![Page 1: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/1.jpg)
FHWA/TPF
Intelligent Compaction
by
George Chang, PhD, PETranstec Group
FHWA/TPF IC Team
![Page 2: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/2.jpg)
Intelligent Compaction
Courtesy of Bomag
![Page 3: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/3.jpg)
Single Smooth Drum IC Rollers
Bomag America
Caterpillar
Ammann/Case Dynapac
Sakai America
![Page 4: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/4.jpg)
Single Drum IC Padfoot RollersSakai
Caterpillar
Ammann/CaseBomag
![Page 5: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/5.jpg)
Tandem Drum IC Roller
Developers
Bomag America
CaterpillarAmmann/Case Dynapac
Sakai AmericaVolvo
![Page 6: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/6.jpg)
In-Situ Testing Methods
Which tests can be used as companion
tests to RMV?
Im pa ct Force
From R ollers 300 m m
LW D /FW D
200 m m
LW D
Nuc lea r
D ensity G a ug e
D yna m ic
C one Penetrom eter
2.1 m
2.1 m
0.3 m0.2 m 0.3 m
1.0 m
X X X X X X X X2.1 m
D ista nce = R oller tra vel in 0.5 sec .
In-situ spot test m ea surem ents
Influence depths a re assum ed ~ 1 x B (width)
Area over witch the
roller M V’s a re a vera g ed
Courtesy of Dr. David White
![Page 7: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/7.jpg)
In-Situ Test Methods for HMANG
PSPA
NNG
LWD-a
![Page 8: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/8.jpg)
LWD
FWD
DCP NG
PLT DSPA BCD
In Situ Test Methods for
Soils/SB/STB
![Page 9: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/9.jpg)
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
2008
2009
2010
IC Field Demo Schedule
![Page 10: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/10.jpg)
IC Field Demo for HMA
State Dates Materials Rollers
MN June, 2008HMA overlay &
new constructionSakai
NY May, 2009HMA new
constructionSakai
MS July, 2009HMA new
constructionSakai
MD July, 2009 SMA overlay Sakai & Bomag
GA Sept, 2009New HMA
constructionSakai
IN Sept, 2009 HMA overlay Sakai & Bomag
![Page 11: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/11.jpg)
IC Field Demo for Soils/SB/STB
State Dates Materials Rollers
TX July, 2008Cohesive Soils,
SB, STB
Case/Ammann
Dynapac
KS Aug, 2008 Cohesive SoilsSakai
Caterpillar
NY May, 2009Incohesive Soils,
SB
Bomag
Caterpillar
MS July, 2009 STBCase/Ammann
Caterpillar
![Page 12: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/12.jpg)
Key Findings
Values of mapping existing support before
construction or overlay
Significant improvements of rolling
patterns, thus, consistent products
Improvement of roller operators’
acountability
![Page 13: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/13.jpg)
Key Findings (cont’d)
Construction process-control greatly
improved
IC-MVs correlate to various in-situ point
measurements
Measurement influence depth varies
depending on technology and site
conditions
Machine operation parameters influence
MVs
![Page 14: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/14.jpg)
HMA Map Subbase
Map
Premature
Failure
![Page 15: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/15.jpg)
Mapping STB 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
![Page 16: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/16.jpg)
Mapping
Milled ACP
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
![Page 17: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/17.jpg)
Accessing
Uniformity
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
![Page 18: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/18.jpg)
Improved Rolling Pattern
Before
After
Sakai
Double-drum
IC roller
TB 04
TB 05
TB05TB04
![Page 19: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/19.jpg)
CBR (%)
0 10 20 30 40 50 60
Depth
(m
m)
0
200
400
600
800
Point 13
CBR (%)
0 10 20 30 40 50 60
Depth
(m
m)
0
200
400
600
800
Point 12
DCP refusal(Box Culvert)
CBR (% )
0 10 20 30 40 50 60
De
pth
(m
m)
0
200
400
600
800
Point 5
CBR (% )
0 10 20 30 40 50 60
De
pth
(m
m)
0
200
400
600
800
Point 1
Point 1
Point 5
Point 12
Point 13
w = 29.5%
d = 13.8 kN/m3
ELWD-Z2 = 11.6 MPa
ELWD-Z2 = 58.4 MPaEV1 = 96.9 MPaEV2 = 381.1 MPaEFWD-D3 = 145 MPaED-SPA = 88 MPa
w = 20.8%
d = 16.0 kN/m3
ELWD-Z2 = 61.1 MPa
ELWD-Z2 = 47.5 MPaEV1 = 42.1 MPaEV2 = 121.1 MPaEFWD-D3 = 57 MPaED-SPA = 44 MPa
Box Culvert
Lime Stabilized
Subgrade
Courtesy of Dr. David White
![Page 20: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/20.jpg)
Compaction Curves
Pass
0 2 4 6 8 10 12 14
CC
V
1
2
3
4
5
6
7
Pass
0 2 4 6 8 10 12 14
ELW
D-Z
2 (
MP
a)
0
10
20
30
40
50
60
Pass
0 2 4 6 8 10 12 14
d (
kN
/m3)
10
12
14
16
18
20
Pass
0 2 4 6 8 10 12 14
CB
R (
%)
0
5
10
15
20
Values corrected using w% values after Pass 1
TB 4 Lane 3:
a = 2.19 mm, v = 6 km/h, f = 26 Hz (High amp setting)
Cohesive SubgradeCourtesy of Dr. David White
![Page 21: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/21.jpg)
Future Work
Improve data management
– Data management guidelines
– Standardization of IC data storage
– Data transfer and archiving
– Data visualization
– Correlation with in-situ tests
– Advanced statistic analyses
Harmonize IC roller measurement values
(RMVs)
![Page 22: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/22.jpg)
Future Work (cont’d)
Implementation Approaches:
– Use IC RMVs as part of QC/QA
– Link IC RMVs to mechanistic QA parameters
within depths of 1~3 m
IC Analysis
– Link IC data to (long-term) performance
– Form committee to review/standardize
analysis approach/protocols
![Page 23: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/23.jpg)
Benefits of IC
Improve density…better performance
Improve efficiency…cost savings
Increase information…better QC/QA
![Page 24: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/24.jpg)
Future IC Spec
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 70
R ange = 15
D istance to Asym ptotic "S ill" = 100
Station 275+00 to 277+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 73
R ange = 23
D istance to Asym ptotic "S ill" = 150
Station 273+00 to 275+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 43
R ange = 12
D istance to Asym ptotic "S ill" = 74
Station 273+00 to 271+00
Lag D istance (h )
0 50 100 150 200
Se
mi-
Va
ria
nc
e g
(h)
0
20
40
60
80
100
120
Experim enta l Variogram
Exponentia l Variogram
N ugget = 0
S ill = 35
R ange = 8
D istance to Asym ptotic "S ill" = 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 25: FHWA/TPF Intelligent Compaction and Recent Findings](https://reader034.vdocument.in/reader034/viewer/2022052508/5596dc001a28abe36a8b473f/html5/thumbnails/25.jpg)
IC Clearing House
www.IntelligentCompaction.com