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