fhwa/tpf intelligent compaction study · 2020. 5. 21. · transtec group the the acknowledgement...
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THETRANSTEC GROUP
THE
FHWA/TPF FHWA/TPF Intelligent Compaction StudyIntelligent Compaction Study
ByByGeorge Chang, PhD, PEGeorge Chang, PhD, PE
The Transtec GroupThe Transtec Group
THETRANSTEC GROUP
THE
AcknowledgementAcknowledgementVDOT VDOT –– Brian Diefenderfer and Trenton ClarkBrian Diefenderfer and Trenton ClarkSuperior Paving Superior Paving –– Dave Helmick, James Dave Helmick, James Mitchell, Chris Griffith, Todd Atkins, and etc.Mitchell, Chris Griffith, Todd Atkins, and etc.Volvo Volvo –– Dale Starry, Chad Fluent, Mohammad Dale Starry, Chad Fluent, Mohammad Siam.Siam.OU OU –– Prof. Sesh Commuri and students.Prof. Sesh Commuri and students.Sakai Sakai –– Todd MansellTodd MansellKessler Kessler –– Larry and Virginia AickenLarry and Virginia AickenTrimble Trimble –– Pete Kaz and Bruce HanesPete Kaz and Bruce HanesKeystone Precision Keystone Precision –– Mike WinsorMike Winsor
THETRANSTEC GROUP
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Transportation Pooled Fund #954Transportation Pooled Fund #954
““Accelerated implementation of intelligent Accelerated implementation of intelligent compaction for embankment subgrade compaction for embankment subgrade soil, aggregate base, and asphalt soil, aggregate base, and asphalt pavement materialpavement material””33--year IC study for all the above materialsyear IC study for all the above materials12 participating States12 participating States12+ field demonstration12+ field demonstration
THETRANSTEC GROUP
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MN
KS
TXMS
IN
NY
PA
VA
MD
ND
GA
WI
200820092010
IC Field Demo Schedule
Mini demo
2 demos
THETRANSTEC GROUP
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ObjectivesObjectives
Accelerated development of QC/QA Accelerated development of QC/QA specifications for subgrade soils, aggregate specifications for subgrade soils, aggregate base and asphalt pavement materialsbase and asphalt pavement materialsDevelop an experienced and knowledgeable IC Develop an experienced and knowledgeable IC expertise base within Pool Fund participating expertise base within Pool Fund participating State State DOTsDOTsIdentify and prioritize needed improvements to Identify and prioritize needed improvements to and/or research of IC equipment and field and/or research of IC equipment and field QC/QA testing equipmentQC/QA testing equipment
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Prioritization of IC ImprovementsPrioritization of IC Improvements
Simplifying IC usageSimplifying IC usageAchieving greater IC value, cost benefit, Achieving greater IC value, cost benefit, etc.etc.Improved accuracyImproved accuracy
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Task Working Group (TWG)Task Working Group (TWG)Mr. Victor Gallivan
FHWACOTR
Dr. George K. Chang, P.E.Transtec Group, Inc.
PI
Dr. David WhiteIowa State University
Co-PI
Mr. Larry L. MichaelAsphalt Technology Consultants
Co-PI
Mr. Jason DickTranstec Group, Inc.
Programmer
Mr. David Merritt andMr. Dennis Turner
Transtec Group, Inc.Technical Writers
Mr. Bob HoranAsphalt Institute
IC Facilitator
Pooled Fund StateRepresentatives
Dr. Rob O. Rasmussen, P.E.Transtec Group, Inc.
Project Management Support
IC Roller VendorRepresentatives
THETRANSTEC GROUP
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Application of Material TypesApplication of Material Types
Type I Type I : Non: Non--cohesive subgrade soilcohesive subgrade soilType II Type II : Cohesive subgrade soils: Cohesive subgrade soilsType IIIType III : Aggregate base material: Aggregate base materialType IVType IV : Asphalt pavement material: Asphalt pavement materialType VType V : Stabilized base material: Stabilized base material
THETRANSTEC GROUP
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IC Roller RequirementsIC Roller Requirements
Continuous rollerContinuous roller--integrated measurement integrated measurement systemsystemRealReal--Time Kinematic (RTK) Global Time Kinematic (RTK) Global Position System (GPS) based mappingPosition System (GPS) based mappingRealReal--time onboard display and integrated time onboard display and integrated software reporting systemsoftware reporting system(Optional) Feedback control(Optional) Feedback control
THETRANSTEC GROUP
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THETRANSTEC GROUP
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Participating Soil/SB RollersParticipating Soil/SB Rollers
BomagBomag AmericaAmerica
CaterpillarCaterpillar
AmmannAmmann/Case/Case DynapacDynapac
Sakai AmericaSakai America
THETRANSTEC GROUP
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Participating Asphalt RollersParticipating Asphalt RollersBomagBomag AmericaAmerica
CaterpillarCaterpillarAmmannAmmann/Case/Case DynapacDynapac
Sakai AmericaSakai America VolvoVolvo
THETRANSTEC GROUP
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CaterpillarCMV, RMV, MDP
DynapacCMV
BomagEVIB
SakaiCCV
Ammann/CASEkb
Roller Measurement ValuesRoller Measurement Values
Courtesy of Dr. David White
THETRANSTEC GROUP
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InIn--Situ Testing MethodsSitu Testing Methods
Which tests can be used as companion tests to RMV?
