pavement surface microtexture: testing, …pavement surface microtexture: testing, characterization...
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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
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.
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
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
3. Fundamentals on Surface Texture
Basic equation for periodic waves
where, f=frequency; ν=phase velocity; and λ=wavelength
PIARC texture definition
Macrotextures: λ=0.5-50 mm, A=0.1-20 mm Microtextures: λ 0.5 mm, and A=500-0.1 m
f (1)
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
Test Speed (mph)
Frequency by Wavelength (Hz)
0.5-mm 0.03-mm 0.001-mm
10 8939 148981 4469444
20 17878 297963 8938889
30 26817 446944 13408333
40 35756 595926 17877778
50 44694 744907 22347222
60 53633 893889 26816667
TABLE 1 Microtexture frequencies by wavelength and speeds
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
Test Speed (mph)
Required Frequency by Texture Wavelength (kHz)
0.5-mm 0.025-mm 0.001-mm
0.1 0.2 3.0 89.4
1 1.8 29.8 893.9
2 3.6 59.6 1787.8
3 5.4 89.4 2681.7
4 7.2 119.2 3575.6
5 8.9 149.0 4469.4
10 17.9 298.0 8938.9
20 35.8 595.9 17877.8
30 53.6 893.9 26816.7
40 71.5 1191.9 35755.6
50 89.4 1489.9 44694.4
60 107.3 1787.8 53633.3
TABLE 2 Required Laser Frequencies for Microtexture Testing
The Selected Microtexture Measuring Device
1-kHz laser scanner
A stand-alone system
Scanning speed: 15 mm/sec
Spot size: 0.050 mm
Vertical resolution: 0.015 mm
Sampling spacing: 0.015 mm
Wavelength range: 0.03-50 mm
TABLE 3 Laser System Specifications
TABLE 4 Macrotexture MPD Measurements
Description 1-kHz Laser CTMeter
Laser spot size 0.050 mm 0.07 mm
Vertical Resolution 0.015 mm 0.003 mm
Sampling Spacing 0.015 mm 0.87 mm
Wavelength Range 0.03 mm to 50 mm Not Available
Pavement TypeDevices
1 kHz Laser CTMeter
Slick concrete 0.051 0.08
9.5-mm HMA 1.156 0.59
Tined Concrete 1.487 1.45
Microsurfacing (SR-227) 0.663 0.61
4.75-mm UTO (SR-227) 0.21 0.18
4.75-mm UTO (SR-29) 0.22 0.23
4. Development of Microtexture Profiles
Microtexture Profiles
Cores taken from INDOT friction test track
- Slick concrete surface
- 9.5-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: 0.084 mm
Fig. 1 Cores from INDOT friction
test track
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
FIG. 2 Continued
- HMA/tined surfaces: dense occurrences at some locations, and sparse occurrences at other locations, and greater amplitudes and variations
FIG. 2 Continued
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
Wavelength
Slick Concrete HMA Tined Concrete
Large-
Scale
Small-
Scale
Large-
Scale
Small-
Scale
Large-
Scale
Small-
Scale
Ave (mm) 0.131 0.046 0.172 0.046 0.151 0.043
Stdev (mm) 0.062 0.013 0.053 0.013 0.073 0.013
Combined Combined Combined
Ave (mm) 0.082 0.083 0.080
Stdev. (mm) 0.059 0.065 0.067
N 157 156 154
Range (mm) 0.025-0.395 0.020-0.290 0.02-0.475
TABLE 4 Microtexture Wavelength
Microtexture profile dimensions
- 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
Percentile
Microtexture Depth (mm)
Slick Concrete HMA Tined Concrete
50th 0.009 0.010 0.014
70th 0.014 0.016 0.022
85th 0.019 0.023 0.030
90th 0.021 0.027 0.034
95th 0.025 0.034 0.040
100th 0.049 0.050 0.075
Average Depth 0.010 0.014 0.017
Stdev. 0.008 0.021 0.013
COV (%) 75.5 148.7 75.9
TABLE 5 Microtexture Depth Distributions
- Percentile definitionThe total percentage of the texture depths less than acertain value
- Most microtexture depths (more than 95%) less than 0.05 mm for all the three surfaces
- Average depthSlick surface: smallest
Tined concrete surface: greatest
- Standard deviationSlick concrete surface: smallest
