Advances in Intelligent Compaction for HMA NCAUPG HMA Conference Overland Park, Ks. Victor (Lee) Gallivan, PE FHWA - Office of Pavement Technology February 3, 2010
What is Intelligent Compaction Technology An Innovation in Compaction Control and Testing Office of Pavement Technology Federal Highway Administration www.fhwa.dot.gov/pavement/
----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 LWD-a NNG PSPA
Ammann/Case Dynapac Caterpillar Bomag America Sakai America
Mapping of Roller Passes Shoulder (Supported) Paving Direction Longitudinal Joint Courtesy Sakai America
Correlation w/ In-Situ Testing Area over witch the roller MV s are averaged In-situ spot test measurements X 2.1 m X X X X X X X Impact Force From Rollers Distance = Roller travel in 0.5 sec. 300 mm LWD/FWD 200 mm LWD Nuclear Density Gauge Dynamic Cone Penetrometer 2.1 m 0.3 m 0.2 m 0.3 m 1.0 m 2.1 m Influence depths are assumed ~ 1 x B (width) 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.)
ND MN WI NY PA KS IN VA MD TX TX MS GA
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?
ND MN WI NY KS IN PA VA MD 2008 2009 2010 TX MS GA
ND MN WI NY Route 4, Kandiyohi County, MN KS IN PA MD VA Mapping existing subbase TX MS GA New HMA construction Sakai double-drum IC roller
Subbase Mapping Reflection of hard spots on the HMA layer HMA Map HMA non-wearing course layer map a = 0.6 mm, f = 3000 vpm Subbase Map Class 5 aggregate subbase layer map, a = 0.6 mm, f = 2500 vpm CCV HMA (a = 0.6 mm, f = 3000) 25 20 15 10 5 0 y = 2.45 ln(x) + 2.3 R 2 = 0.69 0 5 10 15 20 25 CCV Subbase (a = 0.6 mm, f = 2500) CCV Subbase (a = 0.3 mm, f = 3000) 25 20 15 10 5 0 0 y R CC Reflection of hard spots on the HMA layer Reflection of soft spots on the HMA layer CCV Subbase (a = 0.3 mm, f = 3000) 25 20 15 10 5 0 y = 0.27x + 8.0 R 2 = 0.30 0 5 10 15 2 Sakai double-drum IC roller CCV Subbase (a = 0.6 mm, f =
Premature Failure HMA Map Subbase Map
ND MN WI NY Peter s Road, Springville, NY KS IN PA MD VA Mapping existing subbase TX MS GA New HMA construction Sakai double-drum IC roller
Subbase Mapping 3000 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 s Day 3 Intermediate Course
120 125 130 135 140 145 NNG density (pcf) 125 127 129 131 133 135 137 139 141 143 145 NNG density (pcf) NG vs NNG 1st lift base NG 125 124 123 y = 0.0978x + 108.26 R 2 = 0.1473 NG vs NNG Linear (NG vs NNG) 122 121 120 NG density (pcf) Binder base NNG (PQI) 130 129 128 NG vs NNG Linear (NG vs NNG) 127 126 125 124 123 122 121 120 y = 0.118x + 107.54 R 2 = 0.1552 NG density (pcf)
ND MN WI NY US 84, Wayne County, MS KS IN PA MD VA Mapping existing stabilized base TX MS GA New HMA Construction Sakai double-drum IC roller
CCVs TB 2B-2 TB 2C-2 TB 2B-1 TB 2C-1 TB 2A-3 TB 2A-2 TB 2A-1 N Mapping Results TB 2A-1 TB 2A-2 TB 2A-3 Mapping w/ Sakai double-drum IC roller TB 2B-1 TB 2C-1 25 20 15 TB 2B-2 TB 2C-2 10 5 0 TB02A (5-day cure) TB02B (6-day cure) TB02C (7-day cure)
Variogram Variogram Sakai CCV Semi-variogram of CCV Column D Direction: 0.0 Tolerance: 90.0 3.5 Exponential Model 3 2.5 2 North 1.5 1 0.5 Nugget=1.38 Sill = 2.2 Range = 35 0 0 20 40 60 80 100 120 140 160 180 200 Lag Distance EB Lane 1 (400 to 582 m) 2.5 Column D Direction: 0.0 Tolerance: 90.0 2 Exponential Model 1.5 1 0.5 Nugget=1.68 Sill = 2.2 Range = 30 Sakai double-drum IC roller 0 0 50 100 150 200 250 300 Lag Distance EB Lane 1 (0 to 300 m) Total length of 582 m
ND MN WI NY US 340EB, Frederick, MD KS IN PA MD VA SMA overlay TX MS GA Mapping milled HMA surface Bomag double-drum IC roller Sakai double-drum IC roller
Test bed 02 Mapping Bomag Evib Bomag Sakai Sakai CCV Mapping Milled HMA US 340 EB
TB 03A Mapping on Exiting HMA Pavement Variogram Sakai CCV Kridging Map North 90 80 Lane 1 Shoulder 70 60 50 40 30 20 Mapping Milled HMA Semi-variogram for CCV 10 0 Bridge 500 450 Column L: CCV Direction: 0.0 Tolerance: 90.0 400 350 300 250 200 150 100 Exponential Model Nugget = 300 Sill = 398 Range = 65 Sakai double-drum IC roller 50 0 0 50 100 150 200 250 300 350 400 450 Lag Distance
