 

offering the visible roller measurements to the operator;

Sakai CCV Pass No.

Figure 120. Night paving of SMA overlay.

• Both Sakai and Bomag mappings of the existing HMA pavements have shown that the shoulder 

has much lower ICMVs (relative stiffness) than that of the pavement lane, and ICMVs from both 

machines can consistently identify soft or stiff support;
154

Test bed 02 Mapping

Bomag Evib Sakai CCV

Figure 121. Mapping results - Bomag Evib and Sakai CCV - TB 02.

• ICMVs correlate with the FWD measured deflection better than with PSPA seismic modulus;

y = -0.0633x + 9.3611

R2

 = 0.3585

0

2

4

6

8

10

12

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00

SAKAI CCV on Existing HMA Pavement

FWD measured deflection of No.1 sensor (10 -6 inch)

Series1

Linear (Series1)

y = 1.011x + 477.16

R2

 = 0.1289

300

350

400

450

500

550

600

650

0.00 20.00 40.00 60.00 80.00 100.00

SAKAI CCV on Existing HMA Pavement

PSPA Seismic modulus of existing HMA layer 

(ksi)

Modulus of Existing HMA Layer vs SMA Overlay CCV

Linear (Modulus of Existing HMA Layer vs SMA Overlay CCV)

Sakai CCV vs FWD deflection Sakai CCV vs PSPA moduli

Figure 122. Sakai CCV vs. FWD deflection and PSPA seismic modulus.

• The compaction uniformity has significantly improved from the existing HMA pavements to the 

fresh SMA overlay as approved by the ICMV map and the geostatistics semi-variograms;
155

TB 03A Mapping (0-416m)TB 03B Mapping (0-684m)

Figure 123. Semi-variograms of Sakai CCV for the mapping areas.

• The ICMVs and nuclear density gauge test results of HMA are shown to have a linear 

relationship with a relatively low correlation. Their relationship may be explained by the fact that 

a greater material stiffness under a higher compaction effort correlates to a greater material 

density, and vice versa. However, there are also other factors that may affect the above 

relationship. To name a few: HMA temperature variation, roller passes, vibration amplitude and 

frequency, etc. It should also be noted that the factors affecting HMA density may also include 

the mixture properties, construction machines, etc. in additional to compaction.

y = 0.2858x + 149.28

R2

 = 0.2031

140

145

150

155

160

165

0 5 10 15 20 25 30 35 40

SAKAI CCV

Density

Density vs CCV

Linear (Density vs CCV)

Figure 124. NG density vs Sakai CCV of TB 03 SMA overlay at discrete test locations.

Findings confirming previous demo sites

• The IC roller can track well the roller pass number, HMA surface temperature, and the Roller 

Measurement Value (ICMV), which provides important proof for the compaction quality;
156

• With the real-time information of IC roller pass, ICMVs displayed on the screen, the roller 

operator can adjust rolling pattern to improve the compaction quality;

• The ICMV mapping on the existing pavement layers can identify relatively softer and stiffer areas 

of the roadway;

• The ICMV can also identify the weak or stiff areas of the constructed HMA overlay and the 

whole pavement layer system;

• The HMA temperature affects the ICMVs or stiffness due to the temperature-dependent 

viscoelastic property of HMA material.

Georgia IC Demonstration

This is a demonstration of IC on the HMA overlay on the graded aggregate base using Sakai machine. 

The demonstration site is a parking lot close to the junction of highway 19, 41, and SR 3 in Clayton 

County, Georgia (Figure 125). The field IC demonstration was performed from Sept. 14 to Sept. 18, 

2009. The objectives of this demonstration project were short-term goals for introducing HMA IC 

technology to GDOT and contractors who may not have prior experience with IC technology. The 

project was intended to demonstrate the benefits of IC for improving the compaction process and quality 

by achieving more uniform density and modulus of the HMA material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality control.
157

GAB base

LWD

Figure 125. Georgia HMA IC demonstration at the Park & Ride.

Major Findings

• Low Sakai compaction control values (CCVs) on the graded aggregate base (GAB) reflect on low 

CCVs on the HMA layer. Thus, it is strong evidence that one should construct a sound 
158

subbase/base course to achieve the integral strength of pavement layer system.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

1.061.381.702.022.342.662.983.303.623.944.264.584.905.225.545.866.186.506.827.14

Sakai CCV

Ocurrance (%)

μ=2.9

σ=0.9

Cov=0.3

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

2.507.50

12.5017.5022.5027.5032.5037.5042.5047.5052.5057.5062.5067.5072.5077.5082.5087.5092.5097.50

Sakai CCV

Ocurrance (%)

μ=3.2

σ=1.2

Cov=0.4

TB 01 GAB TB 01A HMA

Sakai CCV

Figure 126. Sakai CCV map and statistics.

• The moisture of GAB significantly affects the CCVs based on the mapping data. As shown in 

Figure 127, TB01 is the first test bed for mapping when moisture after rain is less dried than other 

test beds, resulting in lower CCV. Therefore, it is recommended to pave the HMA layer when the 

GAB is in “drier” condition.
159

Sakai CCV

TB01

TB02

TB03

TB04

Bus lane

Figure 127. Sakai CCV of GAB.

• The curing and treatment help improve the stiffness of materials. E.g. the TE04 GAB base has 

been applied the prime, which results in higher Sakai CCVs than other GAB test beds (see Figure 

127);

• The mapping of subbase using the Sakai double drum IC roller can be correlated to conventional 

in-situ nuclear-gauge density measurements.
160

y = 1.4439x + 137.48

R2

 = 0.5518

138

140

142

144

146

148

150

152

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00

Moist%

Wet density

Figure 128. Sakai CCV vs NG density of GAB material.

• The FWD measurements of the GAB layer are correlated to CCVs at some test beds but not at 

others. Further investigation is wanted.

Sakai CCV vs FWD Deflection

y = -0.0343x + 6.8611

R2

 = 0.4059

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0 20 40 60 80 100 120 140

FWD Sensor 1 deflection (mils)

Sakai CCV

TB 02 GAB Sakai CCV vs. FWD deflection

Sakai CCV vs FWD Deflection

y = -0.0049x + 4.9244

R2

 = 0.0088

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0 20 40 60 80 100 120 140

FWD measured deflection at 1st sensor (mils)

Sakai CCV

TB 03 GAB Sakai CCV vs. FWD deflection

Figure 129. Sakai CCV vs FWD measurements.

• The densities of HMA core samples are correlated to CCVs on the HMA layer, but in a reversed 

trend. However, it should be noted that only limited (5 in this case) core samples are available 

while more samples may be required to reach a solid conclusion.
161

Core sample Gmm% vs Sakai CCV

y = -67.458x + 65.169

R2

 = 0.4201

0.0%

50.0%

100.0%

150.0%

200.0%

250.0%

300.0%

350.0%

400.0%

0.915 0.92 0.925 0.93 0.935 0.94

Gmm%

Sakai CCV

Figure 130. Sakai CCV vs Gmm% of HMA.

• The measured LWD deflections and derived CBR values are not correlated to CCVs on the HMA 

layer based on the available and yet limited data.

y = -0.3229x + 3.0028

R2

 = 0.2472

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 1 2 3 4 5 6

LWD measured deflection (mm)

Sakai CCV

y = 0.011x + 1.6015

R2

 = 0.1903

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 20 40 60 80 100 120 140

LWD back measured CBR (%)

Sakai CCV

Figure 131. Sakai CCV VS LWD measurements of HMA.

Findings confirming previous demo sites

• The Sakai IC system can track roller passes, HMA surface temperatures, and the CCVs in real 

time - which provides essential tools for improving compaction quality;

• HMA temperatures affect the CCVs due to the temperature-dependent viscoelastic property of 

HMA materials. Note that CCVs’ measurement depth is approximately 6 feet.

Indiana IC Demonstration

The test site is located on US 52 between the junction with US 231 and Cumberland Ave, Lafayette, IN 

(Figure 132), on September 21 2009 to September 23 2010, using both the Sakai and Bomag double-drum 

IC rollers. The length of the project is about 5 miles long with two lanes on each direction. The existing 

pavement structure is 6” of HMA on top of 7” of concrete pavements. The existing pavements were 

firstly milled for about 2”, then overlaid with a 2.5” of intermediate HMA layer (19-mm max. nominal 
162

aggregate size) and 1.5” HMA surface layer (9.5-mm max. nominal aggregate size). The objectives of 

this demonstration project were short-term goals for introducing HMA IC technology to INDOT and 

contractors who may not have prior experience with IC technology. The project was intended to 

demonstrate the benefits of IC for improving the compaction process and quality by achieving more 

uniform density and modulus of the HMA material and providing roller operators (and superintendents) 

better feedback tools to make right decisions, and ultimately real-time quality control.

Figure 132. Indiana HMA IC Demonstration.
163

Major Findings

• Double-drum IC rollers can be used to map the milled asphalt pavements prior to the paving of 

HMA overlay.

Bomag Evib

Pavement lane Evib

Shoulder Evib

Figure 133. Mapping of milled HMA on PCC slabs using Bomag.

• From the Bomag IC mapping of the existing asphalt shoulders, lower vibration amplitude setting 

results in higher vibration modulus values (Evib) due to its shallower influence depth that 

concentrating on the asphalt layers;

Shoulder

Shoulder

Low amp

High amp

Figure 134. Mapping of milled HMA shoulder on PCC slabs.

• The Bomag Evib values on the milled HMA surface is higher than that of the HMA shoulder, 

indicating the former is a stronger pavement structure (see Figure 133);

• For both the Bomag and Sakai IC compactions, the non-nuclear density gauge measurements 
164

increase with increasing roller pass numbers, but the increase rate reduces;

NNG measurement after Bomag compaction

86%

87%

88%

89%

90%

91%

92%

N1N2

A

B

N3-A

N3-B

N4-A

N4-B

N4-CN4-D

N5-A

N5-B

N5-C

NNG-1

Test ID

Gmm%

Pass No. 3

Pass No. 2

Pass No. 1

NNG measurement after Sakai compaction

87%

88%

88%

89%

89%

90%

90%

91%

91%

92%

92%

93%

N6-AN6-B

N6-C

N7-AN7-B

N7-C

N8-AN8-B

N8-C

N9-AN9-B

N9-C

N10-AN10-B

Test ID

Gmm%

Pass No. 3

Pass No. 2

Pass No. 1

NNG after Bomag compaction NNG after Sakai compaction

Figure 135. NNG density of each breakdown roller pass following Bomag and Sakai 

compaction, respectively.

• The non-nuclear density was measured for each roller pass, which was found to correlate well 

with the Sakai CCV;

y = 5.5907x - 486.44

R2

 = 0.4833

0

5

10

15

20

25

30

35

88.5 89.0 89.5 90.0 90.5 91.0 91.5 92.0

Gmm (%)

Sakai CCV

Figure 136. Sakai CCV vs. NNG gauge measured Gmm% for combined single passes.

• The FWD deflections at HMA surfaces corresponding to the underlying concrete slab joints is 

higher than those at HMA surfaces corresponding to slab centers due to the weaker support at the 

concrete slab joints, and the FWD deflections at the milled HMA are higher than those at the 

HMA surface overlay due to the improved structure and material strength;
165

0

2

4

6

8

10

12

14

FW1FW3FW5FW7FW9

FW11

FW13FW15

FW17

FW19

FW21FW23

FW25FW27FW29

Test spot

FWD deflection under loading plate (mils)

Milled HMA at Slab Center

Milled HMA at Slab Joint

HMA Surface at Slab Center

HMA Surface at Slab Joint

Figure 137. FWD measured deflection under loading plate.

• The Bomag Evib has a linear relationship with the FWD measured deflection under the loading 

plate, but with a relatively low correlation;

y = -286864x + 5E+06

R2

 = 0.2613

0.00E+00

1.00E+06

2.00E+06

3.00E+06

4.00E+06

5.00E+06

6.00E+06

0 1 2 3 4 5 6 7

FWD measured deflection at loading plate (mils)

Bomag Evib (N/m2)

Figure 138. Bomag Evib vs. FWD measured deflection under the loading plate.

• The Bomag Evib does not correlate well with cored sample density. One of the reasons can be that 

Bomag Evib represents the stiffness of the pavement system rather than the single HMA layer. 

Also, IC measured surface temperatures at core locations are different as shown in Figure 139 and 

Evib is not adjusted with asphalt temperature. IC measurements at some core locations can be 

influenced by the underlying PCC slabs or joints.
166

Bomag Evib vs Cored Density

y = -458525x + 5E+07

R2

 = 0.246

0

1000000

2000000

3000000

4000000

5000000

6000000

91.5 92.0 92.5 93.0 93.5 94.0 94.5

Cored sample Gmm%

Bomag Evib

HMA Surface Temperature vs Cored Density

y = 5.5685x - 449.43

R2

 = 0.071

0

10

20

30

40

50

60

70

80

90

100

91.50 92.00 92.50 93.00 93.50 94.00 94.50

Cored sample Gmm%

HMA surface temperature ( oF)

Figure 139. Bomag measurements vs. cored sample Gmm% of HMA intermediate layer 

(TB 03).

Findings confirming previous demo sites

• The IC roller can track the roller pass numbers, roller speed, HMA surface temperatures, and the 

ICMVs, which provides important metrics for the compaction quality;

• With the real-time information of IC roller passes, HMA surface temperatures and ICMVs 

displayed on the screen, the roller operator can adjust rolling patterns to improve the compaction

quality;

• The HMA temperatures affect the ICMVs due to the temperature-dependent viscoelastic property 

of HMA materials;

• For the roller final pass (proof data), the non-nuclear density gauge measurements of HMA 

overlay do not correlate well with the Bomag Evib and Sakai CCV (they have linear relationships 

but with relatively low R2 values like 0.25 and 0.27).

Wisconsin IC Demonstration

The test site is located on IH 39 at the junction of IH 39 and 153, Mosinee, WI (see Figure 140). The 

length of the project is about 5 miles long with two lanes on the South Bound (SB) direction. The layers 

and materials include the rubblized and crack-and-seat PCC base, HMA base course, intermediate HMA 

layer, and HMA surface layer. The Sakai double-drum IC roller was sued for all the mapping and 

compaction.

The objectives of this demonstration project were short-term goals for introducing HMA IC technology to 

WISDOT and contractors who may not have prior experience with IC technology. The project was 

intended to demonstrate the benefits of IC for improving the compaction process and quality by achieving 

more uniform density and modulus of the HMA material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality control.
167

Figure 140. Wisconsin HMA IC demonstration.

Unique Features

• It is the first demo project to pave HMA courses on the rubblized PCC base; 

• It is the first demo to use IC roller for multi-layers including mapping the PCC base, compacting 

on the HMA base, 2nd lift HMA intermediate layer, and HMA surface, which has provided a 

wealth of information and knowledge to share with the industry in order to accelerate the 

implementation of IC technologies;

• It is the first demo to perform FWD and LWD test on multi-layers from the PCC base, HMA 

base, 2nd lift HMA intermediate layer, and HMA surface;

• It is the first demo to use IC roller for both the breakdown and finishing compactions;
168

• It is the first demo to perform the LWD test following the breakdown compaction;

• Thermal bar was used for measuring HMA surface temperatures.

Major Findings

• One of the repeated evidence of immediate benefits is improvement of rolling patterns. See the 

significant difference between the “before” and “after” using of the IC technologies in Figure 

141.

Before

After

Figure 141. Improvement of rolling patterns using the IC technologies.

• Double-drum IC rollers can be used to map the rubblized and crack-and-seat PCC bases prior to 

the paving of HMA layer;

Sakai CCV Pass No Temperature (

o

F)

Rubblized PCC Base

Sakai CCV Pass No Temperature (

o

F)

Crack-and-seat PCC Base

Sakai CCV Pass No Temperature (

o

F)

Rubblized PCC Base

Sakai CCV Pass No Temperature (

o

F)

Crack-and-seat PCC Base

Figure 142. Sakai mapping existing rubblized and crack-and-seat PCC bases (TB2M).
169

• IC mapping of the rubblized and crack-and-seat PCC base was shown to be crucial in identifying 

the pavement conditions prior to the paving of the HMA base;

shoulder

Rubblized

PCC base

shoulder

Crack-and-seat

PCC base

Figure 143. Sakai CCV of existing rubblized and crack-and-seat PCC bases (TB1M).

• The non-nuclear density was measured for each roller pass, which was found to correlate well 

with the Sakai CCV (R2 value of 0.48);
170

y = 5.5907x - 486.44

R2

 = 0.4833

0

5

10

15

20

25

30

35

88.5 89.0 89.5 90.0 90.5 91.0 91.5 92.0

Gmm (%)

Sakai CCV

Figure 144. Sakai CCV vs. NNG gauge measured Gmm% for combined single passes.

• Night paving has been successfully implemented with the assistance of IC technology;

Night paving Compaction

Figure 145. Night paving and HMA compaction.

• LWD measurements (deflection and moduli) and NG density have relatively good linear 

correlation with the roller measurement value (ICMV) for some test beds, and HMA temperature 

has significant effect on the correlation result (see examples below, ICMV vs. LWD on the left, 

and ICMV vs. NG density on the right);
171

Figure 146. Roller MVs vs. FWD deflections and NG Densities.

• Generally compaction uniformity increases from the “ground up”: i.e., from PCC base to the 

HMA base and then the 2nd lift HMA course and HMA surface, as indicated in the semivariogram analysis (see below, semi-variograms for HMA base course and the second lift, 

intermediate course);

Range: 11. 78

Sill: 0.29

Nugget: 0.0

Sill: 0.29

Range:11. 78

Sill: 0.17

Range:14.41

Passing Lane HMA Base Passing Lane 2nd lift HMA

Range: 11. 78

Sill: 0.29

Nugget: 0.0

Sill: 0.29

Range:11. 78

Sill: 0.17

Range:14.41

Passing Lane HMA Base Passing Lane 2nd lift HMA

Figure 147. Semi-variograms of HMA base and 2nd lift HMA.

• Optimum roller passes can be determined by the “compaction curves” that help avoiding the over 

compaction or under compaction. (see below examples of compaction curves that indicate 

optimum pass as 4)
172

Passing lane HMA base Driving lane 2nd lift HMA

Figure 148. Compaction curves of HMA base and 2nd lift HMA.

Findings confirming previous demo sites

• The IC roller can track the roller pass numbers, roller speeds, HMA surface temperatures, and the 

ICMVs, which provides important metrics for the compaction quality;

• With the real-time information of IC roller passes, HMA surface temperatures and ICMVs 

displayed on the screen, the roller operator can adjust rolling patterns to improve the compaction 

quality.

Texas IC Demonstration

This report describes the FHWA/TPF mini intelligent compaction (IC) demonstration for the hot mix 

asphalt (HMA) materials in El Paso, TX, in May 2010. It is a new HMA construction project (CSJ 3451-

01-027) on FM 1281. Project limits are from 0.3 miles east of IH-10 to 3.301 miles east of IH-10. The 

existing asphalt layer was milled and the top 4” of granular base was stabilized with 4% cement by weight. 

Then, a 2” of new HMA layer was paved on top of the cement stabilized base (CTB).
173

Cement treated subbase HMA base

Figure 149. Texas HMA IC demonstration.

Major Findings

• Sakai double-drum IC rollers can be used to map cement stabilized bases prior to the paving of 

HMA layer that can differentiate performances from different cement mixing techniques;

 

 

Figure 150. Sakai CCVs from the IC mapping on the cement stabilized base, TB 01M

• Falling Weight Deflectomer (FWD) and Non-Nuclear density Gauge (NNG) test data, when 

performed close to the IC mapping temperatures on HMA, can be correlated well with the Sakai 

CCV;
174

FWD NNG

Figure 151. Correlation between Sakai CCVs (mapping run) and in-situ tests on HMA (FWD 

deflections normalized to 9,000 lb in mils, NNG in %Gmm), TB 01A.

