Advances in Intelligent Compaction for HMA

Transcription

1 Advances in Intelligent Compaction for HMA NCAUPG HMA Conference Overland Park, Ks. Victor (Lee) Gallivan, PE FHWA - Office of Pavement Technology February 3, 2010

2 What is Intelligent Compaction Technology An Innovation in Compaction Control and Testing Office of Pavement Technology Federal Highway Administration

3 ----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 TIMES A MINUTE

4 Shortcomings Density Acceptance Limited Number of Locations

5 Benefits of IC for HMA Improve density.better performance Improve efficiency.cost savings Increase information better QC/QA

6 GPS Base Station GPS Radio & Receiver GPS Rover Real Time Kinematic (RTK) GPS Precision

7 NG LWD-a NNG PSPA

8 Ammann/Case Dynapac Caterpillar Bomag America Sakai America

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10 Mapping of Roller Passes Shoulder (Supported) Paving Direction Longitudinal Joint Courtesy Sakai America

11 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

12 IC National Efforts NCHRP 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.)

13 ND MN WI NY PA KS IN VA MD TX TX MS GA

14 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

15 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

16 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?

17 ND MN WI NY KS IN PA VA MD TX MS GA

18 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

19 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) y = 2.45 ln(x) R 2 = CCV Subbase (a = 0.6 mm, f = 2500) CCV Subbase (a = 0.3 mm, f = 3000) 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) y = 0.27x R 2 = Sakai double-drum IC roller CCV Subbase (a = 0.6 mm, f =

20 Premature Failure HMA Map Subbase Map

21 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

22 Subbase Mapping 3000 vpm, 0.6mm, 5 tracks, 2mph 2500 vpm, 0.6mm, 3 tracks, 2mph 3000 vpm, 0.6mm, 4 tracks, 3 mph

23 Day 2 First Lift of Base Course Day 3 2nd Lift of Base Course s Day 3 Intermediate Course

24 NNG density (pcf) NNG density (pcf) NG vs NNG 1st lift base NG y = x R 2 = NG vs NNG Linear (NG vs NNG) NG density (pcf) Binder base NNG (PQI) NG vs NNG Linear (NG vs NNG) y = 0.118x R 2 = NG density (pcf)

25 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

26 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 TB 2B-2 TB 2C TB02A (5-day cure) TB02B (6-day cure) TB02C (7-day cure)

27 Variogram Variogram Sakai CCV Semi-variogram of CCV Column D Direction: 0.0 Tolerance: Exponential Model North Nugget=1.38 Sill = 2.2 Range = Lag Distance EB Lane 1 (400 to 582 m) 2.5 Column D Direction: 0.0 Tolerance: Exponential Model Nugget=1.68 Sill = 2.2 Range = 30 Sakai double-drum IC roller Lag Distance EB Lane 1 (0 to 300 m) Total length of 582 m

28 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

29 Test bed 02 Mapping Bomag Evib Bomag Sakai Sakai CCV Mapping Milled HMA US 340 EB

30 TB 03A Mapping on Exiting HMA Pavement Variogram Sakai CCV Kridging Map North Lane 1 Shoulder Mapping Milled HMA Semi-variogram for CCV 10 0 Bridge Column L: CCV Direction: 0.0 Tolerance: Exponential Model Nugget = 300 Sill = 398 Range = 65 Sakai double-drum IC roller Lag Distance

31 TB 03B SMA overlay (distance 0 to 684 m) Variogram SAKAI CCV Surface Temperature Semi-variogram - exponential model Column L: CCV Direction: 0.0 Tolerance: Nugget=16.5 Sill=28.5 Range= Lag Distance

32 PSPA Seismic modulus of existing HMA layer (ksi) PSPA seismic modulus of existing HMA pavement (ksi) Existing pavements Back-calculated modulus of existing HMA pavement (ksi) PSPA vs FWD New SMA constrcution y = 1.011x R 2 = Modulus of Existing HMA Layer vs SMA Overlay CCV Linear (Modulus of Existing HMA Layer vs SMA Overlay CCV) PSPA Vs IC SAKAI CCV on Existing HMA Pavement

33 Density y = x R 2 = Density vs CCV Linear (Density vs CCV) SAKAI CCV IC RMV vs NG NG Sakai Double-drum IC roller

34 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

35 Sakai CCV Mapping GAB Park & Ride Sakai Double-drum IC roller

36 TB 01A Intermediate HMA Layer Roller pass Sakai CCV TB 01A Surface temperature ( o C) Sakai Double-drum IC roller

37 TB 05A Intermediate HMA Layer Outer loop Roller passes Inner loop North Sakai CCV TB 05A Sakai Double-drum IC roller NG

38 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

39 Before After Sakai Double-drum IC roller TB 03 HMA intermediate layer TB04 TB 04

40 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

41 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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43 Benefits of IC Improve density better performance Improve efficiency cost savings Increase information better QC/QA

44 Ultimate Goals of TPF IC Gain the knowledge needed to develop credible and productive IC specifications for future projects

45 Semi-Variance g(h Station to Nugget = 0 Sill = 70 Range = 15 Distance to Asymptotic "Sill" = Lag Distance (h) Semi-Variance g(h) Future IC Spec Station Station to to Nugget Nugget = 0 20 Sill 20 Sill = Range Range = Distance Distance to to Asymptotic Asymptotic "Sill" "Sill" = Lag Lag Distance Distance (h) (h) Window Variograms!!! Semi-Variance g(h) Semi-Variance g(h) 120 Experimental Variogram Exponential Variogram Station to Nugget = 0 Sill = 43 Range = 12 Distance to Asymptotic "Sill" = 74 Station to Nugget = 0 Sill = 70 Range = 15 Distance to Asymptotic "Sill" = Lag Distance (h) Semi-Variance g(h) Experimental Variogram Experimental Exponential Variogram Variogram 100 Exponential Variogram 100 Station to Nugget Station = to Sill = Nugget 35 = 0 Range Sill = Distance Range to = Asymptotic 12 "Sill" = Distance to Asymptotic "Sill" = Station to Nugget = 0 20 Sill Sill = Range Range = Distance to to Asymptotic "Sill" "Sill" = Lag Distance (h) Lag Distance (h) Semi-Variance Semi-Variance g(h) g(h) Exp Exp Stat Nugg Sill = Rang Dista Semi-Variance g(h) Experimental Variogram Exponential Variogram Station to Nugget = 0 Sill = 43 Range = 12 Distance to Asymptotic "Sill" = 74 Semi-Variance Semi-Variance g(h) g(h) % Target Experimental Variogram Experimental Variogram Exponential Variogram 55 Exponential Variogram >130% % 80-90% CCV % 31% 17% 10% 52% 59% 79% 83% 3% 6% 4% <1% 4% < 0% 1% Station to Station to Nugget 70-80% = 0 Nugget = 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) Exp Exp Stati Nugge Sill = 3 Range Distan 20 20

46 Indianapolis - Colts Superbowl XLIV Champions - 02/07/2010?????

47 Thank you