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

By Omid Ghaemi Staff Geotechnical Engineer, MSCE,MBA GEOTECH ENGINEERING, INC. OTEC 2015 Columbus, Ohio October 27th, 2015

“Exploring Time -Dependent Relationship between Polishing and Friction behaviour of Pavement in the Lab and in the Field on Ohio”

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• 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

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UA Accelerated Polishing Device for HMA surface and

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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.

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British Pendulum Tester

British Pendulum Tester(BPN)

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volumetric test

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ASTM E-965 mean texture depth

Friction and Texture Measurements (Slab Specimen)

17.75” 2”

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4 ” 6 ”

Polished Surface

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Job Mix Formulas(JMF)

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Time-dependent curve fitting equations for BPN in the lab

y = 0.0001x2 - 0.1102x + 75.026 R² = 0.9918

45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00 100 200 300 400 500

BPN Time (min.)

BPN vs. Time

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y = 1E-06x2 - 0.0009x + 1.009 R² = 0.9667

0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 100 200 300 400 500

MTD Time (min.)

MTD vs. Time

Time-dependent curve fitting equations for BPN in the lab

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

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• 1. SN(64)R
• 2. DFT64
• 3. DFT20, and
• 4. MPD
• 5. BPN
• 6. RUT

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The long term field study SPSS 1 5 .0 .1 for w indow s

Existing Pavem ent Sections. New Pavem ent Sections.

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

1 1 0 SN( 6 4 ) R data points.



1 1 0 BPN data points.

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1 1 0 DFT6 4 and DFT2 0 data points.

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1 1 0 MPD data points.

Polish Susceptibility District Location: Route (Section)

• No. of

Measurement s Possible High Polish (Gravel) 10 007 (37.3-39.0) 8 Possible Medium Polish (Limestone) 3 250 (3.55-5.11) 6 Possible Medium Polish (Dolomite) 2 025(15.68-22) 36 Possible Low Polish (Gravel) 11 250 (22.5-25.5) 10 Possible Medium Polish (Limestone) 3 162 (14.00-19.00) 18 Possible Low Polish (Gravel) 11 022(5.00-8.00) 12 Possible Medium Polish (Dolomite) 2 064 (8.90-12.40) 14 Low Polish (Trap Rock) 12 090 (28.25-29.21) 6 Stoneco @ Maumee Aggregate Source HMA Pavement sections identification Existing Pavement Sections Chesterville @ Stockport Sandusky Crushed @ Parkertown Martin Marietta @ Apple Grove Sandusky Crushed @ Parkertown Stocker Sand & Gravel @ Gnadenhutten Stoneco @ Maumee Ontario Trap Rock @ London 19

20 SKID DRIVE ALONG.mpg

 ASTM : E-1911 

Measure friction in different speed in lab and field

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

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LWST PBTester SN MPD DFT10 DFT20 DFT64 BPN

250 East 23.0 0.50 0.78 0.663 0.65 0.55 0.61 6 250 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 0.60 2 250 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 0.63 3 250 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 0.51 0.84 0.67 0.64 0.54 0.63 3.00 0.01 0.13 0.07 0.05 0.04 0.02 1.70 Mean Standard Deviation

Rout Section CTM DFT RUT, mm 2010

Sim ple Linear Regression betw een SN( 6 4 ) R and BPN in different years

Correlation Between Model R2 (%) R2

a (%)

ANOVA 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&2009and 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

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Correlation Between Model R2

(%)

R2

a

(%) ANOVA F P-value

2008

SN(64)R vs ,DFT20,DFT64 and MPD SN(64)R=11.2‐ 0.7DFT20+2.8DFT64+3.24MPD

69 68 27 0.00

2009

SN(64)R vs ,DFT20,DFT64 and MPD SN(64)R=11.2‐ 0.7DFT20+2.8DFT64+3.24MPD

42 40 18.6 0.00 2010

SN(64)R vs ,DFT20,DFT64 and MPD SN(64)R=11.2‐ 0.7DFT20+2.8DFT64+3.24MPD

67 66 23.1 0.00 Combination between 2008&2009and 2010

SN(64)R vs ,DFT20,DFT64 and MPD SN(64)R=11.2‐ 0.7DFT20+2.8DFT64+3.24MPD

55 536 9.8 0.00

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Predictive Method by using the Lab Equation and Traffic Condition in Pavement Section

Polishing Tim e Minimum BPN lab Polishing Tim e Minimum Initial Initial BPN field

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Section Pavements a b c Huron,Route-162 3.41

• 17.33

73.39 Lucas,Route-64 6.66

• 27.35

75.03 Huron,Route-250 4.55

• 20.49

73.21 Washigton, Route-07 0.23

• 3.70

68.93 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

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28 Route Section District Traffic(Total AADT) Huron,Route-162 14.00‐19.00 3 494.44 2.54

0.0051

600 Lucas,Route-64 8.90‐12.40 2 550.00 2.05

0.0037

4390 Huron,Route-250 3.55‐5.11 3 465.00 2.25

0.0048

9290 Washigton, Route-07 37.3‐39.0 10 340.00 8.04

0.0237

3560 Harrison,Route-22 5.00‐8.00 11 385.00 1.93

0.0050

1300 Harrison,Route-250 3.55‐5.11 11 355.56 2.19

0.0062

1430 Wood,Route-25 15.68‐22 2 53.00 2.44

0.0461

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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.

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