Advances in Intelligent
Compaction for HMA
Victor (Lee) Gallivan, PE
FHWA - Office of Pavement Technology
February 3, 2010
NCAUPG HMA Conference
Overland Park, Ks.
What is Intelligent Compaction
Technology
Office of Pavement Technology
Federal Highway Administration
www.fhwa.dot.gov/pavement/
An Innovation in Compaction
Control and Testing
----Definition----
What is “Intelligence?”
– Oxford Dictionary: “…able to vary behavior in response to varying situations and requirements”
– Ability to:Collect information
Analyze information
Make an appropriate decision
Execute the decision
3000-4000 TIMES A MINUTE
Shortcomings Density Acceptance…
Limited Number of Locations
Benefits of IC for HMA
Improve density….better performance
Improve efficiency….cost savings
Increase information…better QC/QA
GPS Base Station GPS Radio & Receiver GPS Rover
Real Time Kinematic (RTK) GPS Precision
NG
PSPA
NNG
LWD-a
Bomag America
Caterpillar
Ammann/Case Dynapac
Sakai America
Mapping of Roller Passes
Longitudinal Joint
Shoulder (Supported)Paving Direction
Courtesy Sakai America
Correlation w/ In-Situ Testing
Impact ForceFrom Rollers 300 mm
LWD/FWD200 mm
LWD
Nuclear Density Gauge
DynamicCone Penetrometer
2.1 m
2.1 m
0.3 m 0.2 m 0.3 m
1.0 m
X X X X X X X X2.1 m
Distance = Roller travel in 0.5 sec.
In-situ spot test measurements
Influence depths are assumed ~ 1 x B (width)
Area over witch the roller MV’s are averaged
Courtesy of Dr. David White
IC National Efforts
– NCHRP 21-09 “Examining the Benefits and
Adoptability of Intelligent Soil Compaction”
(Completed but not published yet)
– Transportation Pooled Fund #954 – “Accelerated
Implementation of Intelligent Compaction Technology
for Embankment Subgrade Soils, Aggregate Base and
Asphalt Pavement Material”
– The Transtec, Group, Austin, Texas
(George Chang- PI)
– Additional State IC Programs (OK, WI, etc.)
MD
MN
KS
TXMS
IN
NY
PA
VA
ND
GA
TX
WI
Objectives: Based on data obtained from
field studies:
– Accelerated development of QC/QA
specifications for granular and cohesive
subgrade soils, aggregate base and Hot
Mix Asphalt (HMA) pavement materials…
– Short, Long and Future Term Goals
– 3-year IC study for all the above materials
– 12 participating States
– 12+ field demonstration
Objectives
Develop an experienced and
knowledgeable IC expertise base within
Pool Fund participating State DOTs
Identify and prioritize needed
improvements to and/or research of IC
equipment and field QC/QA testing
equipment
Short Term Goals
Improved Density
More Uniform Density
More efficient compaction process
Operator Accountability
Correlate Measurements with Field Densities
Real-time Density Control (QC)
Long Term Goals
Continuous Compaction Control specifications
Real-time Density Acceptance (QA)
Future Goals
Tie to Design Guide (verify design)?
Performance specifications?