Impact ForceFrom Rollers 300 mm
LWD/FWD200 mm
LWDNuclear
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
THETRANSTEC GROUP
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InIn--Situ Test Methods for HMASitu Test Methods for HMANGNG
PSPAPSPA
NNGNNG
LWDLWD--aa
THETRANSTEC GROUP
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LWDLWD
FWDFWD
DCPDCP NGNG
PLTPLT DSPADSPA BCDBCD
In Situ Test Methods for Soils/SB/STB
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Key Findings
Values of mapping existing support before construction or overlaySignificant improvements of rolling patterns, thus, consistent productsImprovement of roller operators’ acountability
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Key Findings (contKey Findings (cont’’d)d)Construction processConstruction process--control greatly control greatly improvedimprovedICIC--MVsMVs correlate to various incorrelate to various in--situ point situ point measurementsmeasurementsMeasurement influence depth varies Measurement influence depth varies depending on technology and site depending on technology and site conditionsconditionsMachine operation parameters influence Machine operation parameters influence MVsMVs
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Ultimate Goals of TPF ICUltimate Goals of TPF IC
Gain the knowledge needed to develop Gain the knowledge needed to develop credible and productive credible and productive IC specificationsIC specifications for future projectsfor future projects
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Future IC Spec
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E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 7 0R a n g e = 1 5D is ta n c e to A s ym p to tic "S ill" = 1 0 0
S ta tio n 2 7 5 + 0 0 to 2 7 7 + 0 0
< 1%< 29<70%<1%29 ‐ 3470‐80%3%34 ‐ 3880‐90%52%38 ‐ 5590‐130%45%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
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2 0
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E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 7 0R a n g e = 1 5D is ta n c e to A s ym p to tic "S ill" = 1 0 0
S ta tio n 2 7 5 + 0 0 to 2 7 7 + 0 0
< 1%< 29<70%<1%29 ‐ 3470‐80%3%34 ‐ 3880‐90%52%38 ‐ 5590‐130%45%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
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e g (
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0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 7 3R a n g e = 2 3D is ta n c e to A s ym p to tic "S ill" = 1 5 0
S ta tio n 2 7 3 + 0 0 to 2 7 5 + 0 0
< 1%< 29<70%4%29 ‐ 3470‐80%6%34 ‐ 3880‐90%59%38 ‐ 5590‐130%31%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
i-Var
ianc
e g (
h)
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 7 3R a n g e = 2 3D is ta n c e to A s ym p to tic "S ill" = 1 5 0
S ta tio n 2 7 3 + 0 0 to 2 7 5 + 0 0
< 1%< 29<70%4%29 ‐ 3470‐80%6%34 ‐ 3880‐90%59%38 ‐ 5590‐130%31%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
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e g (
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0
2 0
4 0
6 0
8 0
1 0 0
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E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 4 3R a n g e = 1 2D is ta n c e to A s ym p to tic "S ill" = 7 4
S ta t io n 2 7 3 + 0 0 to 2 7 1 + 0 0
0%< 29<70%<1%29 ‐ 3470‐80%4%34 ‐ 3880‐90%79%38 ‐ 5590‐130%17%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
i-Var
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e g (
h)
0
2 0
4 0
6 0
8 0
1 0 0
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E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 4 3R a n g e = 1 2D is ta n c e to A s ym p to tic "S ill" = 7 4
S ta t io n 2 7 3 + 0 0 to 2 7 1 + 0 0
0%< 29<70%<1%29 ‐ 3470‐80%4%34 ‐ 3880‐90%79%38 ‐ 5590‐130%17%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
i-Var
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e g (
h)
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 3 5R a n g e = 8D is ta n c e to A s ym p to tic "S ill" = 4 8
S ta t io n 2 7 1 + 0 0 to 2 6 9 + 0 0
< 1%< 29<70%1%29 ‐ 3470‐80%4%34 ‐ 3880‐90%83%38 ‐ 5590‐130%10%55>130%
IC DataCCV% Target
L a g D is ta n c e (h )0 5 0 1 0 0 1 5 0 2 0 0
Sem
i-Var
ianc
e g (
h)
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
E x p e r im e n ta l V a r io g ra mE x p o n e n tia l V a r io g ra m
N u g g e t = 0S ill = 3 5R a n g e = 8D is ta n c e to A s ym p to tic "S ill" = 4 8
S ta t io n 2 7 1 + 0 0 to 2 6 9 + 0 0
< 1%< 29<70%1%29 ‐ 3470‐80%4%34 ‐ 3880‐90%83%38 ‐ 5590‐130%10%55>130%
IC DataCCV% Target
Courtesy of Dr. David White
THETRANSTEC GROUP
THE
IC Clearing HouseIC Clearing House
www.IntelligentCompaction.com