• PSPA tests, when performed close to the IC mapping temperatures on HMA, can be correlated 

very well with the Sakai CCV;

Figure 152. Correlation between Sakai CCVs (mapping run) and PSPA tests (normalized to 77 °F 

in ksi) on HMA, TB 01A (left) and TB 02A (right) at 1 and 2 hours after paving, respectively.

• The Sakai IC roller can track roller passes, roller speeds, HMA surface temperatures, and 

CCV successfully for HMA compaction;

• With the real-time information of IC roller passes, HMA surface temperatures and 

ICMVs displayed on the onboard Sakai CIS screen, roller operators can adjust rolling 

patterns to improve the compaction quality;

• Semi-variogram can be used as a metric for evaluating uniformity.

Range: 19.68’

Sill: 0.06

Scale: 0.06

Nugget: 0.00

CCV

Figure 153. Sakai CCVs for mapping run on fresh HMA and its associated semi-variogram, TB 

01A.
175

Pennsylvania IC Demonstration

This is an HMA overlay project that the location is centered at the interchange of SR 53 and SR 219 in 

Summerhill, PA (Figure 154). The existing pavement is HMA on top of PCC slabs. During the 

demonstration, the existing HMA layer was milled and removed before paving HMA binder and wearing 

courses (9.5-mm mix). The Sakai and Volvo double-drum IC rollers were used for all the mapping and 

compaction.

The objectives of this demonstration project were short-term goals for introducing HMA IC technology to 

PennDOT and contractors who may not have prior experience with IC technology. The project was 

intended to demonstrate the benefits of IC for improving the compaction process and quality by achieving 

more uniform density and modulus of the HMA material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality control.
176

Figure 154. Pennsylvania HMA IC demonstration.

Unique Features

• It is the first IC demonstration project on mountainous areas that bring challenges to 

global position signals (GPS) signal transmission;

• It is the first IC demonstration project to conduct light-weight deflectometer (LWD) tests 

on hot mixture asphalt after breakdown or intermediate compaction;

• It is the first IC demonstration to map newly paved HMA binder course;
177

• It is the first IC demonstration to use Volvo IACA roller for measuring HMA densities in 

real time.

Major Findings

• One of the repeated evidence of immediate benefits is improvement of rolling patterns;

• Measurements of IC roller GPS can be verified by placing a GPS rover receiver on the 

top of roller GPS receiver unit to eliminate the potential error resulted from offset and 

direction angle;

• Relocation of GPS base station will cause roller GPS data shifting, therefore restarting 

the IC roller computer system as well as hand-held GPS rover is required under such 

condition;

• The Sakai compaction control value (CCV) of underneath layer reflects to that of the 

upper HMA layer (e.g. uniform CCV of the HMA binder course from mapping 

corresponds to relatively uniform CCV of HMA wear course paved on top as shown in 

the following figure);

TB 03M Mapping 

binder course TB 03W Wearing 

course

Figure 155. Sakai CCVs of mapping binder course and paving on wearing course.
178

• Compaction uniformity can be compared for different sections using semi-variogram 

analysis (e.g. Uniformity increases from sections 1 to 4 for TB 03W HMA wearing 

course. See the following figure);

Range: 13. 12

Sill: 1.25

Nugget: 0.0

Range: 14. 44

Sill: 1.00

Nugget: 0.0

Range: 18.37

Sill: 0.32

Nugget: 0.0

Range: 15. 74

Sill: 0.87

Section 1 Nugget: 0.0

Section 2

Section 3

Section 4

Figure 156. Semi-variograms of HMA wearing course.

• Compaction uniformity appears to increase from the “ground up” for the paving materials 

(i.e. from the hot HMA binder course paving to the hot HMA surface course paving);

• The newly paved HMA binder course exhibits good uniformity likely due to uniform 

temperature distribution. However, more research is needed to quantify the effects of 

temperature distribution on compaction uniformity;
179

• Optimum roller passes can be determined by the “compaction curves” to prevent decompaction or under-compaction. (see the figure below for an example of compaction 

curves)

Figure 157. Compaction curve for HMA wearing course.

Confirming Past Findings

• The IC roller can track the roller pass numbers, roller speeds, HMA surface temperatures, 

and the ICMVs, which provides important metrics for the compaction quality;

• With the real-time information of IC roller passes, HMA surface temperatures and 

ICMVs displayed on the screen, the roller operator can adjust rolling patterns to improve 

the compaction quality.

Virginia IC Demonstration

It is an HMA overlay project on I-66, Markham, VA, conducted from September 27 to September 30 

2010 (see Figure 158). The existing pavement is HMA pavement. During the demonstration, the existing 

HMA surface layer was milled and removed before paving HMA overlay.

The objectives of this demonstration project were short-term goals for introducing HMA IC technology to 

VADOT and contractors who may not have prior experience with IC technology. The project was 

intended to demonstrate the benefits of IC for improving the compaction process and quality by achieving 

more uniform density and modulus of the HMA material and providing roller operators (and 

superintendents) better feedback tools to make right decisions, and ultimately real-time quality control.
180

Figure 158. Virginia HMA IC demonstration.

New Findings:

• Measurements of IC roller GPS can be verified by placing a GPS rover receiver on the 

top of roller GPS receiver unit to eliminate the potential error resulted from offset and 

movement angles (see figure below);
181

Figure 159. Roller GPS validation.

• Mapping of exiting HMA surfaces can be used to evaluate pavement conditions in order 

to identify relatively weaker areas (see figure below that shoulder area –right hand side -

has lower CCV than pavement lane –left hand side-);

Sakai CCV

Figure 160. Sakai CCV of mapping HMA surface.
182

• Compaction uniformity for different construction sections can be compared using semivariogram analysis (see figure below that higher range values indicates higher 

compaction uniformity);

Range: 10. 50

Sill: 0.88

Nugget: 0.00

Range: 13.13

Sill: 1.11

Nugget: 0.00

Range: 11. 80

Sill: 0.84

Nugget: 0.00

Section 1

Section 2

Section 3

Figure 161. Semi-variogrqams of HMA overlay.

• Mapping of existing HMA pavement shows a relatively higher uniformity than that from 

compaction of hot mixture asphalt overlay. This observation may be due to the uniform 

(colder) temperatures of existing HMA. However, more research is needed to quantify 

the effects of temperature distribution on compaction uniformity;

• Compaction curve can be established using IC technology to determine optimum roller 

pass number (see figure below for compaction curves of three different sections of HMA 

overlay);

Section 1

Section 2

Section 3

Figure 162. Compaction curves of HMA overlay.

• Nuclear density gauge measurements of fresh HMA overlay behind the Volvo IACA 

roller forms a compaction curve within a narrow range of densities (see figure below);
183

NG Tests Following Volvo Compaction

152.0

152.5

153.0

153.5

154.0

154.5

155.0

155.5

0 1 2 3 4 5 6

Volvo Roller Pass Number

NG Density (kg/m3

)

Point 1

Point 2

Figure 163. NG density at different Volvo roller pass.

Confirming Past Findings

• The IC roller can track roller pass numbers, roller speeds, HMA surface temperatures, 

and the ICMVs, which provides important metrics for compaction quality;

• With real-time information of IC roller passes, HMA surface temperatures and ICMVs 

displayed on the onboard screen, roller operators can adjust rolling patterns to improve 

the compaction quality.

Rolling Patterns and Mapping

All the FHWA/TPF demonstration projects have successfully demonstrated the use of IC rollers for 

tracking roller locations and passes, ICMVs, and surface temperatures for 100 percent coverage of 

compaction areas to facilitate decision making.

• Improvement of Rolling Patterns: IC technologies can be used to improve roller patterns as an 

immediate benefit. Figure 164 shows that roller passes become more consistent and uniform after 

using the IC technologies for the Indiana demonstration project. Knowing pavement 

temperatures in real-time helps operator determine compaction modes (static vs. vibratory) during 

the intermediate/finishing roller compaction, and avoid material premature failure (e.g. vibratory 

compaction at relatively low temperature will damage HMA materials).

Before

After

Figure 164. Roller Passes before and after using IC technology (Indiana IC demonstration)

• Mapping the Strength of Existing Support: IC rollers were also successfully used for mapping 

the underlying materials prior to HMA paving (IC roller drives on the existing pavement layer 

using the vibration mode), for just-in-time corrective actions. The Minnesota project identified 
184

weak spots on the grade prior and after paving.

• Monitoring the Strength Gains of Stabilized Base: IC mapping can be used to monitor strength 

gains of stabilized base as shown in the Mississippi project (Figure 165). IC technologies also 

help improve compaction quality during night paving as it tracks rollers in the dark.

TB 2A

TB 2C

TB 2B

MS Mapping Subbase

0

5

10

15

20

25

 

RMV

TB 2A

5 day cure

TB 2B

6 day cure

TB 2C

7 day cure

RMV

Figure 165. Sakai CCV of mapping existing stabilized subbase (Mississippi IC demonstration).

Compaction Curves

For a paving project with relatively uniform support, an optimum roller pass can be established from a 

test strip for a given HMA mix. Figure 166 shows compaction curves. Results show that for most 

highway projects the ICMV increase first and then decreases with increasing roller pass number, or 

ICMV continuously increases until being constant after a certain roller pass. E.g. for the Wisconsin 

demonstration, the optimum roller pass is 4 or 5. This finding offers very important information to help 

achieve the best compaction, while avoid under/over-compaction by using the IC technology.
185

WI, TB01B HMA Base WI, TB01C HMA 2nd Lift WI, TB02B HMA Base

WI, TB02C HMA 2nd Lift

NY, 1st Lift HMA Base EB NY, 2nd Lift HMA Base_EB NY, Binder Course

MS, TB23 HMA Base EB LN1 MS, TB23 HMA Base EB LN2 MD, TB01 HMA Overlay

IN, TB04_2 HMA Overlay IN, TB04_3 HMA Overlay IN, TB05_1 HMA Overlay

MN, route 4 HMA Base 1 MN, route 4 HMA Wearing

PA, TB02 HMA Base PA, TB03 HMA Surface VA, TB01 HMA Overlay

Figure 166. Compaction curves of TPF HMA IC Projects
186

Compaction Uniformity

Semivarigram parameters were derived using the geostatistical analysis. Table 13 summarizes the semivariogram parameters of range and sill. Results have shown that generally compaction uniformity 

increases (with greater range while lower sill values) from the “ground up”: i.e., from the base to the 

HMA base and then HMA surface courses (see results of the NY, MS, MD, and WI demonstrations). 

This is because that the adjustable compaction efforts on the upper layer have increased the compaction 

uniformity.
187

Table 13. Fitted Semi-variogram parameters.

State Test bed Roller RMV Mean Range Normalized sill

MN Route 4 HMA Base 1 SW880 Breakdown 7.25 5.22 3.28

MN Route 4 HMA wearing course SW880 Breakdown 10.64 15.67 2.94

NY Mapping SB SW880 Mapping 12.98 41 1.67

NY 1st Lift HMA Base EB SW880 Breakdown 15.36 93 0.44

NY 2nd Lift HMA Base EB SW880 Breakdown 14.05 92 0.70

MS TB 2A SB Mapping SW880 Mapping 13.7 28 1.53

MS TB23 HMA Base EB Ln1 SW880 Breakdown 9.38 35 0.23

MS TB23 HMA Base EB Ln2 SW880 Breakdown 9.78 37 0.40

MD TB02 HMA Base Mapping SW880 Mapping 24.33 27 10.39

MD TB02 HMA Overlay SW880 Breakdown 22.8 85 2.70

MD TB03A HMA Base Mapping SW880 Mapping 35.98 65 11.06

MD TB03B HMA Overlay SW880 Breakdown 24.11 40 1.18

IN TB04-1 HMA Overlay Bomag Breakdown 5,511,175 2.8 0.13

IN TB04-2 HMA Overlay Bomag Intermediate 5,682,027 4.4 0.09

IN TB04-3 HMA Overlay Bomag Breakdown 6,138,302 4.4 0.06

IN TB05-1 HMA Overlay Bomag Intermediate 5,784,951 2 0.07

IN TB04-1 HMA Overlay SW880 Intermediate 14.2 4 1.38

IN TB04-2 HMA Overlay SW880 Breakdown 19.4 4 0.76

IN TB04-3 HMA Overlay SW880 Intermediate 17.7 4 1.07

IN TB05-1 HMA Overlay SW880 Breakdown 22.8 6.4 0.04

IN TB05-2 HMA Overlay SW880 Breakdown 18.4 4 0.80

WI TB01B HMA Base_1 SW990 Breakdown 17.49 11.78 0.29

WI TB01C 2nd HMA lift_1 SW990 Breakdown 19.15 14.41 0.17

WI TB01D HMA surface_1 SW990 Breakdown 13.07 17.05 0.08

WI TB01B HMA Base_2 SW990 Breakdown 18.03 13.06 0.28

WI TB01C 2nd HMA lift_2 SW990 Breakdown 21.07 15.68 0.15

WI TB01D HMA surface_2 SW990 Breakdown 15.48 19.58 0.08

WI TB02B HMA Base_1 SW990 Breakdown 13.58 18.36 0.10

WI TB02C 2nd HMA lift_1 SW990 Breakdown 23.13 19.66 0.08

WI TB02D HMA surface_1 SW990 Breakdown 21.99 19.68 0.07

WI TB02B HMA Base_2 SW990 Breakdown 15.28 19.58 0.07

WI TB02C 2nd HMA lift_2 SW990 Breakdown 24.35 15.67 0.10

WI TB02D HMA surface_2 SW990 Breakdown 24.13 15.67 0.10

PA TB01B HMA binder course SW880 Intermediate 14.67 10.49 0.00

PA TB02W HMA wearing course_1 SW880 Intermediate 27.15 15.74 0.00

PA TB02W HMA wearing course_2 SW880 Intermediate 21.56 13.12 0.00

PA TB02W HMA wearing course _3 SW880 Intermediate 24.9 14.44 0.00

PA TB02W HMA wearing course _4 SW880 Intermediate 33.83 18.37 0.00

PA TB03M HMA binder course (mapping) SW880 Mapping 65.48 21 0.00

PA TB03W HMA wearing course_1 SW880 Intermediate 28.48 13.12 0.00

PA TB03W HMA wearing course_2 SW880 Intermediate 25.58 11.81 0.00

PA TB03W HMA wearing course_3 SW880 Intermediate 21.22 14.43 0.00

PA TB03W HMA wearing course_4 SW880 Mapping 19.72 14.44 0.00

VA TB 01M SW880 Mapping 26.33 14.42 0.00

VA TB 01 HMA Overlay_1 SW880 Breakdown 22.12 11.8 0.00

VA TB 01 HMA Overlay_2 SW880 Breakdown 21.88 10.5 0.00

VA TB 01 HMA Overlay_3 SW880 Breakdown 17.79 13.13 0.00

Notes: For the Indiana demonstration, the existing HMA mapping data using Bomag was not exportable 

due to some technical issues and thus not analyzed in this table.
188

Correlation Study

Univariate Linear Correlation

Correlation of ICMV to NG/NNG density

Figure 167 presents the linear correlation of ICMV with density. Results show that mostly ICMV 

increases with increasing density, indicating that a higher stiffness corresponds with a greater material 

density. For some test beds, a good correlation was achieved (e.g. for the Indiana TB 04-2 test bed with 

NNG densities for all passes, and the Wisconsin TB02C test bed with NG for the proof data). However, 

for some test beds, the correlation is poor and the trend even reverses for rare ones like the NY HMA base. 

Those relatively poor correlations could be explained by at least the following reasons: 1) ICMV is a 

stiffness of the pavement system with an influence depth going through underlying layers and soil 

foundation, while density is measured only for the top HMA layer; 2) During the compaction HMA 

temperature and/or the vibration frequency are not constant at those in-situ test spots, which will affect 

ICMV much more significantly than density; however, the existing ICMV models are unable to convert 

ICMVs to that at reference temperature and decouple the stiffness to a respective pavement layer due to 

technical challenging.

NY, 2nd Lift HMA Base EB

Proof

NY, Binder Course Proof

MS, TB23 HMA Base 

EB LN1 Proof

MS, TB24 HMA 

Base EB LN2 Proof

MD, TB03B HMA 

Overlay Proof

IN, TB04-2 HMA Overlay 

NG Proof

IN, TB04-2 Sakai Breakdown 

NG All Passes

IN, Bomag, Evib TB04-1 

Bomag NNG Proof

WI, TB01C 2nd Lift HMA 

Proof WI,TB02B HMA Base

Section 2 All Passes

WI, TB02B HMA Base 

Section 2 Proof

WI, TB02C 2nd Lift HMA 

All Passes

WI, TB02C 2nd Lift 

HMA NG Proof

NY, 1st Lift HMA Base EB

Proof

Density (pcf) Density (pcf)

Density (Gmm%)

Density (pcf) Density (pcf)

Density (pcf)

Density (pcf)

Density (pcf) Density (pcf)

Density (pcf) Density (pcf)

Density (pcf) Density (Gmm%)

Density (Gmm%)

Figure 167. NG/NNG Density Correlation (NY, MS, MD, IN, WI) 
189

Note: Only units of original research of in-situ measurements are presented as also required by the TRB 

guideline, and thus not converted to uniform one as the same for following figures.

Correlation of ICMV to cores density

Figure 168 presents the linear correlation between ICMV and cores density. Results show inconsistent 

correlations, as ICMV may increase or decrease with the increase of core density. This inconsistent 

correlation may be explained by at least the following reasons: 1) the cores were taken after the static 

finishing roller compaction when pavement cooled down, while the ICMV was measured from the 

vibratory breakdown roller compaction; 2) the test spot number was usually very limited (e.g. 4) due to 

the high cost for the destructive test; and 3) others include the inconsistent HMA temperatures and 

frequencies at test spots, and ICMV is measuring the pavement system rather than only a top HMA layer 

for cores.

MD, TB03 SMA overlay, Sakai GA, TB01 HMA 

intermediate layer, Sakai

IN, TB03 HMA 

overlay, Bomaga

MN, TB02 HMA base, Sakai MN, HMA wearing 

course, Sakai

MN, TB01

HMA base, Sakai

Density (specific gravity) Density (Gmm%)

Density (Gmm%)

Density (Gmm%) Density (Gmm%) Density (Gmm%)

Figure 168. Cores density vs. ICMV (MN, MD, GA, IN)

Correlation of ICMV to FWD deflections and modulus

Figure 169 presents the linear correlation of ICMV with normalized FWD measured deflection (e.g. at 

9000 lbs) or layer moduli (note that here only one project presents the layer moduli from FWD test). 

Apparently ICMV decreases with increasing FWD measured deflection as expected. Results show that 

they have more consistent and relatively good linear correlation compared to that of ICMV with density, 

which may be due to that FWD measurement is for the integral pavement system as measured by ICMV. 

However, not very high R2 values are achieved in these demonstration projects, which could be explained 

by following reasons: 1) ICMV and deflection are two different concepts and they may not have a linear 

correlation, though both of them indicate the stiffness of pavement; 2) the inconsistent HMA temperature 

and vibration frequency during construction at those spots affect the correlation results as discussed above; 

and 3) other factors include moisture variation of subbase/soil.
190

GA, TB02 GAB Mapping GA, TB05 GAB Mapping MD, TB03A HMA Mapping

MS, TB02 Base Mapping MS, TB02 Base Mapping

WI TB01M Rubblized

PCC Base Mapping

Figure 169. Correlation of Sakai CCVs with Normalized FWD Deflections or moduli

Note: 1. deflection unit is 10-3 inch, layer moduli unit is psi; 2. GAB is granular aggregate base.

ICMV correlation with LWD measured deflection and moduli

Figure 170 presents the linear correlation of ICMV with LWD measured deflection and moduli or CBR. 

