Exploring Time -Dependent Relationship between Polishing and - PowerPoint PPT Presentation

“ Exploring Time -Dependent Relationship between Polishing and Friction behaviour of Pavement in the Lab and in the Field on Ohio ” By Omid Ghaemi Staff Geotechnical Engineer, MSCE,MBA GEOTECH ENGINEERING, INC. OTEC 2015 Columbus, Ohio October 27th, 2015 1

1. Statement of the Problem. 2. Objectives of the Research. 3. Accelerated Polishing Device for HMA surface 4. The long term field study between SN and BPN. 5. explore the time-dependent relationships between lab data and field data 2

3

UA Accelerated Polishing Device for HMA surface and 4

1. Test small-size HMA specimens 6” cylindrical gyratory compacted and 18” roller compacted slab specimens. 2. It is fast , comfortable, repeatable , efficient 3. Show trend polishing behavior anticipated time. 4. Simulate polishing and abrasion behavior of HMA in the field for screening polishing and friction properties. 5

6

7

8 British Pendulum Tester(BPN) British Pendulum Tester

ASTM E-965 volumetric test mean texture depth 9

10 Friction and Texture Measurements (Slab 4 ” Specimen) 6 ” 17.75” 2”

11 Polished Surface

Job Mix Formulas(JMF) 12

BPN vs. Time 80.00 75.00 70.00 y = 0.0001x 2 - 0.1102x + 75.026 65.00 BPN R² = 0.9918 60.00 55.00 50.00 45.00 0 100 200 300 400 500 Time (min.) Time-dependent curve fitting equations for BPN in the lab 13

MTD vs. Time 1.1 1.05 1 0.95 y = 1E-06x 2 - 0.0009x + 1.009 MTD 0.9 R² = 0.9667 0.85 0.8 0.75 0.7 0 100 200 300 400 500 Time (min.) Time-dependent curve fitting equations for BPN in the lab 14

Coefficient of Predictive Time-dependent BPN vs Time curve fitting Equation for each section pavement y= a x 2 +b x+ c Section Pavements a b c Huron,Route-162 0.00009 -0.089 73.3900 Lucas,Route-64 0.00010 -0.110 75.0260 Huron,Route-250 0.00010 -0.093 73.2100 Washigton, Route-07 0.00020 -0.136 68.9300 Harrison,Route-22 0.00010 -0.077 71.3300 Harrison,Route-250 0.00009 -0.064 70.8800 Wood,Route-25 0.00010 -0.106 75.2100 Lake,Route-90 0.00010 -0.096 75.2100 15

Coefficient of Predictive Time-dependent MTD vs Time curve fitting equation for each section pavement y= a x2 +b x+ c Section Pavements a b c 0.000001 ‐ 0.0009 1.00 Huron,Route-162 0.000002 ‐ 0.0014 1.07 Lucas,Route-64 0.000002 ‐ 0.0018 0.97 Huron,Route-250 0.000004 ‐ 0.0025 1.29 Washigton, Route-07 0.000001 ‐ 0.0004 0.87 Harrison,Route-22 0.000001 ‐ 0.0006 0.97 Harrison,Route-250 0.000003 ‐ 0.0018 1.07 Wood,Route-25 0.000003 ‐ 0.0022 1.43 Lake,Route-90 16

The long term field study 1. SN(64)R 2. DFT64 3. DFT20, and 4. MPD 5. BPN 6. RUT SPSS 1 5 .0 .1 for w indow s 17

New Pavem ent 18 Pavem ent Sections. Sections. Existing

HMA Pavement sections identification No. of Polish Location: Route Aggregate Source District Measurement Susceptibility (Section) s Existing Pavement Sections Possible High Polish Chesterville @ Stockport 10 007 (37.3-39.0) 8 (Gravel) Possible Medium Sandusky Crushed @ 3 250 (3.55-5.11) 6 Polish (Limestone) Parkertown Possible Medium Stoneco @ Maumee 2 025(15.68-22) 36 Polish (Dolomite) Possible Low Polish Martin Marietta @ Apple Grove 11 250 (22.5-25.5) 10 (Gravel) Possible Medium Sandusky Crushed @ 3 162 (14.00-19.00) 18 Polish (Limestone) Parkertown Possible Low Polish Stocker Sand & Gravel @ 11 022(5.00-8.00) 12 (Gravel) Gnadenhutten Possible Medium Stoneco @ Maumee 2 064 (8.90-12.40) 14 Polish (Dolomite) Low Polish (Trap Ontario Trap Rock @ London 12 090 (28.25-29.21) 6 Rock) 1 1 0 SN( 6 4 ) R data points.  1 1 0 BPN data points.  1 1 0 DFT6 4 and DFT2 0 data points.  1 1 0 MPD data points.  19

