advances in intelligent compaction for hma
TRANSCRIPT
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Advances in Intelligent
Compaction for HMA
Victor (Lee) Gallivan, PE
FHWA - Office of Pavement Technology
February 3, 2010
NCAUPG HMA Conference
Overland Park, Ks.
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What is Intelligent Compaction
Technology
Office of Pavement Technology
Federal Highway Administration
www.fhwa.dot.gov/pavement/
An Innovation in Compaction
Control and Testing
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----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
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Shortcomings Density Acceptance…
Limited Number of Locations
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Benefits of IC for HMA
Improve density….better performance
Improve efficiency….cost savings
Increase information…better QC/QA
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GPS Base Station GPS Radio & Receiver GPS Rover
Real Time Kinematic (RTK) GPS Precision
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NG
PSPA
NNG
LWD-a
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Bomag America
Caterpillar
Ammann/Case Dynapac
Sakai America
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Mapping of Roller Passes
Longitudinal Joint
Shoulder (Supported)Paving Direction
Courtesy Sakai America
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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
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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.)
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MD
MN
KS
TXMS
IN
NY
PA
VA
ND
GA
TX
WI
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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
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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
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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?
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MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
2008
2009
2010
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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
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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
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HMA Map Subbase
Map
Premature
Failure
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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
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Subbase Mapping3000 vpm, 0.6mm, 5 tracks, 2mph
2500 vpm, 0.6mm, 3 tracks, 2mph
3000 vpm, 0.6mm, 4 tracks, 3 mph
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Day 2 – First Lift of Base Course
Day 3 – 2nd Lift of Base Course
Day 3 – Intermediate Course
s
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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)
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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
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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
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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
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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
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Mapping
Milled HMA
Test bed 02 Mapping
Bomag Evib Sakai CCV
Bomag Sakai
US 340 EB
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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
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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
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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
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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
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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
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Mapping
GAB
Sakai
CCV
Sakai
Double-drum
IC roller
Park & Ride
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TB 01A Intermediate HMA Layer
Sakai CCV
Surface
temperature (oC)
Roller pass
Sakai
Double-drum
IC roller
TB 01A
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Sakai
Double-drum
IC roller
TB 05A
TB 05A Intermediate HMA Layer
Roller passes
Sakai CCV
Outer loop
Inner loop
Nor
th
NG
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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
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Before
After
TB 03 HMA intermediate layer
TB04
TB 03 HMA intermediate layer
TB04
Sakai
Double-drum
IC roller
TB 04
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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
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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
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Benefits of IC
Improve density…better performance
Improve efficiency…cost savings
Increase information…better QC/QA
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Ultimate Goals of TPF IC
Gain the knowledge needed to develop
credible and productive IC specifications
for future projects
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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
![Page 46: Advances in Intelligent Compaction for HMA](https://reader031.vdocuments.mx/reader031/viewer/2022012419/6173758e94c1001a2c22381d/html5/thumbnails/46.jpg)
Indianapolis - Colts
Superbowl XLIV
Champions - 02/07/2010
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