Apparently ICMV increases with increasing LWD moduli/CBR or decreasing LWD deflection as 

expected. Results show that they have more consistent linear correlation than that of ICMV and density 

discussed above. Nonetheless, no very high R2 values are achieved, which may be explained by the same 

reasons as that for the correlation between ICMV and FWD measurements.
191

GA, parking lot, HMA 

intermediate layer

GA, parking lot, HMA 

intermediate layer

WI, TB01B HMA base 

Sakai breakdown

WI, TB01B HMA base

Sakai breakdown

WI, TB02B section 1 HMA 

base Sakai breakdown

WI, TB02B full length HMA 

base Sakai breakdown

WI, TB02B full length HMA 

base Sakai breakdown

WI, TB02B Section 1 HMA 

base Sakai breakdown

WI, TB02C Section 1 HMA 

2nd fift Sakai breakdown

WI, TB02C Section 1 HMA 

2nd lift Sakai breakdown

WI, TB02C Section 2

HMA 2nd Lift Sakai breakdown

WI, TB02C Section 2

HMA 2nd Lift Sakai breakdown WI, TB02C Section 2

HMA 2nd lift Sakai finishing

WI, TB02C Section 2

HMA 2nd lift Sakai finishing

Figure 170. Correlation of ICMV with LWD measurements (layer moduli unit: MPa, deflection 

unit: x 10-6 m).
192

Multivariate Correlation

ICMV is potentially influenced by various factors, and therefore a multivariate analysis is warranted to 

study these effects. Multivariate linear regression study was performed on data from some tests beds of 

the Minnesota and Wisconsin projects. : The correlation among HMA base ICMV, and in-situ 

measurements of HMA base, roller vibration frequencies, HMA surface temperatures, subbase ICMV and 

associated vibration frequencies is studied. 

Table 14 and Table 15 summarize the multivariate linear correlation results for the Minnesota and 

Wisconsin, respectively. Results indicate that correlation indicators (e.g., R2

) have improved with 

multivariate linear regression compared to those from univariate linear regression. For the Minnesota 

projects, subbase ICMV and HMA base temperatures are shown to have significant effect on the HMA 

base ICMV (with lower P values). For the Wisconsin TB01B HMA base study, the FWD deflection and 

subbase ICMV are shown to have significant relationship with the HMA ICMV (with lowest P values). 

For the Wisconsin TB02B HMA base study, the HMA base temperature and subbase ICMV have shown 

significant effect (with very low P values) on HMA base ICMV, while the HMA base NG densities have 

shown less relevant relationship. These results demonstrate that using univariate linear regression may 

omit the effects of other important factors. Therefore, multivariate analyses are recommended to evaluate 

the influences of multiple potential factors on the ICMV for future studies.

Table 14. Multivariate Correlation Results of Minnesota Project

a) Sample 1: ICMV of Route 4 HMA base with NG density, Minnesota

Regression Statistics Coefficients Standard Error t Stat P-value

Multiple R 0.9975 Intercept -276.0905 22.0802 -12.5040 0.0011

R Square 0.9950 HMA Base NG Density 0.3064 0.0340 9.0235 0.0029

Adjusted R Square 0.9866 HMA Base Freq -0.0013 0.0011 -1.1282 0.3413

Standard Error 0.1623 HMA Base Temp 0.0318 0.0110 2.8950 0.0628

Observations 9 Agg Subbase RMV 0.8823 0.0440 20.0494 0.0003

df 8 Agg Subbase Freq 0.0718 0.0078 9.1625 0.0027

b) Sample 2: ICMV of Route 4 HMA base with core density, Minnesota

Regression Statistics Coefficients Standard Error t Stat P-value

Multiple R 0.9669 Intercept -238.8587 102.1644 -2.3380 0.1444

R Square 0.9349 HMA Base Core Density 0.0956 0.3005 0.3183 0.7804

Adjusted R Square 0.7720 HMA Base Freq 0.0071 0.0045 1.5994 0.2508

Standard Error 0.6547 HMA Base Temp 0.0550 0.0707 0.7783 0.5179

Observations 8 Agg Subbase RMV -0.6805 0.3068 -2.2180 0.1568

df 7 Agg Subbase Freq 0.0677 0.0350 1.9350 0.1926
193

Table 15. Multivariate Correlation Results of Wisconsin Project

a) Sample 1: ICMV of TB01B HMA base, Wisconsin

Regression Statistics Coefficients Standard Error t Stat P-value

Multiple R 0.7575 Intercept 410.5940 409.9901 1.0015 0.3275

R Square 0.5738 HMA base frequency -0.0946 0.0654 -1.4456 0.1624

Adjusted R Square 0.4769 HMA base temperature -0.0005 0.0912 -0.0051 0.9960

Standard Error 2.8808 HMA base FWD deflection 0.1100 0.0451 2.4380 0.0233

Observations 28 Subbase RMV 0.4164 0.1612 2.5833 0.0170

Subbase frequency -0.0121 0.0870 -0.1387 0.8909

b) Sample 2: ICMV of TB02B HMA base, Wisconsin

Regression Statistics Coefficients Standard Error t Stat P-value

Multiple R 0.9879 Intercept 131.1543 61.2292 2.1420 0.0490

R Square 0.9759 HMA base NG 0.0499 0.0603 0.8274 0.4210

Adjusted R Square 0.9679 HMA base frequency -0.0755 0.0363 -2.0770 0.0554

Standard Error 0.7279 HMA base temperature -0.0574 0.0081 -7.0589 3.88E-06

Observations 21 Subbase RMV 0.8173 0.0412 19.8184 3.60E-12

Subbase frequency 0.0282 0.0249 1.1295 0.2764
194

Chapter 8 Guidelines for IC Implementation

The recommended guidelines for successful IC implementation consist of: IC Road Map, 

General IC Plan, IC Data Management, IC Specifications, and IC training/workshops.

IC Road Map

The IC research team has developed an IC Road Map based on lessons-learned and knowledgegained through various efforts under this project. The IC Road Map is to lay out the shortest path for IC 

implementation by filling gaps and overcoming barriers through streamlined strategies. This will include 

applications of IC technologies to various pavement materials including subgrade soils, subbase, and hot 

asphalt mixture materials. 

There are four major tracks with three subtracks under each major track:

Track 1 — Equipment and Technologies

• Subtrack 1.1 : Standardization of IC roller measurement systems

• Subtrack 1.2 : Practical use of GPS in IC

• Subtrack 1.3 : Valid In-situ point tests to correlate with IC measurements

Track 2 — Data Management and Integration

• Subtrack 2.1 : National IC database and data collection guidelines

• Subtrack 2.2 : Standardization of IC data storage and exchange

• Subtrack 2.3 : A software tool for IC data viewing, analysis and reporting

Track 3 — Specifications

• Subtrack 3.1 : National guidelines for IC QC/QA specifications

• Subtrack 3.2 : Expert Task Group (ETG) for AASHTO IC specification development 

• Subtrack 3.3 :Technical support for States specifications development and customization

Track 4 — Technology Transfer and Training

• Subtrack 4.1 : IC workshops and certification

• Subtrack 4.2 : IC field demonstration

• Subtrack 4.3 : IC website and knowledge base
195

Figure 171. IC Road Map
196

General IC Plan

The general IC plan can be adapted by State agencies for local implementation. Any successful data 

collection needs a good plan and communication among all parties. While all the parties will work 

together and coordinate efforts, each will have primary responsibility for different aspects of the project 

both in the planning phase and during the actual project, as delineated below.

Project Team Responsibilities

o Coordination among all parties,

o Assisting in the project selection,

o Scheduling and arrangements for IC roller,

o Coordination with IC roller manufacturers and other equipment suppliers,

o Assisting with correlation testing,

o Data collection/management/analysis/reporting,

o Cost of agreed upon fees associated with mobilization of IC roller, and

o Cost of other test parties such as ISU geotechnical mobile lab.

State DOT Responsibilities

o Project selection (with assistance from the project team),

o Project contractual arrangements (if required),

o Coordination with contractors,

o Providing storage locations for IC rollers,

o Providing test equipment and manpower for in-situ testing (using standard equipment 

and practices for acceptance, but possibly at a higher frequency than normal),

o Providing or coordinating traffic control (when necessary),

o Facilitating the Open House, and

o Project delta costs for contractors.

Roller Manufacturer Responsibilities

o Coordinating with the project team, DOT, and contractor concerning shipping the IC 

roller in a timely manner,

o Providing the project team a copy of their software to view the information obtained 

from their IC roller,

o Arranging for necessary GPS base station setup,

o Training the operator, DOT representative, and project team on proper roller 

operation,

o Participating in the roller demonstrations in a limited capacity, and

o Providing technical support in a timely manner throughout the project (e.g., via phone) 

including equipment maintenance and repair, if needed.

Paving/Earthwork Contractor Responsibilities:

o Coordination of paving/rolling activities and cooperation with the above parties 

during the course of field demonstration, and

o Providing conventional roller operation for the control sections.
197

Special Provision Documents

Often, special provision specifications by the DOT are provided to the contractor as a part of the 

paving contract. These special provision specifications would clearly define the scope and detailed 

requirements for the paving contractor’s involvement. Costs resulting from field delays, including 

adverse weather conditions and machine malfunctioning, are normally shared by the project team and 

DOTs.

Project Website 

A project website is essential for effective communication among the above parties. This website 

normally provides real time, updated information on the current project (see Figure 172 to Figure 175). 

Information includes:

• Project period

• Site location and real-time weather information

• Material types

• IC rollers

• Objectives

• Meetings, documents, photos, videos

• Host DOT contacts
198

Figure 172. An example of IC project webpage (1 of 4)

Figure 173. An example of IC project webpage (2 of 4)
199

Figure 174. An example of IC project webpage (3 of 4)

Figure 175. An example of IC project webpage (4 of 4)
200

On-Site Data Collection Schedule

An example of an on-site daily activity schedule is illustrated in Table 16 and Table 17 as follows:

Table 16. An example of an on-site daily activity schedule (ICPF TxDOT demo)

Date Tasks Detailed Activities

Sun., July 20 Set up rollers (ISU) Set up the IC machine(s) and GPS base station system.

(Texana_Ammann) Conduct trial runs to verify the machine is 

operating and communicating with the GPS base station.

(All) Meet at the Sleep Inn at 7:30PM for a field site tour.

Mon., July 

21 (Day 1)

Training & 

Calibration/

Production/

Mapping

(Research Team) Initial training of TxDOT and contractor 

personnel in machine operations, data collection, data 

management, and in-situ testing strategies.

(Research Team/Dynapac) Calibration, production and 

mapping rolling with the pad foot IC roller.

(Research Team/ISU/TxDOT) A variety of in-situ testing 

measurements.

Tue. July 22 

(Day 2)

Calibration/

Production/

Mapping

(Research Team/Texana_Ammann) Calibration, production 

and mapping rolling with the pad foot IC roller.

(Research Team/ISU/TxDOT) A variety of in-situ testing 

measurements.

Wed. July 23 

(Day 3)

Calibration/

Production/

Mapping

(Research Team/Dynapac/Texana_Ammann) Calibration, 

production and mapping rolling with the Dynapac smooth drum 

and Texan pad foot IC rollers and Texana/Ammann smooth drum 

roller.

(Research Team/ISU/TxDOT) A variety of in-situ testing 

measurements.

Thu. July 24 

(Day 4)

Calibration/

Production/

Mapping/

Open House

(Research Team/Texana) Calibration, production and mapping 

rolling with the Texana/Ammann smooth drum roller.

(Research Team/ISU/TxDOT) A variety of in-situ testing 

measurements.

(Research Team) Analyze and report the IC and in-situ results, 

generating a preliminary report and presentation of results.

(TXDOT) 2-4PM conduct a two-hour Open House to discuss the 

results and lessons learned followed by a field tour.

Fri. July 25 

(Day 5)

Calibration/

Production/

Mapping/

(Research Team/Texana) Calibration, production and mapping 

rolling.

(Research Team/ISU/TxDOT) A variety of in-situ testing 

measurements.
201

An example of an on-site test schedule and machine settings:

Table 17. An example of an on-site test schedule and machine settings

Date TB Strip Layer Machine

Amp 

(mm) Spot Tests Notes/Comments

07/20 ISU meeet TxDOT at 6:30pm. ISU mobile move from maintenance shop to project site.

All parties meet at Sleep Inn at 7:30pm

07/21 1

Calibration

1 Dynapac

0.7

DCP, LWD, NG, 

ST 10’ X 150’ test strip (see Note A)

Production DCP, LWD, NG, 

PLT 40’ X 500’ area (see Note A)

Mapping Auto − Mapping on the production area

07/22 2

Calibration

1 Texana/Ammann

(pad)

0.7

DCP, LWD, NG, 

ST 10’ X 150’ test strip (see Note A)

Production DCP, LWD, NG, 

PLT 40’ X 500’ area (see Note A)

Mapping Auto − Mapping on the production area

07/23

3

Calibration

2 Dynapac

0.7 

DCP, LWD, NG 10’ X 100’ test strip (see Note B)

Production DCP, LWD, NG 40’ X 300’ area (see Note B)

Mapping Auto − Mapping on the production area

4

Calibration

2 Texana/Ammann

(pad)

0.7

DCP, LWD, NG 10’ X 100’ test strip (see Note B)

Production DCP, LWD, NG 40’ X 300’ area (see Note B)

Mapping Auto − Mapping on the production area

5 Mapping 2 Texana/Ammann

(smooth) 0.7 NG, LWD, 

DSPA, FWD See Note C

07/24

6

Calibration

3 Texana/Ammann

(smooth) 

0.7 DCP, LWD, NG, 

PLT, FWD See Notes C, D, and E

Production 0.7

DCP, LWD, NG, 

PLT, DSPA, 

FWD

See Notes C and D

07/25 Mapping Auto, 

0.7, 1.8 − Mapping on the production area

07/24 2-4PM: Open House

Date TB Strip Layer Machine

Amp 

(mm) Spot Tests Notes/Comments

07/20 ISU meeet TxDOT at 6:30pm. ISU mobile move from maintenance shop to project site.

All parties meet at Sleep Inn at 7:30pm

07/21 1

Calibration

1 Dynapac

0.7

DCP, LWD, NG, 

ST 10’ X 150’ test strip (see Note A)

Production DCP, LWD, NG, 

PLT 40’ X 500’ area (see Note A)

Mapping Auto − Mapping on the production area

07/22 2

Calibration

1 Texana/Ammann

(pad)

0.7

DCP, LWD, NG, 

ST 10’ X 150’ test strip (see Note A)

Production DCP, LWD, NG, 

PLT 40’ X 500’ area (see Note A)

Mapping Auto − Mapping on the production area

07/23

3

Calibration

2 Dynapac

0.7 

DCP, LWD, NG 10’ X 100’ test strip (see Note B)

Production DCP, LWD, NG 40’ X 300’ area (see Note B)

Mapping Auto − Mapping on the production area

4

Calibration

2 Texana/Ammann

(pad)

0.7

DCP, LWD, NG 10’ X 100’ test strip (see Note B)

Production DCP, LWD, NG 40’ X 300’ area (see Note B)

Mapping Auto − Mapping on the production area

5 Mapping 2 Texana/Ammann

(smooth) 0.7 NG, LWD, 

DSPA, FWD See Note C

07/24

6

Calibration

3 Texana/Ammann

(smooth) 

0.7 DCP, LWD, NG, 

PLT, FWD See Notes C, D, and E

Production 0.7

DCP, LWD, NG, 

PLT, DSPA, 

FWD

See Notes C and D

07/25 Mapping Auto, 

0.7, 1.8 − Mapping on the production area

07/24 2-4PM: Open House

Data Sources

The data source should be identified prior to execution of any IC plan in order to effectively manage 

the data. This is a crucial step for the success of any IC-related activities.

There are important pre-site visit data needed for the project planning and preparation of experimental 

plans:

• Project design data – including pavement structural design (layer thicknesses and 

materials)

• Project alignment data – including geometric design (in AutoCAD or MicroStation 

formats) or plan files 

• Lab material test data – Job Mix Formula for asphalt, soils properties (PL, LL, 

Proctor data) for embankments, and mixture design for stabilized base
202

• GPS information of the test site (e.g. control points from the survey data are useful to 

prepare “plan files” to delineate road edges in the IC data display.)

On-site data include:

• Field notes

• IC data

• In-situ test and GPS data

• Images and videos

• Interview records and other notes (e.g., open house event)

Post site visit data include:

• Lab test data from cores taken from the fields

• Additional on-site tests (e.g. FWD tests on cooled asphalt pavements)

The subsequent IC data analysis and report should follow the guidelines described under Chapter 4.

IC Data Management

One of the key issues during the IC field implementation is the “data management”. As mentioned 

before, different IC rollers produce IC data in different forms (including: data format, data elements, GPS 

datum, and roller measurement values) and can only be viewed by vendor-specific software tools. In 

addition, other in-situ test data are either recorded manually or by various vendors’ field programs 

depending on types of devices (such as: nuclear gauges, LWD, FWD, DCP, plate loading test, cores, and 

their associated GPS measurements, etc.). Data integration for the above poses great challenges. This in 

turn introduces a steep learning curve for State agencies and paving contractors. 

Objectives of Data Management

Therefore, this section is to briefly lay out the blueprint for the solution of IC data management. 

The primary objectives of data management are to:

• Establish a data management framework to fill the gap for accelerating the 

development of IC QC/QA specifications for subgrade soils, aggregate base, and 

asphalt pavement materials. The focus will be on providing a reliable method to 

capture the maximum potential value added from current IC technology based on

currently used QC/QA field-testing equipment.

• Pave the road for IC data standardization including data requirements, storage 

formats, processes, and reporting.

• Identify and prioritize needed improvements and/or research for IC data management.

Prioritization will be based on the potential for: (1) simplifying IC usage; (2) 

achieving greater IC cost-benefit, etc.; and (3) improved accuracy.

Data Management Framework

The scope of the IC data management under this study is restricted with a local (e.g., test bed) scale. 

It does not intend to cover the global scale of such a pavement management system. The IC data 
203

management framework consists of data collection guidelines, IC data, in-situ test data, correlation 

analysis, and geostatistics (spatial statistical) analysis. These elements are illustrated in Figure 176.

In-situ test IC roller data

Correlation Geostatistics

Data collection 

guidelines

Figure 176. IC data management framework.

The data collection guidelines contain what and how data should be collected. One very important 

aspect of IC-related data collection is GPS references. All data should be associated with accurate GPS 

measurements. Normally, survey grade GPS would be required.

IC data should be collected following proper guidelines for GPS referencing (e.g. UTM zone), 

machine settings (including offsets from the GPS receiver to the bottom center of the roller drums and 

soil/asphalt system selection for some vendors), electronic filename designation (w.r.t. test beds, 

operation settings), and ASCII data export.

In-situ test can create further challenges unless the data collection procedure, GPS referencing, and 

data storage are strictly followed.

The correlation analysis is required to establish confidence of IC roller measurement values (ICMV). 

It is often performed for IC data and in-situ data from calibration test strips. Until a unified ICMV is 

reached, the correlation analysis is the only link among various ICMVs and in-situ tests.

Finally, geostatistical analysis of IC data is essential to evaluate spatial uniformity and other statistical 

characteristics. It is envisioned to be a basis for IC acceptance in future performance-based specifications.
204

IC Specifications

There are IC specifications developed over the years mostly in European countries and, recently, in 

the US with the focus mainly on soils and granular materials. The review of the existing IC specifications 

is described in the followings sections. Alternative approaches to IC specifications are recommended for 

future development.