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
2008
2009
2010
Route 4, Kandiyohi County, MN
Mapping existing subbase
New HMA construction
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai double-drum
IC roller
HMA non-wearing course layer mapa = 0.6 mm,
f = 3000 vpm
Class 5 aggregate subbase layer map, a = 0.6 mm,
f = 2500 vpm
Reflection of hard spots onthe HMA layer
Reflection of hard spots onthe HMA layer
Reflection of
soft spots onthe HMA layer
HMA Map Subbase Map
CCVSubbase
(a = 0.6 mm, f = 2500)
0 5 10 15 20 25
CC
VS
ub
ba
se (
a =
0.3
mm
, f
= 3
00
0)
0
5
10
15
20
25
CCVSubbase
(a = 0.6 mm, f = 2500)
0 5 10 15 20 25
CC
VH
MA (
a =
0.6
mm
, f
= 3
00
0)
0
5
10
15
20
25
CCVSubbase
(a = 0.3 mm, f = 3000)
0 5 10 15 20 25
CC
VS
ub
ba
se (
a =
0.3
mm
, f
= 3
00
0)
0
5
10
15
20
25y = 2.45 ln(x) + 2.3
R2 = 0.69
y = 0.41x + 2.7
R2 = 0.33
y = 0.27x + 8.0
R2 = 0.30
Sakai double-drum IC roller
Subbase Mapping
HMA Map Subbase
Map
Premature
Failure
Peter’s Road, Springville, NY
Mapping existing subbase
New HMA construction
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai double-drum
IC roller
Subbase Mapping3000 vpm, 0.6mm, 5 tracks, 2mph
2500 vpm, 0.6mm, 3 tracks, 2mph
3000 vpm, 0.6mm, 4 tracks, 3 mph
Day 2 – First Lift of Base Course
Day 3 – 2nd Lift of Base Course
Day 3 – Intermediate Course
s
NG vs NNG 1st lift base
y = 0.0978x + 108.26
R2 = 0.1473
120
121
122
123
124
125
125
127
129
131
133
135
137
139
141
143
145
NG density (pcf)
NN
G d
en
sit
y (
pcf)
NG vs NNG
Linear (NG vs NNG)
Binder base
y = 0.118x + 107.54
R2 = 0.1552
120
121
122
123
124
125
126
127
128
129
130
120
125
130
135
140
145
NG density (pcf)
NN
G d
en
sit
y (
pcf)
NG vs NNG
Linear (NG vs NNG)
NG
NNG (PQI)
US 84, Wayne County, MS
Mapping existing stabilized base
New HMA Construction
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai double-drum
IC roller
Mapping
Results
TB 2A-2
N
TB 2B-1
TB 2C-1
TB 2A-1TB 2A-3
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)
CC
Vs
Mapping w/
Sakai double-drum
IC roller
Sakai double-drum
IC roller
0 50 100 150 200 250 300
Lag Distance
0
0.5
1
1.5
2
2.5
Va
rio
gra
m
Direction: 0.0 Tolerance: 90.0Column 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
Variogra
m
Direction: 0.0 Tolerance: 90.0Column 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
US 340EB, Frederick, MD
SMA overlay
Mapping milled HMA surface
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai
double-drum
IC roller
Bomag
double-drum
IC roller
Mapping
Milled HMA
Test bed 02 Mapping
Bomag Evib Sakai CCV
Bomag Sakai
US 340 EB
Mapping
Milled HMA
Sakai
double-drum
IC roller
TB 03A Mapping on Exiting HMA Pavement
0
10
20
30
40
50
60
70
80
90
0 50 100 150 200 250 300 350 400 450
Lag Distance
0
50
100
150
200
250
300
350
400
450
500
Va
rio
gra
m
Direction: 0.0 Tolerance: 90.0Column L: CCV
Exponential Model
Nugget = 300
Sill = 398
Range = 65
North
Sakai CCV Kridging Map
Semi-variogram for CCV
Lane 1
Shoulder
Bridge
TB 03B SMA overlay (distance 0 to 684 m)
140
160
180
200
220
240
260
280
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
0 50 100 150 200 250 300 350 400 450
Lag Distance
0
5
10
15
20
25
30
35
Va
riog
ram
Direction: 0.0 Tolerance: 90.0Column L: CCV
Nugget=16.5
Sill=28.5
Range=40
SAKAI CCV Surface Temperature
Semi-variogram - exponential model
PSPA
vs
FWD
200
300
400
500
600
700
800
900
200 300 400 500 600 700 800 900
Back-calculated modulus of existing HMA pavement (ksi)
PS
PA
se
ism
ic m
od
ulu
s o
f e
xis
tin
g H
MA
pa
ve
me
nt
(ks
i)
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
PS
PA
Seis
mic
mo
du
lus o
f exis
tin
g H
MA
layer
(ksi)
Modulus of Existing HMA Layer vs SMA Overlay CCV
Linear (Modulus of Existing HMA Layer vs SMA Overlay CCV)