20 SKID DRIVE ALONG.mpg

 ASTM : E-1911 Measure friction in different speed in lab and  field 21

 ASTM : E-1 9 1 1  Mean Profile Depth (MPD) 22

2010 LWST CTM DFT PBTester Rout Section RUT, mm SN MPD DFT10 DFT20 DFT64 BPN 250 East 23.0 0.78 0.65 0.55 6 0.61 0.50 0.663 250 East 23.5 0.53 0.88 0.54 0.53 0.46 0.60 4 250 East 24.0 0.50 0.83 0.57 0.56 0.48 2 0.60 250 East 24.5 0.52 0.76 0.67 0.67 0.55 0.62 4 250 East 25.0 0.52 0.78 0.71 0.68 0.57 0.64 5 250 West 23.0 0.50 0.90 0.70 0.67 0.58 3 0.63 250 West 23.5 0.51 1.11 0.64 0.62 0.54 0.62 2 250 West 24.0 0.52 0.91 0.70 0.66 0.54 0.63 1 250 West 24.5 0.53 0.79 0.77 0.69 0.57 0.66 1 250 West 25.0 0.51 0.63 0.71 0.66 0.55 0.65 2 Mean 0.51 0.84 0.67 0.64 0.54 0.63 3.00 Standard Deviation 0.01 0.13 0.07 0.05 0.04 0.02 1.70 23

Sim ple Linear Regression betw een SN( 6 4 ) R and BPN in different years ANOVA R 2 (%) Correlation Between Model R 2 a (%) F P-value 2008 SN(64)R vs. BPN SN(64)R = +0.64 BPN +17.14 53.8 53.3 118.71 0.00 2009 SN(64)R vs. BPN SN(64)R = +0.44 BPN +27.9 37.7 37.1 65.27 0.00 2010 SN(64)R vs. BPN SN(64)R = +1.2615 BPN - 6 66.3 66.1 185.50 0.00 Combination between 2008 & 2009 and 2010 SN(64)R vs. BPN SN(64)R = +0.94 BPN +7.74 48.5 48.4 290.50 0.00 Significant at the p-value smaller than 0.05 24

ANOVA R 2 Correlation a R 2 Model (%) Between (%) F P-value 2008 SN(64)R SN(64)R=11.2 ‐ vs ,DFT20,DFT64 and 69 68 27 0.00 0.7DFT20+2.8DFT64+3.24MPD MPD 2009 SN(64)R vs ,DFT20,DFT64 and SN(64)R=11.2 ‐ 42 40 18.6 0.00 0.7DFT20+2.8DFT64+3.24MPD MPD 2010 SN(64)R vs ,DFT20,DFT64 and SN(64)R=11.2 ‐ 67 66 23.1 0.00 0.7DFT20+2.8DFT64+3.24MPD MPD Combination between 2008 & 2009 and 2010 SN(64)R vs ,DFT20,DFT64 and SN(64)R=11.2 ‐ 55 536 9.8 0.00 0.7DFT20+2.8DFT64+3.24MPD MPD 25

Predictive Method by using the Lab Equation and Traffic Condition in Pavement Section Initial Initial BPN lab BPN field Minimum Minimum Polishing Tim e Polishing Tim e 26

Section Pavements a b c 3.41 -17.33 73.39 Huron,Route-162 6.66 -27.35 75.03 Lucas,Route-64 4.55 -20.49 73.21 Huron,Route-250 0.23 -3.70 68.93 Washigton, Route-07 Harrison,Route-22 3.07 -11.82 71.33 Harrison,Route-250 4.51 -19.74 70.88 Wood,Route-25 3.88 -18.94 75.21 27

Route Section District Traffic(Total AADT) 14.00 ‐ 19.00 3 494.44 2.54 600 Huron,Route-162 0.0051 8.90 ‐ 12.40 2 550.00 2.05 4390 Lucas,Route-64 0.0037 3.55 ‐ 5.11 3 465.00 2.25 9290 Huron,Route-250 0.0048 37.3 ‐ 39.0 10 340.00 8.04 3560 Washigton, Route-07 0.0237 5.00 ‐ 8.00 11 385.00 1.93 1300 Harrison,Route-22 0.0050 3.55 ‐ 5.11 11 355.56 2.19 1430 Harrison,Route-250 0.0062 15.68 ‐ 22 2 53.00 2.44 11000 Wood,Route-25 0.0461 28

Conclusions 1. A predictive method is recommended for new pavement sections by using the lab equation and traffic conditions .This method is designed based on the initial value and minimum value in the lab and in the field 2. Time-dependent curve fitting to develop predictive equations in each pavement section with specific Job Mix Formulas. 3. SN(64)R and BPN have high correlation because of both devices are sensitive to micro-texture. 29

30