IC Specification for Soils and Subbase

Overview of Current Specifications

Some agencies (Swedish Road Administration, Federal Ministry of Transport of the Federal Republic 

of Germany, The Austrian Federal Road Administration, International Society for Soil Mechanics and 

Geotechnical Engineering, Mn/DOT, and TXDOT) have developed specifications to facilitate 

implementation of IC into earthwork construction practices. Table 18 provides a summary of key 

elements of the current IC specifications. The specifications typically require performing either PLT or 

LWD on calibration strips to determine average target values (typically based on 3 to 5 measurements) 

and use the same for quality assurance later in production areas. The German specification suggests 

performing at least three PLTs in locations of low, medium, and high degree of compaction during 

calibration process. Further, it is specified that linear regression relationships between roller measurement 

values and plate load test results should achieve a regression coefficient, R ≥ 0.7. 

White and Vennapusa (2009) documented the following as the key attributes required in soil IC 

specifications and noted that the largest dissimilarities exist in the current specifications with the final 

attribute item.

• Descriptions of the rollers and configurations,

• Guidelines for roller operations (speed, vibration frequency, vibration amplitude, and 

track overlap),

• Records to be reported (time of measurement, roller operations/mode, soil type, 

moisture content, layer thickness, etc.),

• Repeatability and reproducibility measurements for IC measurement values (ICMVs),

• Ground conditions (smoothness, levelness, isolated soft/wet spots),

• Calibration procedures for rollers and selection of calibration areas,

• Regression analysis between ICMVs and point measurements,

• Number and location of quality control (QC) and quality assurance (QA) tests,

• Operator training, and

• Acceptance procedures/corrective actions based on achievement of minimum ICMV

target values (ICMV-TVs) and associated variability.
205

Table 18. Review of Current IC Specifications

Reference Equipment Field Size Location Specs Documentation Compaction Specs Speed Freq.

Mn/DOT 

(2007a, b)

Smooth 

drum or 

padfoot 

vibratory 

roller 

(25,000 lbs.)

300 ft x 32 

ft (minimum at 

base). 

Max 4 ft. 

thick.

One 

calibration/ 

control strip 

per type or 

source of 

grading 

material

Compaction, stiffness, 

moisture, QC 

activities, and 

corrective actions 

(weekly report)

90% of the stiffness 

measurements must be at 90% 

of the compaction target value.

Same during 

calibration 

and production 

compaction

ISSMGE 

(2005)

Roller 

chosen by 

experience

100 m by 

the width 

of the site

Homogenous, 

even surface. 

Track overlap 

≤ 10% drum 

width.

Rolling pattern, 

sequence of 

compaction and 

measuring passes; 

amplitude, speed, 

dynamic measuring 

values, frequency, 

jump operation, and 

corresponding 

locations

Correlation coefficient ≥ 0.7. 

Minimum value ≥ 95% of 

Ev1, and mean should be ≥ 

105% (or ≥ 100% during jump 

mode). Dynamic measuring 

values should be lower than 

the specified minimum for ≤ 

10% of the track. Measured 

minimum should be ≥ 80% of 

the specified minimum. 

Standard deviation (of the 

mean) must be ≤ 20% in one 

pass.

Constant 2–6 

km/h

(± 0.2 km/h)

Constant (± 

2 Hz)

Austria —

RVS 

8S.02.6. 

(1999)

Vibrating 

roller 

compactors 

with rubber 

wheels and 

smooth 

drums 

suggested

100 m 

long by 

the width 

of the site

No 

inhomogeneiti

es close to 

surface 

(materials or 

water content). 

Track overlap 

≤ 10% drum 

width.

Compaction run plan, 

sequence of 

compaction and 

measurement runs, 

velocity, amplitude, 

frequency, speed, 

dynamic measuring 

values, jump operation, 

and corresponding 

locations

Correlation coefficient ≥ 0.7. 

Minimum value ≥ 95% of 

Ev1, and median should be ≥ 

105% (or ≥ 100% during jump 

mode). Dynamic measuring 

values should be lower than 

the specified minimum for ≤ 

10% of the track. Measured 

minimum should be ≥ 80% of 

the set minimum. Measured 

maximum in a run cannot 

exceed the set maximum 

(150% of the determined 

minimum).Standard deviation 

(of the median) must be ≤ 

20% in one pass.

Constant 2–6 

km/h

(± 0.2 km/h)

Constant (± 

2 Hz)
206

Reference Equipment Field Size Location Specs Documentation Compaction Specs Speed Freq.

Germany —

ZTVE 

StB/TP BFStB (1994)

Selfpropelled 

rollers with 

rubber tire 

drive are 

preferred; 

towed 

vibratory 

rollers with 

towing 

vehicle are 

suitable.

Each 

calibration 

area must 

cover at 

least 3 

partial 

fields ~20 

m. long

Level and free 

of puddles. 

Similar soil 

type, water 

content, layer 

thickness, and 

bearing 

capacity of 

support layers. 

Track overlap 

≤ 10% 

machine width. 

Dynamic measuring 

value; frequency; 

speed; jump operation; 

amplitude; distance; 

time of measurement; 

roller type; soil type; 

water content; layer 

thickness; date, time, 

file name, or 

registration number; 

weather conditions; 

position of test tracks 

and rolling direction; 

absolute height or 

application position; 

local conditions and 

embankments in 

marginal areas; 

machine parameters; 

and perceived 

deviations

The correlation coefficient 

resulting from a regression 

analysis must be ≥ 0.7. 

Individual area units (the 

width of the roller drum) must 

have a dynamic measuring 

value within 10% of adjacent 

area to be suitable for 

calibration.

Constant

Sweden —

ROAD 94 

(1994)

Vibratory or 

oscillating 

single-drum 

roller. Min. 

linear load 

15–30 kN. 

Rollermounted 

compaction 

meter 

optional.

Thickness 

of largest 

layer 0.2–

0.6 m.

Layer shall be 

homogenous 

and nonfrozen. 

Protective 

layers < 0.5 m 

may be 

compacted 

with sub-base. 

— Bearing capacity or degree of 

compaction requirements may 

be met. Mean of compaction 

values for two inspection 

points ≥ 89% for sub-base 

under roadbase and for 

protective layers over 0.5 m 

thick; mean should be ≥ 90% 

for roadbases. Required mean 

for two bearing capacity ratios 

varies depending on layer 

type.

Constant 2.5–

4.0 km/h
207

Alternative approaches to Soils IC specifications

The following specification options and concepts (Options 1 to 5) were discussed at a national level 

workshop participated in by representatives from many state and federal agencies, contractors, and 

manufacturers (see White and Vennapusa 2009). These specification options differ in the required level of 

upfront calibration work, data analysis, and the level of confidence in the quality of the completed work. 

Option 1: Roller based QC with pre-selected ICMV target values

For this specification option, an appropriate ICMV-TV is pre-selected based on documented case 

histories/literature, a database of information from local projects, laboratory tests, calibration tests on test 

beds of known engineering properties, mechanical apparatus simulating a range of soil conditions, and/or 

numerical modeling. The contractor uses the preselected MV-TV primarily for QC. QA is evaluated using 

a combination of ICMVs and in situ QA point measurements. This option will become more beneficial as 

experience and data become available through implementation of IC on earthwork projects.

Option 2: ICMV maps to target locations for QA test measurements

ICMV geo-referenced maps are used in this specification option to identify “weak” areas to focus on 

QA point measurements. Proper QC measures (e.g., controlling moisture content, lift thickness, etc.) 

should be followed during compaction. The contractor should provide the ICMV map to the field 

inspector for selection of QA test locations. Judgment is involved with selecting the number of tests and 

test locations. Acceptance is based on achievement of target QA point measurement values in rolleridentified “weak” areas. If in-situ test QA criteria are not met, additional compaction passes should be 

performed and/or QC operations should be adjusted (e.g. moisture, lift thickness, etc.) and retested for QA.

Option 3: ICMV target values from compaction curves to target locations for QA point 

measurements

This specification option evaluates the change in ICMVs with successive passes as an indicator of 

compaction quality. As the number of roller passes increases, the change in MV between passes normally 

decreases. A production area is monitored by evaluating the percent change in ICMVs between 

successive passes. Once the percent change of ≤ 5% over 90% (these percentages can be adjusted based 

on judgment and field experience) of the production area between roller passes is achieved, the 

production area is considered fully compacted. This option is more effective for controlled field 

conditions with relatively uniform materials, moisture content, and lift thickness and serves as a QC 

process control for the roller operator. Judgment is involved with selecting the number of tests and test 

locations. Acceptance is similar to Option 1, in that QA testing is targeted in areas with relatively low 

ICMVs.

Option 4: Calibration of ICMV measurements to QA point measurements

This specification option requires calibration of ICMVs to QA point measurements from a 

representative calibration test strip prior to performing production QA testing. The MV-TV is established 

from project QA criteria through regression analysis and applying prediction intervals. For 

modulus/strength measurements simple linear regression analysis is generally suitable, while for 

correlation to dry unit weight/relative compaction measurements, multiple regression analysis including 

moisture content as a variable may be needed. If underlying layer support conditions are heterogeneous, 

relationships are likely improved by performing multiple regression analysis with ICMV or point 

measurement data from underlying layers. Acceptance of the production area is based on achievement of 

MV-TV at the selected prediction interval (80% is suggested) and achievement of target QA point 

measurement values in the areas with MVs < MV-TV.

Option 5: Performance based QA specification with incentive based payment
208

One of the shortcomings of the existing ICMV specifications might be that the acceptance criteria 

(specifically the target limits) are dependent on specific IC technology. This specification option, 

although it requires a more rigorous statistical analysis framework (see White et al. 2009b), could provide 

a consistent means for specifying acceptance criteria. The acceptance criteria for this option are: (a) the 

overall level of critical soil engineering properties over an area achieve the MV-TV and (b) the variability 

of critical soil engineering properties over an area is no more than some specified maximal amount (e.g., 

COV%). These acceptance criteria are established based on regression analysis from calibration, 

applying prediction intervals, accounting for the repeatability and reproducibility errors associated with 

ICMVs and point measurements, and a selected probability or risk level in acceptance decisions. This 

approach could provide a link to performance-based specifications and a quantitative mechanism to 

define incentive-based payment.

IC Specification for HMA

Recommendation for Machines and Settings and Compactions

Based on the demonstration projects, the machines and settings for specifications are recommended 

by the research team, as summarized in Table 19. It includes the specification of IC on HMA pavement 

for the roller equipment, GPS set, documentation, mapping, and machine setting, as recommended by the 

research team based on the demonstration results.

Table 19. IC specifications for Equipment, GPS, Documentation, Mapping, and Machine 

Setting.

Equipment GPS Documentation Mapping Machine 

setting

Smooth 

double-drum 

vibratory 

roller. Sakai 

SW880 and 

SW990 series, 

and Bomag 

tandem IC 

vibratory 

roller

A Real-Time Kinematic 

(RTK); receiver on 

roller, base station; 

precision of 5 cm;

Verify GPS base station 

broadcasting verified on 

the IC rollers and the 

GPS rover units;

verification of spots 

compared to GPS rover 

before compaction

Computer system and software 

is able to present colorcontoured CCV, pass count, 

HMA surface temperature, and 

machine settings (e.g. frequency, 

speed, and amplitude) in real 

time. IC data is exportable in 

ASCII or binary format, which

shall be readable by other 

software like Microsoft Excel 

and TextPad, etc. The exported 

IC data elements for each data 

record should include, but not 

limit to: time stamps, GPS (e.g., 

northing, easting, and elevation), 

vibration frequency, vibration 

amplitude, pass counts, speed, 

directions, ICMV, and HMA 

temperatures.

Mapping is required 

prior to HMA paving

with single or doubledrum vibrated, low 

amplitude and low 

frequency setting (e.g. 

2000 vpm). One pass

at least. Weak spots 

shall be identified and 

treatments may be 

taken before paving 

HMA materials on 

weak spots.

Frequency 

of 2500 vpm 

or above; 

Low 

frequency 

(e.g. 2500 

vpm) 

corresponds 

to higher 

amplitude 

(e.g. 0.6 

mm), higher 

frequency 

(e.g. 3500 

vpm) 

corresponds 

to low 

amplitude 

(e.g. 0.3 

mm); low 

speed (e.g. 3 

mph)

Alternative approaches to HMA IC specifications

Based on the experiences and knowledge learned from the TPF demonstration projects, the research 

team recommends the following specification options for the compaction of HMA materials.

Option 1: Roller-based QC with ICMV target values determined from test strip results
209

For this specification option, an appropriate ICMV target value is selected for a construction project 

with specific pavement structure and materials. A compaction on a test strip (e.g. 100 ft) is required. For 

this test strip, in-situ NG tests are following the breakdown roller at discrete locations (e.g. 10 test spots 

with 20-ft interval between two spots). The linear correlation between NG density and ICMV for all 

passes can be plotted to determine target ICMV corresponding to the density value from the contract 

requirement. A multivariate linear regression may be used to better account for multiple influence factors 

such as machine settings (e.g. frequency, amplitude), environmental conditions (HMA temperatures), and 

pavement conditions of underlying layers (ICMV of the underlying layers). The subsequent production 

rolling can then make use of such target ICMV for QC (e.g., 90% of the ICMV should meet the target 

ICMV).

Option 2: ICMV maps to select locations for QA tests

ICMV geo-referenced maps can be used in this specification option to identify “weak” areas, so that 

the QA point measurements can be reduced and focused on the weak areas. The contractor should provide 

the ICMV map to the field inspector for selection of QA test locations (e.g., 2 to 3 tests per weak areas). 

Acceptance is based on achievement of target QA point measurement values in roller-identified “weak” 

areas. If in-situ test QA criteria are not met, depending on the quality, treatments may be needed and QC 

operations should be adjusted (e.g. density, lift thickness, etc.).

Option 3: Selection of the optimal roller pass count and ICMV target value from a compaction 

curve

This specification option evaluates the optimum roller pass number and the change of ICMV with 

successive passes as an indicator of compaction quality. As the number of roller passes increases, usually 

the ICMV increases first and then decreases or increases continuously until it reaches a relatively constant 

value. From the test strip, the optimum roller pass number achieving the optimum ICMV can be 

identified, which can be set as the roller pass target for the production compaction. This method may help 

avoid over-compaction or under-compaction with excess or insufficient compaction passes. A production 

area can also be monitored by evaluating the percentage change in ICMVs between successive passes. 

For example, once the percent change is less than 5% or the ICMV reach a plateau, the optimal pass count 

and target ICMV can be determined. Then, the acceptance of production rolling can be based on the 

optimal pass count and target ICMV.

Figure 177. Compaction curve – Breakdown Compaction of HMA Base Course - TPF 

WisDOT IC demo (TB 01B-Section 2).

Option 4: Compaction Uniformity Evaluation from the geostatistical semivariograms for QA
210

The geostatistical semivariogram parameters (e.g. sill and range values) can be used to evaluate the 

compaction uniformity of the compaction area to minimize the variability. The compaction area shall be 

divided into multiple segments (e.g. 100 ft per segment) for evaluation on each segment. The 

semivariogram parameters of each segment can be used to evaluate its uniformity. Longer ranges with 

lower sill values indicate better uniformity. Further research is needed to determine target range and sill 

value.

sill / scale

range

nugget

Figure 178. Semivariogram – Breakdown Compaction of HMA Base Course - TPF 

WisDOT IC demo (TB 01B-Section 2).

Generic IC Specifications for Soils, Subbase HMA

IC Specifications for soils, subbase HMA materials were developed under this project to provide 

States guidance to implement IC. These specifications are quality control based and can be served as a 

stepping stone to future quality assurance based specifications. The full contents of the above three IC 

specifications are included in the appendixes: 

• Appendix D - Generic Soils IC Specifications

• Appendix E - Generic HMA IC Specifications

• Appendix F - Generic Subbase IC Specifications
211

IC Training and Workshops

Open House Activities of IC Demonstrations

Following each field demonstration under this project, an Open House was conducted as part of this 

field investigation. The people who attended the Open House include the FHWA and DOT 

engineers/technicians, academics, paving contractors, roller manufacturers/dealer personnel, GPS 

manufacturers/technical supports, etc. Usually the Open House included a two-hour indoor presentation 

and question-&-answer session, followed by an outdoor one-hour equipment demonstration.

Usually the indoor presentation included at least the following:

• FHWA/TPF Intelligent Compaction Project - by Dr. George Chang (Transtec Group)

• Asphalt Intelligent Compaction - by Bob Horan (Asphalt Institute)

• IC Roller System – by the IC roller technical supporters

• GPS System - by Trimble or local dealer technical supporters

• DOT IC Demo and Prelim Results - by Dr. George Chang (Transtec Group)

Following the presentations, issues were discussed during the question-&-answer (Q&A) session.

Subsequently, the outdoor equipment demonstration was performed on a parking lot or the 

construction site. The IC roller manufacturers, GPS dealers, and other in-situ test equipment 

manufacturers introduced the equipment, their functions, and how to operate the equipment.

Recommended Future IC Training and Workshops

Training and workshops are crucial elements for any successful IC implementation – as described in 

the IC Road Map. The following plan is recommended for future IC training and workshops.

Purposes of the Workshops:

• Familiarize attendees with fundamentals of intelligent compaction

• Demonstrate the route to successful IC implementation

• Develop attendees as technology champions of IC for their organizations or 

companies

Target Audiences:

• State DOTs construction and materials QC/QA personnel and specification writers for 

asphalt and soils/subbase

• Industry paving managers, superintendent, and QC personnel for asphalt (HMA) 

paving and earthwork-grading contractors

• IC rollers and Global Position Systems (GPS) vendors/dealers

• Estimated number of attendees is 75 for each workshop. Travel expenses up to 15 

State DOT personnel may be sponsored by the FHWA for each workshop.

Recommended Workshop Presenters:

• Victor (Lee) Gallivan, P.E., FHWA HIPT, COTR of the FHWA/TPF IC Project
212

• Dr. George Chang, P.E., Transtec Group, PI of the FHWA/TPF IC research team

• Bob Horan, P.E., Asphalt Institute, Facilitator of the FHWA/TPF IC research team

• Larry Michael, LLM Asphalt Consultant, Co-PI of the FHWA/TPF IC research team

• Rebecca Embacher, Minnesota Department of Transportation, IC DOT representative

• Dr. David White, ISU, Co-PI of the FHWA/TPF IC research team 

Products and Processes:

• Three seven-hour national IC workshops in different regions of the country

• Generic IC specifications for HMA (asphalt) and soils/subbase

• Workshop contents that cover the following topics under the IC Road Map:

The recommended workshop agenda would include the following sessions:

1 – Introduction and Overview

2 – Fundamentals of Intelligent Compaction

3 – Global Position Systems for IC

4A – IC for HMA

4B – IC for Soils/Subbase (break-out sessions)

5A – Panel discussion on IC for Asphalt

5B – Panel discussion on IC for Soils/Subbase (break-out sessions)

6 – IC-based QC and QA Specifications

7A – Panel discussion on QC/QA HMA IC

7B – Panel discussion on QC/QA for Soils/Subbase IC

8 – Demonstration of IC Data Management program

9 – Panel Discussion (DOTs) on IC Implementation and Barriers-to-Overcome plus Q&As

10 – Conclusion and Workshop Evaluations
213

References

Adam, D., and Kopf, F. (2004). “Operational devices for compaction optimization and 

quality control (Continuous Compaction Control & Light Falling Weight Device).” Proc., of the 

Intl. Seminar on Geotechnics in Pavement and Railway Design and Construction, December, 

Athens, Greece (Invited paper), 97-106.

Anderegg, D.A., and Kaufmann, K. (2004). “Intelligent compaction with vibratory rollers -

feedback control systems in automatic compaction and compaction control,” Transportation 

Research Record No. 1868, Journal of the Transportation Research Board, National Academy 

Press, 124-134.