PSPA
Vs
IC
Existing pavements
New SMA constrcution
IC RMV vs
NGy = 0.2858x + 149.28
R2 = 0.2031
140
145
150
155
160
165
0 5 10 15 20 25 30 35 40
SAKAI CCV
Den
sit
y
Density vs CCV
Linear (Density vs CCV)
Sakai
Double-drum
IC roller
NG
Park&Ride, Clayton County, GA
Mapping subbase
New HMA construction
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai
double-drum
IC roller
Mapping
GAB
Sakai
CCV
Sakai
Double-drum
IC roller
Park & Ride
TB 01A Intermediate HMA Layer
Sakai CCV
Surface
temperature (oC)
Roller pass
Sakai
Double-drum
IC roller
TB 01A
Sakai
Double-drum
IC roller
TB 05A
TB 05A Intermediate HMA Layer
Roller passes
Sakai CCV
Outer loop
Inner loop
Nor
th
NG
US 52, West Lafayette, IN
Mapping milled HMA surface
New HMA overlay
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
Sakai
Bomag
Before
After
TB 03 HMA intermediate layer
TB04
TB 03 HMA intermediate layer
TB04
Sakai
Double-drum
IC roller
TB 04
Future Initiatives:
– Regional Conferences – that target practitioners
– Establishment of Optimum Measurement Values
– Guidance Manual/Best Practices for both Soils and
Hot Mix Asphalt Materials
– Mini-IC Demo’s: Limited support for field trials with
Non-TPF States
– Web-Page Continuation
– 2010 Schedule
May Wisconsin HMA- Full
May/June Texas HMA-Mini
June Virginia HMA-Full
June/July North Dakota Soils-Full
June/July Pennsylvania HMA-Mini Soils-Full
June/July Indiana Soils-Full
June/July Tennessee HMA-Mini
July/Aug California HMA-Mini
August BIA HMA-Mini
www.intelligentcompaction.com
Benefits of IC
Improve density…better performance
Improve efficiency…cost savings
Increase information…better QC/QA
Ultimate Goals of TPF IC
Gain the knowledge needed to develop
credible and productive IC specifications
for future projects
Future IC Spec
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
<1%29 - 3470-80%
3%34 - 3880-90%
52%38 - 5590-130%
45%55>130%
IC DataCCV% Target
Window Variograms!!!
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
<1%29 - 3470-80%
3%34 - 3880-90%
52%38 - 5590-130%
45%55>130%
IC DataCCV% Target
Window Variograms!!!
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
4%29 - 3470-80%
6%34 - 3880-90%
59%38 - 5590-130%
31%55>130%
IC DataCCV% Target
Window Variograms!!!
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
4%29 - 3470-80%
6%34 - 3880-90%
59%38 - 5590-130%
31%55>130%
IC DataCCV% Target
Window Variograms!!!Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
0%< 29<70%
<1%29 - 3470-80%
4%34 - 3880-90%
79%38 - 5590-130%
17%55>130%
IC DataCCV% Target
Window Variograms!!!Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
0%< 29<70%
<1%29 - 3470-80%
4%34 - 3880-90%
79%38 - 5590-130%
17%55>130%
IC DataCCV% Target
Window Variograms!!!Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
1%29 - 3470-80%
4%34 - 3880-90%
83%38 - 5590-130%
10%55>130%
IC DataCCV% Target
Window Variograms!!!Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 70Range = 15Distance to Asymptotic "Sill" = 100
Station 275+00 to 277+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 73Range = 23Distance to Asymptotic "Sill" = 150
Station 273+00 to 275+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 43Range = 12Distance to Asymptotic "Sill" = 74
Station 273+00 to 271+00
Lag Distance (h)
0 50 100 150 200
Sem
i-Var
ianc
e g (
h)
0
20
40
60
80
100
120
Experimental Variogram
Exponential Variogram
Nugget = 0Sill = 35Range = 8Distance to Asymptotic "Sill" = 48
Station 271+00 to 269+00
< 1%< 29<70%
1%29 - 3470-80%
4%34 - 3880-90%
83%38 - 5590-130%
10%55>130%
IC DataCCV% Target
Window Variograms!!!
Courtesy of Dr. David White
Indianapolis - Colts
Superbowl XLIV
Champions - 02/07/2010
?????
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