Pavement Surface Microtexture: Testing, Characterization and Frictional Interpretation

Transcription

1 Pavement Surface Microtexture: Testing, Characterization and Frictional Interpretation S h u o L i, S a m y N o u r e l d i n, K a r e n Z h u a n d Y i J i a n g

2 Acknowledgements This project was sponsored by INDOT in cooperation with FHWA through the Joint Transportation Research Program (JTRP). The authors would like to thank Mark Leichty of Ames Engineering for his valuable assistance. The contents of this presentation reflect the views of the authors who are responsible for the facts and the accuracy of the data presented hereafter. The contents do not necessarily reflect the official views or policies of INDOT. This presentation does not constitute a standard, specification, or regulation.

3 1. Introduction INDOT annual inventory friction testing: 6,000-7,000 lane-miles Interstate speed limits: raised from 65 mph to 70 mph in rural areas Safety concerns arisen during friction testing with the ASTM E274 locked wheel trailer on high speed highways Correlation between friction and macrotexture: weak as indicated in the published studies Surface microtexture: Not addressed

4 2. Objectives Conduct field testing to validate microtexture testing devices, particularly laser-based sensors Procure first-hand information on microtexture characteristics on typical pavement surfaces Identify possible parameters used to characterize microtexture Investigate possible correlation between friction and both macrotexture and microtexture measurements

5 3. Fundamentals on Surface Texture Basic equation for periodic waves f (1) where, f=frequency; ν=phase velocity; and λ=wavelength PIARC texture definition Macrotextures: λ= mm, A= mm Microtextures: λ 0.5 mm, and A= m

6 Microtexture Wavelength, Velocity and Frequency During testing, laser scanning physical, static pavement surface texture at the speed of test vehicle Equivalent to a series of traveling texture waves passing the laser at the speed of test vehicle TABLE 1 Microtexture frequencies by wavelength and speeds Test Speed (mph) Frequency by Wavelength (Hz) 0.5-mm 0.03-mm mm

7 Requirements for Lasers in Microtexture Testing The Nyquist theorem: it states that the sampling frequency should be at least twice the highest frequency contained in the signal to avoid aliasing Laser spot size, sampling resolution, and sampling spacing Selection of Lasers Lasers with high or very high frequency either expensive or not available Compromise needed between test speed and laser frequency when choosing lasers

8 TABLE 2 Required Laser Frequencies for Microtexture Testing Test Speed Required Frequency by Texture Wavelength (khz) (mph) 0.5-mm mm mm

9 The Selected Microtexture Measuring Device 1-kHz laser scanner A stand-alone system Scanning speed: 15 mm/sec Spot size: mm Vertical resolution: mm Sampling spacing: mm Wavelength range: mm

10 TABLE 3 Laser System Specifications Description 1-kHz Laser CTMeter Laser spot size mm 0.07 mm Vertical Resolution mm mm Sampling Spacing mm 0.87 mm Wavelength Range 0.03 mm to 50 mm Not Available TABLE 4 Macrotexture MPD Measurements Pavement Type Devices 1 khz Laser CTMeter Slick concrete mm HMA Tined Concrete Microsurfacing (SR-227) mm UTO (SR-227) mm UTO (SR-29)

11 4. Development of Microtexture Profiles Microtexture Profiles Cores taken from INDOT friction test track - Slick concrete surface mm SuperPave mix, and - Tined concrete surface (3x3x20mm) Data processing - Bandpass for scanning Low pass: 0.25 mm High pass: 0.50 mm - Filtering sample spacing 1/3 Low pass: mm Fig. 1 Cores from INDOT friction test track

12 Microtexture Profiles Microtexture profiles on the three different surfaces - Slick surface: uniform occurrences and consistent amplitudes FIG. 2 Typical microtexture profiles on three different surfaces

13 FIG. 2 Continued

14 FIG. 2 Continued - HMA/tined surfaces: dense occurrences at some locations, and sparse occurrences at other locations, and greater amplitudes and variations

15 Microtexture profile components - Large-scale waves: overall shape of the profile (broken line) - Small-scale waves: local roughness of the profile FIG. 3 Example plot for manual calculation of wavelength

16 Microtexture profile dimensions TABLE 4 Microtexture Wavelength Slick Concrete HMA Tined Concrete Wavelength Large- Scale Small- Scale Large- Scale Small- Scale Large- Scale Small- Scale Ave (mm) Stdev (mm) Combined Combined Combined Ave (mm) Stdev. (mm) N Range (mm)

17 - Large-scale waves The smallest wavelength: slick concrete surface The greatest wavelength: HMA surface - Small-scale waves Almost the same for the three surfaces - Differences mainly associated with the large-scale waves - Large- and small-scale waves combined Average wavelength: very close for the three surfaces Standard deviation: close Maximum wavelength: Greatest on tined, Smallest on HMA

18 TABLE 5 Microtexture Depth Distributions Percentile Microtexture Depth (mm) Slick Concrete HMA Tined Concrete 50 th th th th th th Average Depth Stdev COV (%)

19 - Percentile definition The total percentage of the texture depths less than a certain value - Most microtexture depths (more than 95%) less than 0.05 mm for all the three surfaces - Average depth Slick surface: smallest Tined concrete surface: greatest - Standard deviation Slick concrete surface: smallest HMA surface: greatest 27% of steel slag, and 27% of dolomite

20 5. Characterization of Microtexture Profiles Microtexture Profile Properties Mean profile depth (MPD) Calculation similar to macrotexture Slope variance (SV), sharpness of asperities SV SV n 2 i (2) where, x i = measured x-coordinate y i = measured y-coordinate n = number of segments in a baseline, and SV i = defined in Eq. 3 SV i y x i i 1 1 y x i i y x i i (i=1, 2, n-1) (3)

21 Root mean square (RMS), magnitude of the asperities RMS Peak n 2 i (4) where, y i = measured y-coordinate Y i = predicted y-coordinate, and n = number of segments in a baseline, and Peak i = defined in Eq. 5 Peak i y i Y i (i=1, 2, n) (5)

22 Effect of Baseline Length Macrotexture profile (ASTM E1845) Baseline length: 100 mm (twice the maximum wavelength) Microtexture profile - No specifications currently available - To be consistent with macrotexture profile - Estimated in this study: 1.0 mm mm (0.5 in.) 25.4 mm (1 in.) 50.8 mm (2 in.), and 100 mm (4 in.)