Anderegg, R., von Felten, D., and Kaufmann, K. (2006). “Compaction monitoring using 

intelligent soil compactors.” Proc., GeoCongress 2006: Geotechnical Engineering in the 

Information Technology Age, February, Atlanta, CD-ROM.

Brandl, H., and Adam, D. (1997). “Sophisticated Continuous Compaction Control of Soils 

and Granular Materials” Proc., XIVth Intl. Conf. on Soil Mechanics & Foundation Engineering, 

Vol. 1, September, Hamburg, Germany.

BTM (1983). Bomag Terrameter – Product Bulletin, Bomag-AMCA International, Postfach 

180, West Germany.

Camargo, F., Larsen, B., Chadbourn, B., Roberson, R., and Siekmeier, J. (2006). “Intelligent 

compaction: a Minnesota case history.” Proc., 54th Annual University of Minnesota 

Geotech.Conf., February, Minneapolis, CD-ROM..

Clark, I., and W. Harper. Practical Geostatistics 2000. 3rd edition, Ecosse North America Llc, 

Columbus, OH, 2002.

DipI.-Ing., and D. Adam. Standardization, Design, Quality Assurance and Monitoring of

Earth Works in Road Engineering in Austria (powerpoint presentation). Utépítés és geotechnika –

szabályok és tapasztalatok, MAKADÁM-Klub, Budapest, Lövőház u. 15., 2007, pp. 1-33.

Floss, R., Gruber, N.,and Obermayer, J. (1983). “A dynamical test method for continuous 

compaction control.” Proc. 8th European Conf. on Soil Mechanics and Foundation Engineering, 

Rathmayer, H.G., and Saari, K.H.O., Eds., May, Helsinki, 25-30.

Forssblad, L. (1980). “Compaction meter on vibrating rollers for improved compaction 

control”, Proc., Intl. Conf. on Compaction, Vol. II, 541-546, Paris.

Gorman, P. and Mooney, M. (2003). "Monitoring roller vibration during compaction of 

crushed rock," Proc., 20th Intl. Symp. on Automation and Robotics in Construction, Eindhoven, 

Netherlands, Ger Maas & Frans van Gassel, Eds., 415-419. 

Hansbo, S., and Pramborg, B. (1980). “Compaction control.” Proc., Intl. Conf. on 

Compaction, Vol. II, 559-564, Paris.

Hertz, H. (1895). Über die Berührung fester elasticscher Körper, Gesammelte Werke, Bd. 1. 

Leipzig.

Hoover, J.M. (1985). In-situ stability of smooth-drum vibratory compacted soils with Bomag

Terrameter, Engineering Research Instititute, ERI Project No. 1722, Iowa State University, Ames, 

Iowa, March. ISSMGE. (2005). Roller-Integrated continuous compaction control (CCC): 

Technical Contractual Provisions, Recommendations, TC3: Geotechnics for Pavements in 

Transportation Infrastructure. International Society for Soil Mechanics and Geotechnical 

Engineering. 
214

Hossain, M., Mulandi, J., Keach, L., Hunt, M., and Romanoschi, S. (2006). “Intelligent 

compaction control.” Proc., 2006 Airfield and Highway Pavement Specialty Conf., ASCE, May, 

Atlanta, Ga.

Isaaks, E.H., and R.M Srivastava. An Introduction to Applied Geostatistics. Oxford Univ. 

Press, New York, 1989.

Kauffman, K., Anderegg, R. (2008). “3D-Construction Applications III GPS-based 

Compaction Technology”, 1st Intl. Conf. on Machine Control & Guidance 2008, ETH Zurich, 

Switzerland. 

Kröber, W. (1988). “Untersuchung der dynamischen Vorgänge bei der Vibrationsverdichtung 

von Böden,” Ph.D. Dissertation, Schriftenreihe, Heft 11, Lehrstuhl und Prüfamt für Grundbau, 

Bodenmechanik und Felsmechanik der Technischen Universität München (in German).

Kröber, W., Floss, E., Wallrath, W. (2001). “Dynamic soil stiffness as quality criterion for 

soil compaction,” Geotechnics for Roads, Rail Tracks and Earth Structures, A.A.Balkema 

Publishers, Lisse /Abingdon/ Exton (Pa) /Tokyo, 189-199. 

Lemaire, C.E., Vandanjon, P.O., Motion in planning taking into account the dynamic model 

of vehicles: application to the compactor, 1999.

Lundberg, G. (1939). Elastische Berührung zweier Halbräume, Forschung, auf dem Gebiete 

des Ingenieurwesens, Band 10, Göteborg, 201–211.

Machet, J.M. (1980). “Compactor-mounted control devices”, Proc., Intl. Conf.on Compaction, 

Vol. II, 577-581, Paris.

Masad, E.S., Koneru, K. Rajagopal, T. Scarpas, and C. Kasbergen, Modeling of asphalt 

mixture laboratory and field compaction using a thermodynamics framework, Journal of the 

Association of Asphalt Paving Technologist, 2009.

Mn/DOT. (2007a). Excavation and embankment – (QC/QA) IC quality compaction (2105) 

pilot specification for granular treatment S.P.0301-47. Minnesota Department of Transportation, 

St. Paul, Mn. 

Mn/DOT. (2007b). Excavation and embankment – (QC/QA) IC quality compaction (2105) 

pilot specification for non-granular soils S.P.6211-81. Minnesota Department of Transportation, 

St. Paul, Mn. 

Mooney, M.A., Gorman, P.B., Tawfik, E.F., Gonzalez, J.N. and Akanda, A.S. (2003). 

Exploring Vibration-Based Intelligent Soil Compaction, Oklahoma Department of Transportation 

Report, Item 2146.

NCHRP 21-09. (2010). Intelligent soil compaction systems – NCHRP 21-09, National 

Cooperative Highway Research Program, Transportation Research Board, Washington, D.C.

Newman, K., and White, D. (2008). “Rapid assessment of cement/fiber stabilized soil using 

roller-integrated compaction monitoring.” Transportation Research Record, 2059, 95-102.

Nohse, Y., and M. Kitano. Development of A New Type of Single Drum Vibratory Roller. 

Proc. 14th Intl. Conf. of the Intl. Soc. For Terrain-Vehicle Systems, Vicksburg, MS, 2002, pp. 1–

10. 

Nohse, Y., Uchiyama, K., Kanamori, Y., Kase, J., Kawai, Y., Masumura, K., and Tateyama, 

K. (1999). “An attempt applying a new control system for the vibratory compaction using GPS 

and CMV in the embankment construction (Part 1).” Proc. of the 13th Intl. Conf. of the ISTVS: 

Okinowa, Japan, 295-300.
215

Petersen, D., Siekmeier, J., Nelson, C., Peterson, R. (2006). “Intelligent soil compaction –

technology, results and a roadmap toward widespread use.” Transportation Research Record No. 

1975, Journal of the Trasnsportation Research Board, National Academy Press, 81-88.

Petersen, L., and Peterson, R. (2006). Intelligent Compaction and In-Situ Testing at Mn/DOT 

TH53, Final Report MN/RC-2006-13, May, Minnesota Department of Transportation, St. Paul, 

Mn.

Preisig, M., Caprez, M., and Amann, P. (2003). “Validation of continuous compaction control 

(CCC) methods.” Workshop on Soil Compaction, September, Hamburg.

ROAD 94 (1994). General technical construction specification for roads – Unbound 

Pavement Layers, Road and Traffic Division, Sweden.

RVS 8S.02.6. (1999). “Continuous compactor integrated compaction – Proof (proof of 

compaction),” Technical Contract Stipulations RVS 8S.02.6 – Earthworks, Federal Ministry for 

Economic Affairs, Vienna.

Sakai Heavy Industries, Ltd. Vibrating Roller Type Soil Compaction Quality Controller CCV 

(Compaction Control Value), Operating & Maintenance Instructions, 2007, pp. 1–5.

Sandström A.J., and Pettersson, C.B. (2004). "Intelligent systems for QA/QC in soil 

compaction", Proc., 83rd Annual Transportation Research Board Meeting, January 11-14. 

Washington, D.C. 

Sandström, Å. (1994). Numerical simulation of a vibratory roller on cohesionless soil,

Internal Report, Geodynamik, Stockholm, Sweden. 

Scherocman, J., S. Rakowski, and K. Uchiyama. Intelligent compaction, does it exist? 2007 

Canadian Technical Asphalt Association (CTAA) Conference, Victoria, BC, 2007, pp. 1–25.

Singh, D., Mai, A.T., Beainy, A.F., and Zaman, M.M., In situ measurement of the stiffness 

during the construction of a HMA pavement, Transportation Research Board C.D. Rom, 2010, 

Washington D.C. 

Tawfik, E.F., Validation of numerical evaluation of dynamic response of using runge-kuttaNystrom (R-K-N) method, 2006.

Tehrani, F.S. and Meehan, C.J. (2009). “Continuous compaction control: Preliminary data 

from a Delaware case Study.” Eighth Intl. Conf. on the Bearing Capacity of Roads, Railways, 

and Airfields (BCR2A’09), June 29 – July 2, Champaign, Illinois.

The Transtec Group, Inc. Intelligent Compaction website, 2008-2010 

(http://www.intelligentcompaction.com/index.php?q=node/4)

Thompson, M., and White, D. (2008). “Estimating compaction of cohesive soils from 

machine drive power.” J.of Geotech. and Geoenviron. Engrg, ASCE, 134(12), 1771-1777.

Thompson, M., White, D., Gieselman, H., and Siekmeier, J. (2008). “Variable feedback 

control intelligent compaction to evaluate subgrade and granular pavement layers – Field study at 

Minnesota US 14.” Proc., 87th Annual Transportation Research Board Meeting, Washington, 

D.C. 

Thurner, H. (1980). “The compactometer principle: Contribution to the discussion in Session 

IV.” Proc., Intl. Conf. on Compaction, Vol. II, Paris.

Thurner, H. (1993). “ Continuous compaction control - specifications and experience.” Proc., 

XII IRF World Congress, 951-956, Madrid.
216

Thurner, H. and Sandström, Å. (1980). “A new device for instant compaction control.” Proc., 

Intl. Conf. on Compaction, Vol. II, 611-614, Paris.

Thurner, H., Sandström, Å. (2000). “Continuous Compaction Control, CCC.” Workshop on 

Compaction of Soils and Granular Materials, Modeling of Compacted Materials, Compaction 

Management and Continuous Control, International Society of Soil Mechanics and Geotechnical 

Engineering (European Technical Committee), 237-246, Paris. 

Van Susante, P.J., Mooney, M.A., Capturing nonlinear vibratory roller compactor behavior 

through lumped parameter modeling, Journal of Engineering Mechanics, Vol. 134, No. 8, 2008.

Vennapusa, P., White, D.J., Gieselman, H. (2009). “Influence of support conditions on rollerintegrated machine drive power measurements for granular base.” Intl. Foundation Congress and 

Equipment Expo (IFCEE) 2009, 15-19 March, Orlando, Florida.

Vennapusa, P.K.R., D.J. White, and M.D. Morris. Geostatistical Analysis for Spatially 

Referenced Roller-Integrated Compaction Measurements. Journal of Geotech and 

Geoenvironmental Engineering, Vol. 136, No. 6, 2010, pp. 813-822.

Victor L. Gallivan, Chang, G.K., Xu, Q., and B. Horan. Validation of intelligent compaction 

measurement systems for practical implementation. CD-ROM. Transportation Research Board of 

the National Academies, Washington, D.C., 2011, pp. 1–13.

White, D.J., M. Thompson, and P. Vennapusa. Field Validation of Intelligent Compaction 

Monitoring Technology for Unbound Materials. Mn/DOT Report No. MN/RC 2007–10, 

Minnesota Department of Transportation, 2007.

White, D.J., Vennapusa, P. (2009). Report of the Workshop on Intelligent Technologies for 

Earthworks, EERC Publication ER09-02, Earthworks Engineering Research Center, Iowa State 

University, Ames, Iowa.

White, D.J., Vennapusa, P., Gieselman, H., Johanson, L., Siekmeier, J. (2009a). “Alternatives 

to heavy test rolling for cohesive subgrade assessment,” Eighth Intl. Conf. on the Bearing 

Capacity of Roads, Railways, and Airfields (BCR2A’09), June 29 – July 2, Champaign, Illinois.

White, D.J., Vennapusa, P., Zhang, J., Gieselman, H., Morris, M. (2009b). Implementation of 

Intelligent Compaction Performance Based Specifications in Minnesota, EERC Publication 

ER09-03, MN/RC 2009-14, Minnesota Department of Transportation, St. Paul, Minnesota, March.

Xu, Q., and M. Solaimanian. Modelling Linear Viscoelastic Properties of Asphalt Concrete 

by The Huet-Sayegh Model. International Journal of Pavement Engineering, Vol. 10, No. 6, 2009, 

pp. 401–422.

Xu, Q., Chang, G.K., Vennapusa, P., White, D.J., Horan, B., Michael, L., and Victor L. 

Gallivan. Hot mix asphalt intelligent compaction–a case study. CD-ROM. Transportation 

Research Board of the National Academies, Washington, D.C., 2010, pp. 1–17.

Xu, Q., Chang, G.K., Victor L. Gallivan, and B. Horan. Data analysis for hot mix asphalt 

intelligent compaction. CD-ROM. Transportation Research Board of the National Academies, 

Washington, D.C., 2011, pp. 1–17.

Yoo, T., and Selig, E. (1980). “New concepts for vibratory compaction of soil”, Proc., Intl. 

Conf. on Compaction, Vol. II, 703-707, Paris.

ZTVE StB/TP BF-StB. (1994). Surface Covering Dynamic Compaction Control Methods –

German Specifications and Regulations, Additional Technical Contractual Conditions and 

Guidelines for Earthwork in Road Construction and Technical Testing Instructions for Soil and 

Rock in Road Construction, Research Society of Road and Traffic, Germany.
217

Appendix A. Understand Semi-variogram as a Metric for Uniformity

A variogram is a plot of the average squared differences between data values as a 

function of separation distance, and is a common tool used in geostatistical studies to 

describe spatial variation. Three important features of a semi-variogram include: sill, 

range, and nugget (Figure 179, where Gamma Value is the variogram value). Sill is 

defined as the plateau that the semi-variogram reaches, range is defined as the distance at 

which the semi-variogram reaches the sill, and nugget is defined as the vertical height of 

the discontinuity at the origin which mostly represents sampling error or short scale 

variations (Srivastava, 1996).

Figure 179. Semi-variogram models with exponential and Gaussian fits (Houlding, 2000).

From a semi-variogram model, a low “sill” and longer “range of influence” can represent 

best conditions for uniformity, while the opposite represents an increasingly non-uniform 

condition. Using these semi-variogram parameters, theoretical models can be fit to the 

experimental semi-variogram data. Exponential model (the solid line in Figure 179) 

generally fits well with the roller-integrated and in-situ compaction measurements (see 

White et al. 2007a, White et al. 2007b, Vennapusa and White 2008). Detailed 

descriptions of theoretical models can be found elsewhere in the literature (e.g., Clark and 

Harper 2002).

Data conversion

Spatial variability/continuity depends on the detailed distribution of the petrophysical 

attribute. Attributes, such as permeability with highly skewed data distribution present problems 

in variogram calculation, and the extreme values have significant impact on varigoram. 
218

Therefore, before analyzing the semivariogram raw IC data is transformed to the logarithmical 

scale first:

log ( ) 10 Z = z (16)

Location and lag vector definition 

The location vector and lag vector is shown in Figure 180 for a 2-dimentional (2-D) space.

Location vector (μ)

Lag vector (h)

Location vector (μ+h)

Origin

Location vector (μ)

Lag vector (h)

Location vector (μ+h)

Origin

Figure 180. location and lag vectors.

Direction and model parameter definition

The direction specification (irregular) is defined by users, as shown in Figure 181. The 

direction (angle), lag number, interval for lag distance, bandwidth, and total length (distance) are 

defined by user.
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Y axis (North)

X axis (East)

Lag Tolerance

Azimuth

Lag Distance

Lag 1

Lag 2

Lag 3

Lag 4

Bandwidth

Azimuth tolerance

Y axis (North)

X axis (East)

Lag Tolerance

Azimuth

Lag Distance

Lag 1

Lag 2

Lag 3

Lag 4

Bandwidth

Azimuth tolerance

Figure 181. semnivariogram direction.

Calculation of semi-variogram:

The variogram for lag distance h is defined as the average squared difference of values 

separated approximately by h , for all possible location μ :

{[ ] }2 2γ (h) = E Z(u) − Z(u + h) (17)

Where,

μ is the location;

h is the lag distance ( h could be constant for the same lag area).

The variogram can be re-expressed as follows:

= ∑[ − + ]

( )

2 ( ) ( ) ( )

1 2 ( )

N h

Z u Z u h

N h γ h

(18)

Where,

N(h) the number of pairs for lag distance h of a specific lag area

The semi-variogram is then defined as follows:

= ∑[ − + ]

( )

2 ( ) ( ) 2 ( )

1 ( )

N h

Z u Z u h

N h γ h (19)

Figure 182 shows that one moves from one node to the next to determine the

Z(u) − Z(u + h) value for the calculation of variogram.
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Y axis (North)

Lag 1

Lag 2

Lag 3

Lag 4

Figure 182. semivariogram direction.

Model fit

The determined semivariograms for each lag area is plotted with lag distance as shown in 

Figure 183, which is the experimental semivariogram. Variogram

γ (h)

Lag distance (h)

Increase 

variability

No correlation

Variogram

γ (h)

Lag distance (h)

Increase 

variability

No correlation

Figure 183. Experimental semivariogram.

In this project the Surfer@ software is used to produce the semivariogram of RMV values for 

the final pass of the base and binder course. Based on the data analyzed, it appears that the 
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exponential model or the Gaussian model (see Error! Reference source not found.) can fit the 

data better than other models (e.g. linear model).

Different mathematical models can be used to fit the experimental semivariograms, including 

the power model, exponential model, and the Gaussian model, etc. 

Power model:

c

a

h

r h a 



 



 ( ) = . (20)

Exponential model: 





 



 



 



 = − − a

h

r h c

3 ( ) 1 exp (21)

Gaussian model:





 



















 = − − 2

2 9 ( ) 1 exp

a

h

r h c (22)

Figure 184 shows the experimental semivariogram of the IC demonstration project and fitted 

curves with the exponential, Gaussian, and power model, respectively.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 50 100 150 200

Lag distqance (m)

Semivariogram

Experimental semivariogram

Exponential model

Gaussian model

Pow er model

Figure 184. Semivariogram model fit.

The field demonstration projects have shown that an exponential model generally fits well for 

IC measurement data, thus it is used in the Veda - IC Viewer software.

The semivariogram reports in Veda include:

• Plot: semi-variogram vs. lag distance – with data points and fitted curve.

• Parameters: determined from the fitted curve based on a exponential model

• Range (horizontal range)
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• Variance (known as sill which the sum of vertical scale and nugget effect)

• Vertical scale (vertical scale for the structured component of the variogram)

• Sampling error (known as nugget effects that quantifies the sampling and 

assaying erros and the short scale variability)

Low variance (sill) and longer range (of influence) represent better uniformity.

Figure 185. Semi-variogram report of IC data.
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Appendix B. Understand GPS and Datum

How GPS Works

The Global Positioning System (GPS) is a location system based on satellite signals and 

their ground stations. GPS was developed by the United States Department of Defense 

(DOD), for its application as a locating utility, but GPS has proven to be a useful tool in 

non-military mapping applications.

The GPS, also known as Navstar GPS, consists of three segments as shown in Figure 186:

• Space Segment: a constellation of satellites orbiting about 20,000 km above the earth’s 

surface which transmit ranging signals on two frequencies in the microwave part of the 

radio spectrum.

• Control Segment: a control segment which maintains GPS through a system of ground 

monitor stations and satellite upload facilities.