TB 03B SMA overlay (distance 0 to 684 m) Variogram SAKAI CCV Surface Temperature 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 280 260 240 220 200 180 160 140 Semi-variogram - exponential model Column L: CCV Direction: 0.0 Tolerance: 90.0 35 30 25 20 15 10 5 Nugget=16.5 Sill=28.5 Range=40 0 0 50 100 150 200 250 300 350 400 450 Lag Distance
PSPA Seismic modulus of existing HMA layer (ksi) PSPA seismic modulus of existing HMA pavement (ksi) Existing pavements 900 800 700 600 500 400 300 200 200 300 400 500 600 700 800 900 Back-calculated modulus of existing HMA pavement (ksi) PSPA vs FWD New SMA constrcution 650 600 550 500 450 400 350 y = 1.011x + 477.16 R 2 = 0.1289 Modulus of Existing HMA Layer vs SMA Overlay CCV Linear (Modulus of Existing HMA Layer vs SMA Overlay CCV) 300 0.00 20.00 40.00 60.00 80.00 100.00 PSPA Vs IC SAKAI CCV on Existing HMA Pavement
Density 165 160 155 150 145 140 y = 0.2858x + 149.28 R 2 = 0.2031 Density vs CCV Linear (Density vs CCV) 0 5 10 15 20 25 30 35 40 SAKAI CCV IC RMV vs NG NG Sakai Double-drum IC roller
ND MN WI NY Park&Ride, Clayton County, GA KS IN PA MD VA Mapping subbase TX MS GA New HMA construction Sakai double-drum IC roller
Sakai CCV Mapping GAB Park & Ride Sakai Double-drum IC roller
TB 01A Intermediate HMA Layer Roller pass Sakai CCV TB 01A Surface temperature ( o C) Sakai Double-drum IC roller
TB 05A Intermediate HMA Layer Outer loop Roller passes Inner loop North Sakai CCV TB 05A Sakai Double-drum IC roller NG
ND MN WI NY US 52, West Lafayette, IN KS IN PA MD VA Mapping milled HMA surface TX MS GA New HMA overlay Sakai Bomag
Before After Sakai Double-drum IC roller TB 03 HMA intermediate layer TB04 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
Semi-Variance g(h 80 60 40 20 0 Station 275+00 to 277+00 Nugget = 0 Sill = 70 Range = 15 Distance to Asymptotic "Sill" = 100 0 50 100 150 200 Lag Distance (h) Semi-Variance g(h) 80 80 60 60 Future IC Spec 40 40 40 Station Station 273+00 275+00 to to 275+00 277+00 Nugget Nugget = 0 20 Sill 20 Sill = 73 70 Range Range = 23 15 Distance Distance to to Asymptotic Asymptotic "Sill" "Sill" = 150 100 0 0 50 50 100 100 150 150 200 200 Lag Lag Distance Distance (h) (h) Window Variograms!!! Semi-Variance g(h) 80 60 40 20 0 0 5 Semi-Variance g(h) 120 Experimental Variogram Exponential Variogram 100 80 60 40 20 Station 273+00 to 271+00 Nugget = 0 Sill = 43 Range = 12 Distance to Asymptotic "Sill" = 74 Station 275+00 to 277+00 Nugget = 0 Sill = 70 Range = 15 Distance to Asymptotic "Sill" = 100 0 0 50 100 150 200 Lag Distance (h) Semi-Variance g(h) 120 120 Experimental Variogram Experimental Exponential Variogram Variogram 100 Exponential Variogram 100 Station 271+00 to 269+00 80 80 Nugget Station = 0 273+00 to 271+00 Sill = Nugget 35 = 0 Range Sill = 8 43 60 Distance Range to = Asymptotic 12 "Sill" = 48 60 Distance to Asymptotic "Sill" = 74 40 40 Station 275+00 273+00 to 277+00 275+00 Nugget = 0 20 Sill Sill = 70 73 20 Range Range = 15 23 Distance to to Asymptotic "Sill" "Sill" = 100 150 0 0 50 50 Lag Distance 100 100 (h) 150 150 200 200 Lag Distance (h) Semi-Variance Semi-Variance g(h) g(h) 120 120 Exp Exp 100 100 80 80 60 60 40 40 Stat Nugg Sill = Rang Dista 20 20 0 0 0 0 5 Semi-Variance g(h) 120 100 80 60 40 Experimental Variogram Exponential Variogram Station 273+00 to 271+00 Nugget = 0 Sill = 43 Range = 12 Distance to Asymptotic "Sill" = 74 Semi-Variance Semi-Variance g(h) g(h) 120 120 100 100 80 80 60 60 40 40 % Target Experimental Variogram Experimental Variogram Exponential Variogram 55 Exponential Variogram >130% 90-130% 80-90% CCV 38-55 34-38 45% 31% 17% 10% 52% 59% 79% 83% 3% 6% 4% <1% 4% < 0% 1% Station 271+00 to 269+00 Station 273+00 to 271+00 Nugget 70-80% = 0 Nugget = 0 29-34 Sill = 35 Range <70% Sill = 43 8 < 29 Distance Range to = Asymptotic 12 "Sill" = 48 Distance to Asymptotic "Sill" = 74 IC Data 120 Courtesy of Dr. David White Semi-Variance g(h) 100 80 60 40 Exp Exp Stati Nugge Sill = 3 Range Distan 20 20
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