fhwa/tpf intelligent compaction and recent findings
DESCRIPTION
George’s presentation at the Minnesota AGC annual meeting on Dec. 1, 2009.TRANSCRIPT
FHWA/TPF
Intelligent Compaction
by
George Chang, PhD, PETranstec Group
FHWA/TPF IC Team
Intelligent Compaction
Courtesy of Bomag
Single Smooth Drum IC Rollers
Bomag America
Caterpillar
Ammann/Case Dynapac
Sakai America
Single Drum IC Padfoot RollersSakai
Caterpillar
Ammann/CaseBomag
Tandem Drum IC Roller
Developers
Bomag America
CaterpillarAmmann/Case Dynapac
Sakai AmericaVolvo
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
In-Situ Test Methods for HMANG
PSPA
NNG
LWD-a
LWD
FWD
DCP NG
PLT DSPA BCD
In Situ Test Methods for
Soils/SB/STB
MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
2008
2009
2010
IC Field Demo Schedule
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
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
Key Findings
Values of mapping existing support before
construction or overlay
Significant improvements of rolling
patterns, thus, consistent products
Improvement of roller operators’
acountability
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
HMA Map Subbase
Map
Premature
Failure
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
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
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
Improved Rolling Pattern
Before
After
Sakai
Double-drum
IC roller
TB 04
TB 05
TB05TB04
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
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
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)
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
Benefits of IC
Improve density…better performance
Improve efficiency…cost savings
Increase information…better QC/QA
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
IC Clearing House
www.IntelligentCompaction.com