• User Segment: the receivers – both civil and military.

(Courtesy of Aerospace)

Figure 186. Three segments of GPS.

The receiver can use the trilateration technique to compute distances to four or more 

satellites simultaneously and knowing the exact locations of the satellites. Then, the 

receiver can determine its latitude, longitude, and height while at the same time 

synchronizing its clock with the GPS time standard.

Most civil GPS receivers access the C/A-code (coarse/acquisition code) transmitted on 

the L1 frequency (1575.42 MHz). Military receivers, in addition, use the encrypted Pcode (precise or precision code) which is transmitted on both L1 and the L2 frequency 

(1227.60 MHz). Some military receivers can access the P-code directly rather than 

acquiring the C/A-code first and then transferring to the P-code. Since 2005, the newest 
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satellites transmit a civil signal, L2C, on the L2 frequency that makes GPS, a.k.a. carrier 

phase tracking or surveying, more precise.

Depending on the quality of the receiver, the environment, the type of measurements 

made, and how the measurements are processed, the positioning accuracy of GPS can 

vary from a few meters to below 1 centimeter, permitting a wide range of positioning 

applications from vehicle navigation to studies of the motion of the Earth's tectonic plates.

Use of Differential GPS in IC Systems

GPS is used in the IC system to record the coordinates of rollers at each pass (see Figure 

187). However, there are several external sources that may introduce errors into a GPS 

position including: atmospheric conditions, ephemeris errors/clock drift/measurement 

noise, selective availability, multipath, and etc. The IC systems normally use differential 

correction GPS to improve accuracies. Differential correction requires a second GPS 

receiver, a base station, collecting data at a stationary position on a precisely known point 

(e.g., a surveyed benchmark). With the known physical location of the base station, a 

correction factor can be computed by comparing the known location with the GPS 

location determined by using the satellites. Then, the correction factor can be applied to 

the GPS data collected by a GPS receiver in the field.

(Courtesy of Bomag)

Figure 187. Use GPS in an IC system.
225

Various IC systems are normally equipped with Real Time Kinematic (RTK) GPS 

receiver but have slightly different requirements for GPS connection from the receiver to 

the IC control panels.

Table 20: GPS connection requirements for IC rollers

Vendor Case/Ammann Bomag Caterpillar Dynapac Sakai

Software System ACEPlus BCM05 AccuGrade NA Aithon MT

Interface Serial

Serial 

(sub-D 9 pin) NA NA NA

Baud rate 38400 38400 NA NA NA

Data bits 8 NA NA NA

Parity None None NA NA NA

Stop bits 1 1 NA NA NA

Flow control None/hardware NA NA NA

GPS mode RTK RTK? RTK NA RTK

Sampling rates 1 Hz 10 Hz NA NA NA

Type of message NMEA NMEA NA NA NA

Subtype GGA GGA NA NA NA

NMEA: The National Marine Electronics Association (NMEA) has developed a

specification that defines the interface between various pieces of marine electronic 

equipment. GPS receiver communication is defined within this specification. NMEA 

consists of sentences, the first word of which, called a data type, defines the interpretation 

of the rest of the sentence. Each Data type would have its own unique interpretation and 

is defined in the NMEA standard.

GGA: The GGA sentences are essential fix data which provide 3D location and accuracy 

data.

Position and Time from GPS

The GPS signals received by the receivers including position, velocity and time, etc. 

(Figure 188) can be processed to compute earth-centered, earth-fix (ECEF) x-y-z position 

and time of the receiver (Figure 189). The earth centered position x-y-z is then converted 

to geodetic coordinates: latitudes, longitudes, and height above the ellipsoid (Figure 190).

(Courtesy of Dana)

Figure 188. Position, velocity and time from GPS.
226

(Courtesy of Dana)

Figure 189. Earth-centered, earth-fix x-y-z GPS position. 

(Courtesy of Dana)

Figure 190. Geodetic coordinates – latitudes, longitudes, and height above the ellipsoid. 

Use of UTM in IC Data Process

The Universal Transverse Mercator Coordinate (UTM) system provides coordinates, 

northings and eastings, on a world wide flat grid for easy computation. Therefore, UTM 

is normally used in the IC data processing. The Universal Transverse Mercator 

Coordinate system divides the Earth into 60 zones, each being 6 degrees longitude wide, 

and extending from 80 degrees south latitude to 84 degrees north latitude. The polar 

regions are excluded. The first zone starts at the International Date Line (longitude 180 

degrees) proceeding eastward. Cautions should be taken when the area of interest 
227

extends from one zone to another. Under this study, the UTM zones are restricted in the 

US from zone No. 10 to zone No. 19 as shown in Figure 191.

Figure 191. UTM Zones in the US.

Datum Conversion between Geodetic Longitude/latitude and UTM

Conversion from geodetic coordinates (longitude/latitude) to UTM is recommended to be 

based on the World Geodetic System 84 (WGS84) and North American Datum of 1983 

(NAD83). The conversion algorithm is based on the US Army (revised from 

http://www.uwgb.edu/dutchs/UsefulData/UTMFormulas.htm ).
228

Appendix C. How to Setup GPS for IC

GPS setup is currently one of the most challenging steps for IC implementation. There 

are many pitfalls for losing or degrading IC data due to incorrect GPS setup. The 

followings are the specific procedures to do it properly. (Courtesy of TopCon)

Step 1 – Select a desired coordinate system or datum

• Select the datum to be used for this project. The order of preferred coordinate 

systems is: UTM, state plane, and local coordinate system.

Step 2 – Setup a GPS base station

• Select a known position with established NEZ (northing-easting-elevation) 

coordinates w.r.t. the selected coordinate system.

• Use a network rover and dialed up a specific State reference station network.

• Launch GPS software and apply the desired datum (e.g., UTM) for a 

projection.

• Set up a tripod, and install the receiver on top of the tripod.

• Measured the point using a 2-minute occupation time and save it as the base 

station location.

Figure 192. A Trimble GPS base station (left) and a TopCon GPS base station 

(ICPF WisDOT demo).

Step 3 – Setup hand-held GPS receiver (rover)

• Set up the handheld GPS geodetic receiver (rover) on the tripod. Start the base 

station using the measured coordinates to broadcast the NEZ coordinates of 

the selected datum (e.g., UTM) for that location.

• If the NEZ values are not being broadcasted from the base, the GPS data can 

have a horizontal error up to 12 to 15 feet. This is called an autonomous start, 

which only broadcasts the Latitude and Longitude of the base station and does 

not apply the datum correction.

• Caution: If one base station is started autonomously and then restarted with 

the UTM correction applied, the measurement of the same point will have 
229

different NEZ values depending on the UTM grid factor that is applied by the 

software.

• While the use of an autonomous start provides relative reference points, the 

use of a state plane or UTM base station start is strongly recommended to 

ensure GPS measurements at any time (e.g., after the project is complete) can 

be at the exact locations.

Figure 193. A Trimble GPS rover (left) and a TopCon GPS rover (ICPF WisDOT 

demo).

Step 3 – Setup GPS receiver on IC rollers

• Launch the onboard display software on the IC roller. Check the status of the 

GPS signals. Read out the GPS measurements from the GPS receiver on the 

IC roller.

• Remove the rover receiver unit from the rover and stack it on top of the 

receiver unit on the IC roller. Read out the GPS measurements from the rover 

and compare it with that from the previous measurements from the roller unit. 

The tolerance should be within 1⁄2”.
230

Figure 194. Validation of roller mounted GPS with a hand-held rover (ICPF 

VADOT demo).

Appendix D. Generic Soils IC Specification

The following is a generic soils IC specification that can be served as a national guideline 

for States to implement IC.

Intelligent Compaction Technology for Soils Applications

DESCRIPTION

This work shall consist of the construction of the roadway fill embankment 

utilizing Intelligent Compaction (IC) rollers within the limits of the work as described in 

the plans. IC is defined as a process that uses vibratory rollers equipped with a 

measurement/documentation system that automatically records various critical 

compaction parameters correlated to agency standard testing protocols in real time during 

the compaction process. IC uses roller vibration measurements to assess the mechanistic 

soils properties and to ensure optimum compaction is achieved through continuous 

monitoring of the operations. Additional information on the IC technology may be found 

on the website www.intelligentcompaction.com/index.php?q=node/4 and from the 

Transportation Research Board - NCHRP Report 676 on Intelligent Soil Compaction 

Systems. 

The Contractor shall supply sufficient numbers of rollers and other associated 

equipment necessary to complete the compaction requirements for the specific materials. 

The Contractor will determine the number of IC rollers to use depending on the scope of 
231

the project. The IC roller(s) may be utilized during production with other standard 

compaction equipment and shall be used for the evaluation of the compaction operations. 

EQUIPMENT

The IC rollers shall meet the following specific requirements:

1. IC rollers shall be self propelled single-drum vibratory rollers equipped with 

accelerometers mounted in or about the drum to measure the interactions between the 

rollers and compacted materials in order to evaluate the applied compaction effort. 

Rollers may be smooth or pad footed drums.

2. The output from the roller is designated as the Intelligent Compaction 

Measurement Value (IC-MV) which represents the stiffness of the materials based 

on the vibration of the roller drums and the resulting response from the underlying 

materials.

3. The IC rollers shall include an integrated on-board documentation system that is capable 

of displaying real-time color-coded maps of IC measurement values including the 

stiffness response values, location of the roller, number of roller passes, machine settings, 

together with the speed, frequency and amplitude of roller drums. The display unit shall 

be capable of transferring the data by means of a USB port.

4. Roller mounted GPS radio and receiver units shall be mounted on each IC roller. RTKGPS radio and receivers are required to monitor the location and track the number of 

passes of the rollers.

Real Time Kinematic Global Positioning System (RTK-GPS)

The Universal Transverse Mercator (UTM) Coordinates system divides the surface of 

Earth between 80°S and 84°N latitude into 60 zones, each 6° of longitude in width and centered 

over a meridian of longitude. Zone 1 is bounded by longitude 180° to 174° W and is centered on 

the 177th West meridian. Zones outside of the Continental United States can be acquired on the 

web at http://www.dmap.co.uk/utmworld.htm. The UTM for this project is Zone (xx) N. (DOT 

to fill in the zone number) 
232

Base Station - Ground mounted or virtual GPS base units that record values in northing, 

easting, and the elevation data in meters using the UTM coordinate system along with the 

longitude/latitude of the measurement values shall be provided. The GPS base station shall 

broadcast updated correction data to the GPS receivers on the IC rollers and the hand-held rovers 

during operations with a survey tolerance of not greater than 1.6 in. (40 mm) in both the 

horizontal (x and y) directions. 

Rover - A portable hand-held GPS radio/receiver for in-situ point measurements shall be 

provided.

Data Analysis Software - Standardized data analysis software (Veda Alfa Vr.8.0 

or later) is available on the website 

http://www.intelligentcompaction.com/index.php?q=node/4 or will be provided by 

xxDOT. The software program will utilize the IC-MV data from the IC roller for analysis 

of coverage, uniformity, and stiffness values during construction operations. As a 

minimum, the following Essential IC Data Information and IC Data Elements shall be 

available in either ASCII or text format for post processing.

• Essential IC Data Information:

Item 

No.

Description

1 Section Title

2 Machine Manufacture

3 Machine Type

4 Machine Model

5 Drum Width (m)

6 Drum Diameter (m)

7 Machine Weight (metric ton)

8 Name index of intelligent compaction measurement values (ICMV)
233

9 Unit index for IC-MV

10 Reporting resolution for independent IC-MVs – 90 degrees to 

the roller moving direction (mm)

11 Reporting resolution for independent IC-MVs – in the roller 

moving direction (mm)

12 UTM Zone

13 Offset to UTC (hrs)

14 Number of IC data points

• Essential IC Data Elements:

Item 

No.

Date Field Name Example of Data

1 Date Stamp (YYYYMMDD) e.g. 20080701

2 Time Stamp (HHMMSS.S -military 

format)

e.g. 090504.0 (9 hr 5 

min. 4.0 s.)

3 Longitude (decimal degrees) e.g. 94.85920403

4 Latitude (decimal degrees) e.g. 45.22777335

5 Easting (m) e.g. 354048.3

6 Northing (m) e.g. 5009934.9

7 Height (m) e.g. 339.9450

8 Roller pass number e.g. 2

9 Direction index e.g., 1 forward, 2 reverse

10 Roller speed (kph) e.g. 4.0

11 Vibration on e.g., 1 for yes, 2 for no

12 Frequency (vpm) e.g. 3500.0

13 Amplitude (mm) e.g. 0.6

14 Surface temperature (oC) - HMA e.g. 120

15 Intelligent compaction measurement 

values

e.g. 20.0

QUALITY CONTROL PLAN 

The Contractor shall prepare and submit a written Quality Control Plan (QCP) for 

the project. As a minimum, the QCP shall contain the following information: 

General Requirements.

1. QCP shall be contract specific, stating how the contractor proposes to 

control the materials, equipment, and construction operations including 

subcontractors and suppliers as well as production facilities and 

transportation modes to the project for the embankment operations.
234

2. The QCP shall include an organizational chart showing all quality control 

personnel and how these personnel integrate with other 

management/production and construction functions and personnel.

3. The QCP shall be signed and dated by the Contractor’s representative at 

the time the QCP is submitted to the Engineer. The QCP shall be 

submitted no later than 15 days prior to commencing the embankment 

operations.

4. The xxDOT will review, sign, and date the QCP if the contents of the QCP 

are in compliance with the requirements as stated herein.

5. The QCP shall be maintained to reflect the current status of the operations, 

and revisions shall be provided in writing prior to initiating the change. 

The QCP revision shall not be implemented until the revision has been 

accepted. 

6. The QCP shall contain the name, telephone number, duties, and employer 

of all quality control personnel necessary to implement the QCP. The 

minimum qualifications of quality control personnel shall be as follows:

a. QCP Field Manager or Plan Administrator. The person 

responsible for the execution of the QCP and liaison with the 

Engineer. Additionally the QCP Field Manager requirements 

include:

1. Full-time employee of the Contractor or an independent 

consultant not involved with the Quality Assurance 

(acceptance) activities on the project.

2. Minimum (x) years experience (as determined by the DOT) in 

quality control activities in construction operations 

3. Full authority to institute actions necessary for successful 

implementation of the QCP. 

b. Quality Control Technician (QCT). The person(s) responsible for 

conducting quality control and inspection activities to implement 

the QCP. There may be more than one QCT on a project.

1. Full-time employee of the Contractor or an independent 

consultant with a minimum (x) years experience (as 

determined by the DOT) in quality control activities in 

construction operations. 

2. Completed the xxDOT requirements for the applicable testing.
235

3. Full authority to institute actions necessary for successful 

implementation of the QCP.

c. IC Roller Operator. The person responsible for operating the IC 

roller and attached IC equipment. Sufficient training for the roller 

operator shall be supplied by a representative of the manufacturer 

of the equipment.

7. IC Equipment. The Roller supplier, make, roller model, number of IC 

rollers to be provided, and the GPS system supplier to be utilized. 

8. Embankment operations shall not begin before the QCP has been 

accepted.

9. The Engineer may require the replacement of ineffective or unqualified 

equipment or Quality Control personnel. Construction operations may be 

required to stop until Quality Control corrective actions are taken. 

References. (to be modified/expanded as applicable by the DOT)

1. AASHTO Standards.

AASHTO T 99 Moisture-Density Relations of Soils Using a 

2.5-kg (5.5-lb) Rammer and a 305-mm (12-

in.) Drop

AASHTO T 272 Family of Curves – One-Point Method

2. ASTM Standards.

ASTM D 2583 Measuring Deflections with a Light Weight 

Deflectometer (LWD)

ASTM D 6951 Dynamic Cone Penetrometer in Shallow 

Pavement Applications (17.6-lb (8-kg) 

hammer)

3. xxDOT Standards.

xxx Field Determination of Moisture Content of 

Soils

xxx Field Determination of Deflection Using 

Light Weight Deflectometer
236

Quality Control Technician. The QCT shall be responsible for the following 

minimum functions:

1. GPS check testing for the IC roller(s) and rover(s).

2. Test section construction and establishing target values for the maximum 

dry density, optimum moisture content, production moisture content, 

strength of the materials using the dynamic cone penetrometer (DCP), 

light weight deflectometer (LWD), nuclear gauge, and the IC-roller(s).

3. Monitoring of the construction operations and the IC roller(s) during 

production and final proofing operations.

4. Quality control testing for the maximum dry density and moisture content.

5. Downloading and analysis of the IC-data from the roller(s).

6. Daily set-up, take down and secure storage of GPS and IC roller 

components

Testing Facility. The location of the testing facility and a list of test equipment

shall be included. The testing facility shall be sufficient size to conduct the Quality 

Control tests, and a satisfactory base on which compaction of the soil can be achieved in 

accordance with AASHTO T 99 Method A (or as otherwise defined by the DOT) shall be 

provided. A statement of accessibility of the testing facility shall be included that allows 

xxDOT personnel to witness Quality Control activities and to review Quality Control 

tests. 

A list of the testing equipment proposed for Quality Control testing and the test 

methods and frequency of calibration or verification of the equipment shall be included. 

The Contractor shall maintain a record of all equipment calibration or verification results 

at the testing facility. The minimum frequency and procedures shall be as follows:

Equipment Requirement Minimum 

Frequency Procedure*

Balances Verification 12 months xxx

Sieves Check Physical 

Condition 12 months xxx

Etc.* * *

*to be filled in by the DOT

Materials Sampling and Testing. The procedures for sampling and testing of 

the soil embankment and the frequency of tests shall be identified and include as a 

minimum the following: (details to be modified/expanded as applicable by the DOT)
237

1. Moisture. The procedure for measuring the moisture content of the soil 

during production compaction. The minimum frequency of tests per lift of 

material shall be one test for each construction area.

2. Strength. The procedure for measuring the in-place strength of the soil. 

The minimum frequency of tests shall be a minimum of one test for each 

construction area.

3. Maximum Dry Density and Optimum Moisture Content. The procedure 

for measuring the maximum dry density and optimum moisture content of 

the soil for the test sections and when there is a change in the soil type.

4. IC Roller Data. The procedure for obtaining the IC roller data. The 

frequency of obtaining the data shall be a minimum of two times each day 

of soil compaction. The data is date/time stamped which permits for 

external evaluation at a later time.

GPS Check Testing. Prior to the start of production, the Contractor, GPS 

representative and IC roller manufacturer shall conduct the following to check the proper 

setup of the GPS, IC roller(s) and the rover(s) using the same datum:

1. On a location nearby or within the project limits, the GPS base station 

shall be established and the IC roller and the GPS rover tied into the base 

station.

2. Verification that the roller and rover are working properly and that there is

a connection with the base station.

3. The coordinates of the roller from the on-board, color-coded display shall 

be recorded.

4. The receiver from the rover shall be removed and placed on top of the 

roller receiver and the coordinates shown on the rover display recorded.

5. The roller and rover coordinates shall be compared. If the coordinates 

calculate as being within 1.6 in. (40 mm), the comparison is acceptable. If 

the coordinates are not within 1.6 in. (40 mm), corrections shall be made 

as needed and the above steps repeated until verification is acceptable. 

Work shall not begin until proper verification has been obtained.

6. The project plan file provided by xxDOT shall be uploaded into the IC 

Data analysis software and depending on the roller manufacture, the onboard IC computer.

7. GPS check testing shall be conducted daily during production operations.
238

Test Sections. Test section evaluations are intended to determine the number of 

passes it takes to achieve compaction at the optimum moisture content for the materials. 