23 FIG. 4 Variations of MPD, SV, and RMS with baseline length

24 FIG. 4 Continued

25 FIG. 4 Continued

26 MPD, SV and RMS vs. baseline length - MPD, SV and RMS increase as the baseline length increases regardless of surface type - The increase rate decreases as the baseline length increases regardless of surface type - A turning point occurred on all curves, respectively, when the baseline length = mm (0.5 in) - SV and RMS remain relatively stable after the baseline length = mm (0.5 in) - A baseline of mm appears reasonable in the computation of microtexture profile properties

27 MPD, SV and RMS with a mm Baseline MPD Greatest on HMA surface (probably due to the greatest depth variations), smallest on slick concrete surface SV Greatest on tined concrete surface, smallest on slick surface RMS Greatest on HMA and tined surfaces, smallest on slick surface TABLE 6 MPD, SV, and RMS Based on mm Baseline Surface Slick Concrete HMA Tined Concrete MPD (mm) SV (mm) RMS (mm)

28 6. Use of Microtexture Measurements in Estimating Friction Pavement Friction Components Friction force (NCHRP-37, Kummer and Meyer) - Adhesion force: dominant on dry pavement and smooth surface - Hysteresis force: dominant on rough and wet surface - In-service pavements: adhesion and hysteresis forces apply Field friction testing - Test section: In-service HMA surface and INDOT friction test track - Friction testing ASTM E-274 locked wheel trailer Smooth and ribbed Wet (applying water) and dry

29 Friction test results on in-service HMA surface - Pavement surface: smooth, no distresses but polished aggregates - Friction numbers On dry surface: FN (smooth tire) greater than FN (ribbed tire) On wet surface: FN (ribbed tire) greater than FN (smooth tire) From dry to wet: FN decreased dramatically by 71 points for smooth tire, and 41 points for ribbed tire TABLE 7 Friction Numbers on Wet/Dry Pavement (In-Service) Surface Condition Test Tire Test Speed Friction Number Wet Smooth 40 mph 27 Dry Smooth 40 mph 98 Wet Ribbed 40 mph 37 Dry Ribbed 40 mph 78

30 Comparison of Texture and Friction Friction variation with texture depth (INDOT friction test track) FIG. 5 Variations of Surface Friction with Texture Depth

31 FIG. 5 Continued - FN wet increased as macrotexture MPD increased - FN wet decreased and then increased as microtexture MPD increased - Limited effects on FN dry by both macro- and microtexture MPD

32 Friction variation with microtexture SV and RMS FIG. 6 Variations of FN with Microtexture SV and RMS

33 FIG. 6 Continued - FN variations with RMS and SV following a similar trend - FN is proportional to microtexture SV - As SV increased, FN difference decreased

34 Correlations between FN and texture TABLE 8 Pearson Correlations between Friction and Texture Friction Measurement Macrotexture MPD Texture Measurement Microtexture MPD Microtexture SV Wet Pavement FN Dry Pavement FN FN wet has a positive relationship with all three variables. - Microtexture SV has an effect equivalent to macrotexture MPD, but greater than that by microtexture MPD. - FN dry has a negative relationship with all three variables. - Microtexture affects FN dry more than macrotexture.

35 7. Conclusions Microtexture testing - Texture profile is a series of waves passing the laser mounted on a test vehicle travelling at a specific speed. - The requirements for lasers can bed defined by the frequencyvelocity-wavelength equation and the Nyquist sampling theorem. Microtexture components - Large-scale waves: the overall shape Wavelengths varies with surface type - Small-scale waves: the local roughness Wavelengths remain similar on different surfaces - The differences between the microtexture profiles of different pavements arose mainly in the large-scale waves

36 Parameters characterizing microtexture - MPD, SV and RMS can be used: MPD: depth SV: sharpness of asperities RMS: magnitude of asperities - The length of baseline affects the MPD, SV, and RMS values. - A baseline greater than mm (0.5 in.) can yield stable SCV and RMS values. Correlations between FN and textures - FN increases as microtexture SV and RMS values increase - SV and RMS have an equivalent effect on FN - Macrotexture MPD and microtexture SV have equivalent effect on FN wet - FN wet is more sensitive to microtexture SV than to microtexture MPD

37 Questions and Comments? S h u o L i, S a m y N o u r e l d i n, a n d K a r e n Z h u I n d i a n a D e p a r t m e n t o f T r a n s p o r t a t i o n D i v i s i o n o f R e s e a r c h a n d D e v e l o p m e n t W e s t L a f a y e t t e, I N Y i J i a n g, P h. D., P. E., P r o f e s s o r D e p a r t m e n t o f B u i l d i n g a n d C o n s t r u c t i o n M a n a g e m e n t P u r d u e U n i v e r s i t y, W e s t L a f a y e t t e, I N