HMA surface: greatest
27% of steel slag, and 27% of dolomite
5. Characterization of Microtexture Profiles
Microtexture Profile Properties Mean profile depth (MPD)
Calculation similar to macrotexture
Slope variance (SV), sharpness of asperities
n
SVSV
i
2
i
i
ii
iii
x
y
xx
yySV
1
1
where, xi = measured x-coordinateyi = measured y-coordinaten = number of segments in a
baseline, andSVi = defined in Eq. 3
(2)
(3)(i=1, 2, …… n-1)
Root mean square (RMS), magnitude of the asperities
n
PeakRMS
i
2
iii YyPeak
where, yi = measured y-coordinateYi = predicted y-coordinate, andn = number of segments in a baseline, and
Peaki = defined in Eq. 5
(i=1, 2, …… n) (5)
(4)
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
12.75 mm (0.5 in.)
25.4 mm (1 in.)
50.8 mm (2 in.), and
100 mm (4 in.)
FIG. 4 Variations of MPD, SV, and RMS with baseline length
FIG. 4 Continued
FIG. 4 Continued
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 = 12.75 mm (0.5 in)
- SV and RMS remain relatively stable after the baseline
length = 12.75 mm (0.5 in)
- A baseline of 12.75 mm appears reasonable in the
computation of microtexture profile properties
MPD, SV and RMS with a 12.75 mm Baseline
MPDGreatest on HMA surface (probably due to the greatest depth variations), smallest on slick concrete surface
SVGreatest on tined concrete surface, smallest on slick surface
RMS Greatest on HMA and tined surfaces, smallest on slick surface
Surface Slick Concrete HMA Tined Concrete
MPD (mm) 0.039 0.079 0.061
SV (mm) 0.700 1.000 1.016
RMS (mm) 0.013 0.021 0.021
TABLE 6 MPD, SV, and RMS Based on 12.75 mm Baseline
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 ribbedWet (applying water) and dry
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
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
TABLE 7 Friction Numbers on Wet/Dry Pavement (In-Service)
Comparison of Texture and Friction
Friction variation with texture depth (INDOT friction test track)
FIG. 5 Variations of Surface Friction with Texture Depth
- FNwet increased as macrotexture MPD increased- FNwet decreased and then increased as microtexture MPD increased- Limited effects on FNdry by both macro- and microtexture MPD
FIG. 5 Continued
Friction variation with microtexture SV and RMS
FIG. 6 Variations of FN with Microtexture SV and RMS
- FN variations with RMS and SV following a similar trend- FN is proportional to microtexture SV- As SV increased, FN difference decreased
FIG. 6 Continued
Correlations between FN and texture
Friction Measurement
Texture Measurement
Macrotexture
MPD
Microtexture
MPD
Microtexture
SV
Wet Pavement FN 0.9722 0.6026 0.9159
Dry Pavement FN -0.4645 -0.9211 -0.6131
TABLE 8 Pearson Correlations between Friction and Texture
- FNwet has a positive relationship with all three variables.- Microtexture SV has an effect equivalent to macrotexture MPD, but greater than that by microtexture MPD.
- FNdry has a negative relationship with all three variables. - Microtexture affects FNdry more than macrotexture.
7. Conclusions
Microtexture testing - Texture profile is a series of waves passing the laser mounted on atest vehicle travelling at a specific speed.
- The requirements for lasers can bed defined by the frequency-velocity-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 differentpavements arose mainly in the large-scale waves
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 12.75 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 FNwet
- FNwet is more sensitive to microtexture SV than to microtexture MPD
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 uI 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 4 7 9 0 6
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 4 7 9 0 7