Test sections shall be approximately 225 ft (75 m) long and 24 ft (8 m) wide. The IC 

roller shall be utilized on the test sections to establish the target IC-MV as correlated to 

the xxDOT standard testing devices. GPS measurements for all testing devices will be 

obtained with the rover for correlation to the IC-MV. (test section details to be

modified/expanded as applicable by the DOT)

The evaluations shall be conducted for the various material types, on every lift 

where there is a change of materials. The rollers shall use the same settings (speed, 

frequency) throughout the section while minimizing overlapping of the roller. After each 

roller pass, a nondestructive density device shall be used to estimate the density or 

stiffness of the material at 10 locations uniformly spaced throughout the test section 

within the width of a single roller pass. The readings and the number of roller passes will 

be recorded. The estimated target density will be the peak of the average of the 

nondestructive readings. Linear regression relationships between the in-place density 

data and the number of passes will be used to determine if process meets the xxDOT inplace compaction requirements.

The target IC-MV is the point when the increase in the IC-MV of the material 

between passes is less than 5 percent on the compaction curve. The IC compaction curve 

is defined as the relationship between the IC-MV and the roller passes. A compaction 

curve example is as follows:

Optimal pass

Target ICMV

∆ICMV<5%

Once the target IC-MV is determined, compact an adjoining section using same 

roller settings and the estimated roller passes to verify the compaction with the same 

nondestructive devices after the final roller pass. Straight line best fit linear regression 

relationships between the density testing and IC-MV data will be used to establish the 

production target IC-MV that meets the xxDOT in-place compaction requirements. A 

linear regression curve example is as follows.
239

ICMV

Target ICMV

Target % Gmm

% Gmm

Mapping. Pre-construction mapping/proofing of the initial layer of the fill is 

recommended to identify weak areas that may need to be addressed in advance of the 

production fill operations. Subsequent mapping may be conducted at anytime to 

recognize the changes in the fill that affects the target IC-MV or the density verification 

testing. At a minimum, production mapping is recommended at the final surface of the 

fill and the elevation levels at 1.0 ft., 2.0 ft, 4.0 ft, and 8.0 ft below the final surface as 

applicable. The stiffness of the underlying materials should increase with subsequent lifts 

of soils. The Contractors procedures for mapping shall be included.

Soil Management. The procedures for management of the borrow pit and soil 

cut sections to assure uniform soil material shall be included. The procedures for the 

necessary adjustments in compaction because of a change in soil type shall be stated.

Response to Test Results. The response to quality control tests for the test 

sections and during production compaction shall include as a minimum the following:

1. Moisture. The procedure for corrective action when the QC moisture tests 

are not within -3 and +2 percentage points of the optimum moisture 

content.

2. Strength. The procedure for corrective action when tests do not meet the 

xxDOT requirements for each soil type.

3. Maximum Dry Density and Optimum Moisture Content. The procedure 

for corrective action when the maximum dry density and optimum 

moisture content test results indicate that there is a change in the soil type.

4. IC Coverage Area and Uniformity Criteria. The procedures for reworking the construction area when IC criteria for coverage area or the 

minimum IC-MV are not met.

Documentation. The documentation shall include the following:

1. Quality Control Tests. The results from the moisture, strength, and 

maximum dry density and optimum moisture content tests. All quality 
240

control test results shall be signed by the QCT and submitted to the 

Engineer within 24 h of testing. 

2. Equipment. Documentation of the manufacture, model, and type of rollers 

used each day of soil compaction and the IC roller used for mapping the 

compaction of the soil. The positioning of the IC roller(s) in the paving 

operations shall be noted.

3. IC-MV Analysis. The Contractors will analyze the IC-MV data for 

conformance to the requirements for coverage area and uniformity and 

will submit the results to the Engineer at the completion of the individual 

IC Construction Area operations.

IC data will be saved as Time History Data and Post-Processed Data. 

Post-Processed Data will be imported using the all-passes and proofingdata formats. All passes data includes the data from all of the passes and 

proofing data is the data from just the last pass within a given area.

IC CONSTRUCTION

Technical Assistance. The Contractor shall coordinate for on-site technical 

assistance from the IC roller representative during the initial seven (7) days of production 

and then as needed during the remaining operations. As a minimum, the roller 

representative shall be present during the initial setup and verification testing of the IC 

roller(s). The roller representative shall also assist the Contractor with data management 

using the data analysis software including IC data input and processing.

Construction Areas. IC Construction areas are defined as subsections of the 

project being worked continuously by the Contractor. The magnitude of the evaluation 

areas may vary with production but they need to be at least 25,000 ft2 for evaluation. 

Partial construction areas of 5000 ft2 or less will be included in the previous area 

evaluation. Partial construction areas of greater than 5000 ft2 will constitute a full area to 

close out the mixture. Construction areas may extend over multiple days depending on 

the operations.

IC Construction Operations Criteria. A minimum coverage of 90% of the

individual construction area shall meet the optimal number of roller passes and 70% of 

the target IC-MV determined from the test sections. Construction areas not meeting the 

IC criteria shall be reworked and re-evaluated prior to continuing with the operations in 

that area. The IC Construction Operations Criteria does not affect the standard xxDOT

acceptance processes for the materials or construction operations. 

METHOD OF MEASUREMENT 
241

This item will not be measured as it will be paid as a lump sum for providing for 

the Intelligent Compaction for Soils on the project.

BASIS OF PAYMENT 

The incorporating of the Intelligent Compaction process will be paid at the 

contract lump sum price for Intelligent Compaction for Soils. 

Payment will be made under:

Pay Item Unit

 Intelligent Compaction for Soils........................................................ LS

This item includes all costs related to providing the IC roller including the fuel, 

roller operator, GPS system, or any other equipment required for the IC process. All 

quality control procedures including IC rollers and GPS systems representatives support 

and testing facility shall be included in the contract lump sum price.
242

Appendix E. Generic HMA IC Specification

The following is a generic HMA IC specification that can be served as a national 

guideline for States to implement IC.

Intelligent Compaction Technology for HMA Applications

DESCRIPTION

This work shall consist of the construction of the Hot Mix Asphalt (HMA)

utilizing Intelligent Compaction (IC) rollers within the limits of the work as described in 

the plans. IC is defined as a process that uses vibratory rollers equipped with a 

measurement/documentation system that automatically records various critical 

compaction parameters correlated to agency standard testing protocols in real time during 

the compaction process. IC uses roller vibration measurements to assess the mechanistic 

properties to ensure optimum compaction is achieved through continuous monitoring of 

the operations. Additional information on the IC technology may be found on the 

website http://www.intelligentcompaction.com/index.php?q=node/4 and from the 

Transportation Research Board - NCHRP Report 676 on Intelligent Soil Compaction 

Systems. 

The Contractor shall supply sufficient numbers of rollers and other associated 

equipment necessary to complete the compaction requirements for the specific materials. 

The Contractor will determine the number of IC rollers to use depending on the scope of 

the project. The best location in the paving operations for the IC roller is in the 

breakdown position and can be used with non-IC rollers.

EQUIPMENT

The IC rollers shall meet the following specific requirements:

5. IC rollers shall be self propelled double-drum vibratory rollers equipped with 

accelerometers mounted in or about the drum to measure the interactions between the 

rollers and compacted materials in order to evaluate the applied compaction effort. IC 

rollers shall also be equipped with non-contact temperature sensors for measuring 

pavement surface temperatures.

6. The output from the roller is designated as the Intelligent Compaction 

Measurement Value (IC-MV) which represents the stiffness of the materials based 

on the vibration of the roller drums and the resulting response from the underlying 

materials.
243

7. The IC rollers shall include an integrated on-board documentation system that is capable 

of displaying real-time color-coded maps of IC measurement values including the 

stiffness response values, location of the roller, number of roller passes, machine settings, 

together with the temperature, speed and the frequency and amplitude of roller drums. 

The display unit shall be capable of transferring the data by means of a USB port.

8. Roller mounted GPS radio and receiver units shall be mounted on each IC roller. RTKGPS radio and receivers are required to monitor the location and track the number of 

passes of the rollers.

Real Time Kinematic Global Positioning System (RTK-GPS)

The Universal Transverse Mercator (UTM) Coordinates system divides the surface of 

Earth between 80°S and 84°N latitude into 60 zones, each 6° of longitude in width and centered 

over a meridian of longitude. Zone 1 is bounded by longitude 180° to 174° W and is centered on 

the 177th West meridian. Zones outside of the Continental United States can be acquired on the 

web at http://www.dmap.co.uk/utmworld.htm. The UTM for this project is Zone (xx) N. (DOT 

to fill in the zone number) 

Base Station - Ground mounted or virtual GPS base units that record values in northing, 

easting, and the elevation data in meters using the UTM coordinate system along with the 

longitude/latitude of the measurement values shall be provided. The GPS base station shall 

broadcast updated correction data to the GPS receivers on the IC rollers and the hand-held rovers 

during operations with a survey tolerance of not greater than 1.6 in. (40 mm) in both the 

horizontal (x and y) directions.

Rover - A portable hand-held GPS radio/receiver for in-situ point measurements shall be 

provided.

Data Analysis Software - Standardized data analysis software (Veda Alfa Vr.8.0 

or later) is available on the website www.intelligentcompaction.com or will be provided 
244

by xxDOT. The software program will utilize the IC-MV data from the IC roller for 

analysis of coverage, uniformity, and stiffness values during construction operations. As 

a minimum, the following Essential IC Data Information and IC Data Elements shall be 

available in either ASCII or text format for post processing.

• Essential IC Data Information:

Item 

No.

Description

1 Section Title

2 Machine Manufacture

3 Machine Type

4 Machine Model

5 Drum Width (m)

6 Drum Diameter (m)

7 Machine Weight (metric ton)

8 Name index of intelligent compaction measurement values (ICMV)

9 Unit index for IC-MV

10 Reporting resolution for independent IC-MVs – 90 degrees to 

the roller moving direction (mm)

11 Reporting resolution for independent IC-MVs – in the roller 

moving direction (mm)

12 UTM Zone

13 Offset to UTC (hrs)

14 Number of IC data points

• Essential IC Data Elements:

Item 

No.

Date Field Name Example of Data

1 Date Stamp (YYYYMMDD) e.g. 20080701

2 Time Stamp (HHMMSS.S -military 

format)

e.g. 090504.0 (9 hr 5 

min. 4.0 s.)

3 Longitude (decimal degrees) e.g. 94.85920403

4 Latitude (decimal degrees) e.g. 45.22777335

5 Easting (m) e.g. 354048.3

6 Northing (m) e.g. 5009934.9

7 Height (m) e.g. 339.9450

8 Roller pass number e.g. 2

9 Direction index e.g., 1 forward, 2 reverse

10 Roller speed (kph) e.g. 4.0

11 Vibration on e.g., 1 for yes, 2 for no

12 Frequency (vpm) e.g. 3500.0

13 Amplitude (mm) e.g. 0.6
245

14 Surface temperature (

o

C) - HMA e.g. 120

15 Intelligent compaction measurement 

values

e.g. 20.0

QUALITY CONTROL PLAN 

The Contractor shall prepare and submit a written Quality Control Plan (QCP) for 

the project. As a minimum, the QCP shall contain the following information: 

General Requirements.

10. QCP shall be contract specific, stating how the contractor proposes to 

control the materials, equipment, and construction operations including 

subcontractors and suppliers as well as production facilities and 

transportation modes to the project for the HMA pavement operations.

11. The QCP shall include an organizational chart showing all quality control 

personnel and how these personnel integrate with other 

management/production and construction functions and personnel.

12. The QCP shall be signed and dated by the Contractor’s representative at 

the time the QCP is submitted to the Engineer. The QCP shall be 

submitted no later than 15 days prior to commencing the paving 

operations.

13. The xxDOT will review, sign, and date the QCP if the contents of the QCP 

are in compliance with the requirements as stated herein.

14. The QCP shall be maintained to reflect the current status of the operations, 

and revisions shall be provided in writing prior to initiating the change. 

The QCP revision shall not be implemented until the revision has been 

accepted. 

15. The QCP shall contain the name, telephone number, duties, and employer 

of all quality control personnel necessary to implement the QCP. The 

minimum qualifications of quality control personnel shall be as follows:

d. QCP Field Manager or Plan Administrator. The person 

responsible for the execution of the QCP and liaison with the 

Engineer. Additionally the QCP Field Manager requirements 

include:
246

4. Full-time employee of the Contractor or an independent 

consultant not involved with the Quality Assurance 

(acceptance) activities on the project.

5. Minimum (x) years experience (as determined by the DOT) in 

quality control activities in construction operations 

6. Full authority to institute actions necessary for successful 

implementation of the QCP. 

e. Quality Control Technician (QCT). The person(s) responsible for 

conducting quality control and inspection activities to implement 

the QCP. There may be more than one QCT on a project.

1. Full-time employee of the Contractor or an independent 

consultant with a minimum (x) years experience (as 

determined by the DOT) in quality control activities in 

construction operations. 

2. Completed the xxDOT requirements for the applicable testing.

3. Full authority to institute actions necessary for successful 

implementation of the QCP. 

f. IC Roller Operator. The person responsible for operating the IC 

roller and attached IC equipment. Sufficient training for the roller 

operator shall be supplied by a representative of the manufacturer 

of the equipment.

16. IC Equipment. The Roller supplier, make, roller model, number of IC 

rollers to be provided, and the GPS system supplier to be utilized. 

17. HMA pavement operations shall not begin before the QCP has been 

accepted.

18. The Engineer may require the replacement of ineffective or unqualified 

equipment or Quality Control personnel. Construction operations may be 

required to stop until Quality Control corrective actions are taken. 

References. (to be modified/expanded as applicable by the DOT)

4. AASHTO Standards.

AASHTO R 42 Standard Practice for Developing a Quality 

Assurance Plan for Hot Mix Asphalt (HMA)
247

5. ASTM Standards.

xxx xxx

6. xxDOT Standards.

xxx xxx

Quality Control Technician. The QCT shall be responsible for the following 

minimum functions:

1. GPS check testing for the IC roller(s) and rover(s). 

2. Test section construction to establish target compaction pass counts and 

target values for the strength of the materials using the standard testing 

devices, e.g. Nondestructive density gauges, pavement cores, and IC 

roller(s).

3. Monitoring of the construction operations and the IC roller(s) during 

production and final evaluation operations.

4. Quality control testing to monitor the pavement temperature and the 

required level of compaction.

5. Downloading and analysis of the IC data from the roller(s).

6. Daily set-up, take down and secure storage of GPS and IC roller 

components

Testing Facility. The location of the testing facility and a list of test equipment

shall be included. The testing facility shall be sufficient size to conduct the Quality 

Control tests shall be provided. A statement of accessibility of the testing facility shall be 

included that allows xxDOT personnel to witness Quality Control activities and to review 

Quality Control tests. 

A list of the testing equipment proposed for Quality Control testing and the test 

methods and frequency of calibration or verification of the equipment shall be included. 

The Contractor shall maintain a record of all equipment calibration or verification results 

at the testing facility. The minimum frequency and procedures shall be as follows:

Equipment Requirement Minimum 

Frequency Procedure

* * * *

*to be filled in by the DOT
248

Materials Sampling and Testing. The procedures for sampling and testing of 

the pavement shall be identified and include as a minimum the following: (details to be

modified/expanded as applicable by the DOT)

5. Temperature. The procedure for monitoring the temperature of the 

materials during production, transportation, laydown and compaction 

operations. A minimum frequency shall be one test for two hours of 

placement and shall include all steps in the process.

6. Density/Compaction. Identification of the standard testing device(s) and 

frequency for measuring the in-place density of the HMA. The minimum 

frequency of tests shall be one test for each 250 tons of HMA placed.

7. IC Roller Data. The procedure for obtaining the IC roller data. The 

frequency of obtaining the data from the roller shall be at a minimum of 

two times per day of HMA compaction operations. The data is date/time 

stamped which permits for external evaluation at a later time.

GPS Check Testing. Prior to the start of production, the Contractor, GPS 

representative and IC roller manufacturer shall conduct the following to check the proper 

setup of the GPS, IC roller(s) and the rover(s) using the same datum:

8. On a location nearby or within the project limits, the GPS base station 

shall be established and the IC roller and the GPS rover tied into the base 

station.

9. Verification that the roller and rover are working properly and that there is

a connection with the base station.

10. The coordinates of the roller from the on-board, color-coded display shall 

be recorded.

11. The receiver from the rover shall be removed and placed on top of the 

roller receiver and the coordinates shown on the rover display recorded.

12. The roller and rover coordinates shall be compared. If the coordinates 

calculate as being within 1.6 in. (40 mm), the comparison is acceptable. If 

the coordinates are not within 1.6 in. (40 mm), corrections shall be made 

as needed and the above steps repeated until verification is acceptable. 

Work shall not begin until proper verification has been obtained.

13. The project plan file provided by xxDOT shall be uploaded into the IC 

Data analysis software and depending on the roller manufacture, the onboard IC computer.
249

14. GPS check testing shall be conducted daily during production operations.

Test Sections. Test section evaluations are intended to verify the mixture 

volumetric of mixtures and determine a compaction curve of the HMA mixtures in 

relationship to number of roller passes and to the stiffness of mixture while meeting the 

xxDOT in-place compaction requirements. (test section details to be modified/expanded 

as applicable by the DOT)

The evaluations shall be conducted every lift and be approximately 300 tons of 

mainline mixtures. The IC rollers shall use low amplitude and the same settings (speed, 

frequency) throughout the section while minimizing overlapping of the roller. After each 

roller pass, a nondestructive density device shall be used to estimate the density of the 

HMA at 10 locations uniformly spaced throughout the test section within the width of a 

single roller pass. The density readings and the number of roller passes that takes to 

achieve the desired compaction will be recorded. The estimated target density will be the 

peak of the average of the nondestructive readings within the desired compaction 

temperature range for the mixture. The IC roller data in conjunction with the IC data 

analysis software will create an IC compaction curve for the mixture. The target IC-MV 

is the point when the increase in the IC-MV of the material between passes is less than 5 

percent on the compaction curve. The IC compaction curve is defined as the relationship 

between the IC-MV and the roller passes. A compaction curve example is as follows:

Optimal pass

Target ICMV

∆ICMV<5%

Once the target IC-MV is determined, compact an adjoining 300 tons using same 

roller settings and the number of estimated roller passes and verify the compaction with 

the same nondestructive devices after the final roller pass. At 10 locations, cores should 

be taken uniformly spaced throughout the test section within the width of the single roller.

GPS measurement of the core locations will be obtained with a GPS rover. Straight line 

best fit linear regression relationships between the core data and IC-MV will be used to 

establish the production target IC-MV as the target density (% Gmm) meets the xxDOT inplace compaction requirements. A linear regression curve example is as follows.
250

ICMV

Target ICMV

Target % Gmm

% Gmm

Mapping. Pre-paving mapping with an IC roller of the underlying materials is 

recommended to be completed prior to tacking operations is recommended to identify 

weak areas and may be part of the test section evaluations on the project or independently 

run. Pre-construction mapping should be approximately 500 ft in length and conducted 

on mainline paving sections. Underlying materials includes treated or non-treated 

subgrades, treated or non-treated aggregate bases, or on milled or non-milled asphalt 

pavements. Mapping operations are intended to provide the Contractor and 

understanding of the stiffness of the existing roadway being paved. Subsequent mapping

may be conducted at anytime to understand the changes in the roadway that affects the 

target IC-MV or the density verification testing. The stiffness of the underlying materials

should increase with subsequent lifts of HMA. The Contractors procedures for mapping 

shall be included.

Response to Test Results. The response to quality control tests for the test 

sections and during production compaction shall include as a minimum the following:

5. Temperature. The procedure for corrective action when the QC or IC 

temperature readings are not within the recommended laydown values for 

the mixtures.

6. Density/Compaction. The procedure for corrective action when the 

maximum specific density (Gmm) results fall below the xxDOT

specification limits or 92.0% whichever is greater.

7. IC Coverage Area and Uniformity Criteria. The procedures to be taken 

when the coverage area for pass counts requirements or IC-MV targets are 

not being met.

Documentation. The documentation shall include the following:

4. Quality Control Tests. The results from the temperature and density 

testing. All quality control test results shall be signed by the QCT and 

submitted to the Engineer within 24 h of testing. 
251

5. Equipment. Documentation of the manufacture, model, type of paver, and 

rollers used each day of HMA operations. The positioning of the IC 

roller(s) in the paving operations shall be noted.

6. IC Roller Data. At a minimum, the electronic data from IC roller(s) shall

be provided to the Engineer upon the completion of the Test Section, 

Mapping and individual IC Construction Area operations.

7. IC-MV Analysis. The Contractors will analyze the IC-MV data for 

conformance to the requirements for coverage area and uniformity and 

will submit the results to the Engineer at the completion of the individual 

IC Construction Area operations.

IC data will be saved as Time History Data and Post-Processed Data. 

Post-Processed Data will be imported using the all-passes and proofingdata formats. All passes data includes the data from all of the passes and 

proofing data is the data from just the last pass within a given area.

IC CONSTRUCTION

Technical Assistance. The Contractor shall coordinate for on-site technical 

assistance from the IC roller representative during the initial seven (7) days of production 

and then as needed during the remaining operations. As a minimum, the roller 

representative shall be present during the initial setup and verification testing of the IC 

roller(s). The roller representative shall also assist the Contractor with data management 

using the data analysis software including IC data input and processing.

IC Construction Area. IC Construction areas are defined as subsections of the 

project being worked continuously by the Contractor. The magnitude of the evaluation 

areas may vary with production but they need to be at least 1000 tons per mixture for 

evaluation. Partial construction areas of 500 tons or less will be included in the previous 

area evaluation. Partial construction areas of greater than 500 tons will constitute a full 

area to close out the mixture. Construction areas may extend over multiple days 

depending on the operations.

IC Construction Operations Criteria. A minimum coverage of 90% of the

individual construction area shall meet the optimal number of roller passes and 70% of 

the target IC-MV values determined from the test sections. Construction areas not 

meeting the IC criteria shall be investigated by the xxDOT prior to continuing with the 

paving operations. The IC Construction Operations Criteria does not affect the standard 

xxDOT acceptance processes for the materials or construction operations. 

METHOD OF MEASUREMENT 

This item will not be measured as it will be paid as a lump sum for providing for 

the Intelligent Compaction for HMA on the project.
252

BASIS OF PAYMENT 

The incorporating of the Intelligent Compaction process will be paid at the 

contract lump sum price for Intelligent Compaction for HMA. 

Payment will be made under:

Pay Item Unit

 Intelligent Compaction for HMA LS

This item includes all costs related to providing the IC roller including the fuel, 

roller operator, GPS system, or any other equipment required for the IC process. All 

quality control procedures including IC rollers and GPS systems representatives support 

and testing facility shall be included in the contract lump sum price.
253

Appendix F. Generic Subbase IC Specification

The following is a generic subbase IC specification that can be served as a national 

guideline for States to implement IC.

Intelligent Compaction Technology for Aggregate Base Applications

DESCRIPTION

This work shall consist of the construction of the aggregate base materials

utilizing Intelligent Compaction (IC) rollers within the limits of the work as described in 

the plans. IC is defined as a process that uses vibratory rollers equipped with a 

measurement/documentation system that automatically records various critical 

compaction parameters correlated to agency standard testing protocols in real time during 

the compaction process. IC uses roller vibration measurements to assess the mechanistic 

properties and to ensure optimum compaction is achieved through continuous monitoring 

of the operations. Additional information on the IC technology may be found on the 

website www.intelligentcompaction.com and from the Transportation Research Board -

NCHRP Report 676 on Intelligent Soil Compaction Systems. 

The Contractor shall supply sufficient numbers of rollers and other associated 

equipment necessary to complete the spreading and compaction requirements for the 

aggregate materials. The Contractor will determine the number of IC rollers to use 

depending on the scope of the project. The IC roller(s) may be utilized during production 

with other standard compaction equipment and shall be used for the evaluation of the 

compaction operations. 

EQUIPMENT

The IC rollers shall meet the following specific requirements:

9. IC rollers shall be self propelled single-drum vibratory rollers equipped with 

accelerometers mounted in or about the drum to measure the interactions between the 

rollers and compacted materials in order to evaluate the applied compaction effort. 

Rollers shall have smooth drums.

10. The output from the roller is designated as the Intelligent Compaction 

Measurement Value (IC-MV) which represents the stiffness of the materials based 

on the vibration of the roller drums and the resulting response from the underlying 

materials.

11. The IC rollers shall include an integrated on-board documentation system that is capable 

of displaying real-time color-coded maps of IC measurement values including the 

stiffness response values, location of the roller, number of roller passes, machine settings, 
254

together with the speed, frequency and amplitude of roller drums. The display unit shall 

be capable of transferring the data by means of a USB port.

12. Roller mounted GPS radio and receiver units shall be mounted on each IC roller. RTKGPS radio and receivers are required to monitor the location and track the number of 

passes of the rollers.

Real Time Kinematic Global Positioning System (RTK-GPS)

The Universal Transverse Mercator (UTM) Coordinates system divides the surface of

Earth between 80°S and 84°N latitude into 60 zones, each 6° of longitude in width and centered 

over a meridian of longitude. Zone 1 is bounded by longitude 180° to 174° W and is centered on 

the 177th West meridian. Zones outside of the Continental United States can be acquired on the 

web at http://www.dmap.co.uk/utmworld.htm. The UTM for this project is Zone (xx) N. (DOT 

to fill in the zone number) 

Base Station - Ground mounted or virtual GPS base units that record values in northing, 

easting, and the elevation data in meters using the UTM coordinate system along with the 

longitude/latitude of the measurement values shall be provided. The GPS base station shall 

broadcast updated correction data to the GPS receivers on the IC rollers and the hand-held rovers 

during operations with a survey tolerance of not greater than 1.6 in. (40 mm) in both the 

horizontal (x and y) directions. 

Rover - A portable hand-held GPS radio/receiver for in-situ point measurements shall be 

provided.

Data Analysis Software - Standardized data analysis software (Veda Alfa Vr.8.0 

or later) is available on the website http://www.intelligentcompaction.com/ or will be 

provided by xxDOT. The software program will utilize the IC-MV data from the IC roller 

for analysis of coverage, uniformity, and stiffness values during construction operations. 

As a minimum, the following Essential IC Data Information and IC Data Elements shall 

be available in either ASCII or text format for post processing.
255

• Essential IC Data Information:

Item 

No.

Description

1 Section Title

2 Machine Manufacture

3 Machine Type

4 Machine Model

5 Drum Width (m)

6 Drum Diameter (m)

7 Machine Weight (metric ton)

8 Name index of intelligent compaction measurement values (ICMV)

9 Unit index for IC-MV

10 Reporting resolution for independent IC-MVs – 90 degrees to 

the roller moving direction (mm)

11 Reporting resolution for independent IC-MVs – in the roller 

moving direction (mm)

12 UTM Zone

13 Offset to UTC (hrs)

14 Number of IC data points

• Essential IC Data Elements:

Item 

No.

Date Field Name Example of Data

1 Date Stamp (YYYYMMDD) e.g. 20080701

2 Time Stamp (HHMMSS.S -military 

format)

e.g. 090504.0 (9 hr 5 

min. 4.0 s.)

3 Longitude (decimal degrees) e.g. 94.85920403

4 Latitude (decimal degrees) e.g. 45.22777335

5 Easting (m) e.g. 354048.3

6 Northing (m) e.g. 5009934.9

7 Height (m) e.g. 339.9450

8 Roller pass number e.g. 2

9 Direction index e.g., 1 forward, 2 reverse

10 Roller speed (kph) e.g. 4.0

11 Vibration on e.g., 1 for yes, 2 for no

12 Frequency (vpm) e.g. 3500.0

13 Amplitude (mm) e.g. 0.6

14 Surface temperature (oC) - HMA e.g. 120

15 Intelligent compaction measurement 

values

e.g. 20.0
256

QUALITY CONTROL PLAN 

The Contractor shall prepare and submit a written Quality Control Plan (QCP) for 

the project. As a minimum, the QCP shall contain the following information: 

General Requirements.

19. QCP shall be contract specific, stating how the contractor proposes to 

control the materials, equipment, and construction operations including 

subcontractors and suppliers as well as production facilities and 

transportation modes to the project for the embankment operations.

20. The QCP shall include an organizational chart showing all quality control 

personnel and how these personnel integrate with other 

management/production and construction functions and personnel.

21. The QCP shall be signed and dated by the Contractor’s representative at 

the time the QCP is submitted to the Engineer. The QCP shall be 

submitted no later than 15 days prior to commencing the embankment 

operations.

22. The xxDOT will review, sign, and date the QCP if the contents of the QCP 

are in compliance with the requirements as stated herein.

23. The QCP shall be maintained to reflect the current status of the operations, 

and revisions shall be provided in writing prior to initiating the change. 

The QCP revision shall not be implemented until the revision has been 

accepted. 

24. The QCP shall contain the name, telephone number, duties, and employer 

of all quality control personnel necessary to implement the QCP. The 

minimum qualifications of quality control personnel shall be as follows:

g. QCP Field Manager or Plan Administrator. The person 

responsible for the execution of the QCP and liaison with the 

Engineer. Additionally the QCP Field Manager requirements 

include:

7. Full-time employee of the Contractor or an independent 

consultant not involved with the Quality Assurance 

(acceptance) activities on the project.

8. Minimum (x) years experience (as determined by the DOT) in 

quality control activities in construction operations 
257

9. Full authority to institute actions necessary for successful 

implementation of the QCP. 

h. Quality Control Technician (QCT). The person(s) responsible for 

conducting quality control and inspection activities to implement 

the QCP. There may be more than one QCT on a project.

4. Full-time employee of the Contractor or an independent 

consultant with a minimum (x) years experience (as 

determined by the DOT) in quality control activities in 

construction operations. 

5. Completed the xxDOT requirements for the applicable testing.

6. Full authority to institute actions necessary for successful 

implementation of the QCP.

i. IC Roller Operator. The person responsible for operating the IC 

roller and attached IC equipment. Sufficient training for the roller 

operator shall be supplied by a representative of the manufacturer 

of the equipment.

25. IC Equipment. The Roller supplier, make, roller model, number of IC 

rollers to be provided, and the GPS system supplier to be utilized. 

26. Material placement and compaction operations shall not begin before the 

QCP has been accepted.

27. The Engineer may require the replacement of ineffective or unqualified 

equipment or Quality Control personnel. Construction operations may be 

required to stop until Quality Control corrective actions are taken. 

References. (to be modified/expanded as applicable by the DOT)

7. AASHTO Standards.

AASHTO T 99 Moisture-Density Relations of Soils Using a 

2.5-kg (5.5-lb) Rammer and a 305-mm (12-

in.) Drop

AASHTO T 272 Family of Curves – One-Point Method

8. ASTM Standards.

9. xxDOT Standards.
258

Quality Control Technician. The QCT shall be responsible for the following 

minimum functions:

7. GPS check testing for the IC roller(s) and rover(s).

8. Test section construction and establishing target values for the maximum 

dry density, optimum moisture content, production moisture content, 

strength of the materials using the light weight deflectometer (LWD), 

nuclear gauge, and the IC-roller(s).

9. Monitoring of the construction operations and the IC roller(s) during 

production and final proofing operations.

10. Quality control testing for the maximum dry density and moisture content.

11. Downloading and analysis of the IC-data from the roller(s).

12. Daily set-up, take down and secure storage of GPS and IC roller 

components

Testing Facility. The location of the testing facility and a list of test equipment

shall be included. The testing facility shall be sufficient size to conduct the Quality 

Control tests, and a satisfactory base on which compaction of the aggregates can be 

achieved in accordance with AASHTO T 99 Method A (or as otherwise defined by the 

DOT) shall be provided. A statement of accessibility of the testing facility shall be 

included that allows xxDOT personnel to witness Quality Control activities and to review 

Quality Control tests. 

A list of the testing equipment proposed for Quality Control testing and the test 

methods and frequency of calibration or verification of the equipment shall be included. 

The Contractor shall maintain a record of all equipment calibration or verification results 

at the testing facility. The minimum frequency and procedures shall be as follows:

Equipment Requirement Minimum 

Frequency Procedure*

Balances Verification 12 months xxx

Sieves Check Physical 

Condition 12 months xxx

Etc.* * *

*to be filled in by the DOT

Materials Sampling and Testing. The procedures for sampling and testing of 

the aggregates and the frequency of tests shall be identified and include as a minimum the 

following: (details to be modified/expanded as applicable by the DOT)
259

8. Moisture. The procedure for measuring the moisture content of the 

aggregates during production compaction. The minimum frequency of 

tests per lift of material shall be one test for each construction area.

9. Strength. The procedure for measuring the in-place strength of the 

aggregates. The minimum frequency of tests shall be a minimum of one 

test for each construction area.

10. Maximum Dry Density and Optimum Moisture Content. The procedure 

for measuring the maximum dry density and optimum moisture content of 

the aggregates in the test sections.

11. IC Roller Data. The procedure for obtaining the IC roller data. The 

frequency of obtaining the data shall be a minimum of two times each day 

of aggregate base production. The data is date/time stamped which permits 

for external evaluation at a later time.

GPS Check Testing. Prior to the start of production, the Contractor, GPS 

representative and IC roller manufacturer shall conduct the following to check the proper 

setup of the GPS, IC roller(s) and the rover(s) using the same datum:

15. On a location nearby or within the project limits, the GPS base station 

shall be established and the IC roller and the GPS rover tied into the base 

station.

16. Verification that the roller and rover are working properly and that there is

a connection with the base station.

17. The coordinates of the roller from the on-board, color-coded display shall 

be recorded.

18. The receiver from the rover shall be removed and placed on top of the 

roller receiver and the coordinates shown on the rover display recorded.

19. The roller and rover coordinates shall be compared. If the coordinates 

calculate as being within 1.6 in. (40 mm), the comparison is acceptable. If 

the coordinates are not within 1.6 in. (40 mm), corrections shall be made 

as needed and the above steps repeated until verification is acceptable. 

Work shall not begin until proper verification has been obtained.

20. The project plan file provided by xxDOT shall be uploaded into the IC 

Data analysis software and depending on the roller manufacture, the onboard IC computer.

21. GPS check testing shall be conducted daily during production operations.
260

Test Sections. Test section evaluations are intended to determine the number of 

passes it takes to achieve compaction at the optimum moisture content for the materials. 

Test sections shall be approximately 225 ft (75 m) long and 24 ft (8 m) wide. The IC 

roller shall be utilized on the test sections to establish the target IC-MV as correlated to 

the xxDOT standard testing devices. GPS measurements for all testing devices will be 

obtained with the rover for correlation to the IC-MV. (test section details to be

modified/expanded as applicable by the DOT)

The rollers shall use the same settings (speed, frequency) throughout the section

while minimizing overlapping of the roller. After each roller pass, a nondestructive 

density device shall be used to estimate the density or stiffness of the material at 10 

locations uniformly spaced throughout the test section within the width of a single roller 

pass. The readings and the number of roller passes will be recorded. The estimated target 

density will be the peak of the average of the nondestructive readings. Linear regression 

relationships between the in-place density data and the number of passes will be used to 

determine if process meets the xxDOT in-place compaction requirements.

The target IC-MV is the point when the increase in the IC-MV of the material 

between passes is less than 5 percent on the compaction curve. The IC compaction curve 

is defined as the relationship between the IC-MV and the roller passes. A compaction 

curve example is as follows:

Optimal pass

Target ICMV

∆ICMV<5%

Once the target IC-MV is determined, compact an adjoining section using same 

roller settings and the estimated roller passes to verify the compaction with the same 

nondestructive devices after the final roller pass. Straight line best fit linear regression 

relationships between the density testing and IC-MV data will be used to establish the 

production target IC-MV that meets the xxDOT in-place compaction requirements. A 

linear regression curve example is as follows.
261

ICMV

Target ICMV

Target % Gmm

% Gmm

Mapping. Mapping/proofing of the final layer of the subgrade is recommended 

to identify weak areas that may need to be addressed in advance of the aggregate 

placement and compaction operations. The stiffness of the underlying materials should 

increase with subsequent lifts of aggregate materials. The Contractors procedures for 

mapping shall be included.

Response to Test Results. The response to quality control tests for the test 

sections and during production compaction shall include as a minimum the following:

8. Moisture. The procedure for corrective action when the QC moisture tests 

are not within -3 and +2 percentage points of the optimum moisture 

content.

9. Strength. The procedure for corrective action when tests do not meet the 

xxDOT requirements for aggregate bases.

10. Maximum Dry Density and Optimum Moisture Content. The procedure 

for corrective action when the maximum dry density and optimum 

moisture content test results indicate that there is a change in the aggregate 

source.

11. IC Coverage Area and Uniformity Criteria. The procedures for reworking the construction area when IC criteria for coverage area or the 

minimum IC-MV are not met.

Documentation. The documentation shall include the following:

8. Quality Control Tests. The results from the moisture, strength, and 

maximum dry density and optimum moisture content tests. All quality 

control test results shall be signed by the QCT and submitted to the 

Engineer within 24 h of testing. 

9. Equipment. Documentation of the manufacture, model, and type of rollers 

used each day of aggregate compaction and the IC roller used for mapping 
262

the compaction of the aggregate. The positioning of the IC roller(s) in the 

operations shall be noted.

10. IC-MV Analysis. The Contractors will analyze the IC-MV data for 

conformance to the requirements for coverage area and uniformity and 

will submit the results to the Engineer at the completion of the individual 

IC Construction Area operations.

IC data will be saved as Time History Data and Post-Processed Data. 

Post-Processed Data will be imported using the all-passes and proofingdata formats. All passes data includes the data from all of the passes and 

proofing data is the data from just the last pass within a given area.

IC CONSTRUCTION

Technical Assistance. The Contractor shall coordinate for on-site technical 

assistance from the IC roller representative during the initial seven (7) days of production 

and then as needed during the remaining operations. As a minimum, the roller 

representative shall be present during the initial setup and verification testing of the IC 

roller(s). The roller representative shall also assist the Contractor with data management 

using the data analysis software including IC data input and processing.

Construction Areas. IC Construction areas are defined as subsections of the 

project being worked continuously by the Contractor. The magnitude of the evaluation 

areas may vary with production but they need to be at least 25,000 ft2 for evaluation. 

Partial construction areas of 5000 ft2 or less will be included in the previous area 

evaluation. Partial construction areas of greater than 5000 ft2 will constitute a full area to 

close out the material. Construction areas may extend over multiple days depending on 

the operations.

IC Construction Operations Criteria. A minimum coverage of 90% of the

individual construction area shall meet the optimal number of roller passes and 70% of 

the target IC-MV determined from the test sections. Construction areas not meeting the 

IC criteria shall be reworked and re-evaluated prior to continuing with the operations in 

that area. The IC Construction Operations Criteria does not affect the standard xxDOT

acceptance processes for the materials or construction operations. 

METHOD OF MEASUREMENT 

This item will not be measured as it will be paid as a lump sum for providing for 

the Intelligent Compaction for Aggregates on the project.

BASIS OF PAYMENT 
263

The incorporating of the Intelligent Compaction process will be paid at the 

contract lump sum price for Intelligent Compaction for Aggregates. 

Payment will be made under:

Pay Item Unit

 Intelligent Compaction for Aggregates LS

This item includes all costs related to providing the IC roller including the fuel, 

roller operator, GPS system, or any other equipment required for the IC process. All 

quality control procedures including IC rollers and GPS systems representatives support 

and testing facility shall be included in the contract lump sum price.