Correlation of Tire Wear and Friction to Texture of Concrete Pavements

Abstract

Pavement surface texture significantly contributes to tire wear and tire-pavement friction. Currently available relationships quantify tire wear and tire-pavement friction simply in terms of empirical pavement texture parameters. The objective of this paper is to present correlations with which the tire wear rate and tire-pavement friction on smooth concrete pavements can be predicted using actual texture properties. Using a laboratory tire wear simulator and an aircraft tread-rubber block, a number of wear and friction tests are performed on pavement samples having different fine aggregate sizes. Frequency characteristics of the texture of the pavements are achieved by decomposing the profilometer measurements using the fast Fourier transform technique and constructing power spectral density plots of texture over surface spatial frequency. Then the tire wear rates as well as dry friction and wet friction of the tire-pavement interface are correlated to microtexture and macrotexture components of the texture power spectral density. The developed correlations indicate that both tire wear and dry friction are significantly affected by pavement microtexture. The developed wear correlations can be also useful for predicting the wear index of a pavement based on conventional Mu-meter and grease patch test results. This is illustrated by an example in which the wear index for a concrete runway pavement at Luke Air Force Base, Ariz., is computed using the new relations.

Full text

46 / JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000
C
ORRELATION OF
T
IRE
W
EAR AND
F
RICTION TO
T
EXTURE OF
C
ONCRETE
P
AVEMENTS
By M. Gunaratne,
1
N. Bandara,
2
J. Medzorian,
3
M. Chawla,
4
and P. Ulrich
5
A
BSTRACT
:Pavement surface texture significantly contributes to tire wear and tire-pavement friction. Currently
available relationships quantify tire wear and tire-pavement friction simply in terms of empirical pavement
texture parameters. The objective of this paper is to present correlations with which the tire wear rate and tire-
pavement friction on smooth concrete pavements can be predicted using actual texture properties. Using a
laboratory tire wear simulator and an aircraft tread-rubber block, a number of wear and friction tests are per-
formed on pavement samples having different fine aggregate sizes. Frequency characteristics of the texture of
the pavements are achieved by decomposing the profilometer measurements using the fast Fourier transform
technique and constructing power spectral density plots of texture over surface spatial frequency. Then the tire
wear rates as well as dry friction and wet friction of the tire-pavement interface are correlated to microtexture
and macrotexture components of the texture power spectral density. The developed correlations indicate that
both tire wear and dry friction are significantly affected by pavement microtexture. The developed wear corre-
lations can be also useful for predicting the wear index of a pavement based on conventional Mu-meter and
grease patch test results. This is illustrated by an example in which the wear index for a concrete runway
pavement at Luke Air Force Base, Ariz., is computed using the new relations.
INTRODUCTION
Vehicle maneuvers such as braking and cornering require
sufficient skid resistance or tire-pavement friction to maintain
vehicle stability. On the other hand, these vehicle maneuvers
can also cause excessive tire wear. Hence, in designing a run-
way pavement in particular, it is essential to consider both the
level of skid resistance and the tire wear potential of the sur-
face course. Pavement texture plays a vital role in the devel-
opment of both pavement friction and tire wear. Therefore the
knowledge of the effect of texture parameters of friction and
tire wear will certainly assist pavement engineers in designing
pavements that improve tire life without compromising the all
important skid resistance.
Pavement texture can be grouped into two classes based on
ASTM E 867: (1) Pavement microtexture—the deviations of
a pavement surface with characteristic dimensions of wave-
length and amplitude <0.5 mm; and (2) pavement macrotex-
ture—the deviations of a pavement surface with characteristic
dimensions of wavelength and amplitude from 0.5 mm up to
a value that no longer affects tire-pavement interaction. Ob-
viously, the measurement of microtexture is more rigorous
than the measurement of macrotexture. However, using a very
sensitive profilometer, profile heights related to both microtex-
ture and macrotexture can be obtained.
Several standard pavement texture measuring methods are
available that can be broadly categorized into two groups: (1)
Direct measuring techniques such as the profilometer method;
and (2) indirect methods such as the sand patch method, the
grease patch method and the British pendulum test. The sand
patch and the grease patch method provide an indirect measure
of macrotexture by the volume of grease or sand required to
1
Assoc. Prof., Dept. of Civ. and Envir. Engrg., Univ. of South Florida,
Tampa, FL 33620.
2
Res. Asst., Dept. of Civ. and Envir. Engrg., Univ. of South Florida,
Tampa, FL.
3
Mech. Engr., Wright-Patterson Air Force Base, Dayton, OH 45433.
4
Deceased 1996, formerly, Mech. Engr., Wright-Patterson Air Force
Base, Dayton, OH.
5
Chf. Mech. Engr., Wright-Patterson Air Force Base, Dayton, OH.
Note. Associate Editor: Jan Olek. Discussion open until July 1, 2000.
To extend the closing date one month, a written request must be filed
with the ASCE Manager of Journals. The manuscript for this paper was
submitted for review and possible publication on July 7, 1997. This paper
is part of the Journal of Materials in Civil Engineering, Vol. 12, No. 1,
February, 2000. 䉷ASCE, ISSN 0899-1561/00/0001-0046–0054/$8.00 ⫹
$.50 per page. Paper No. 16150.
cover a known pavement area. Results of the tests are ex-
pressed using an empirical measure of the pavement macro-
texture called the mean texture depth (MTD). On the other
hand the British pendulum test provides an indirect measure
of microtexture. This tester is equipped with a standard rubber
slider that comes into a locked position when it is horizontal.
When released, the slider makes contact with the test surface
on its swing. A drag pointer is used to measure the swing in
terms of the British pendulum number (BPN). The greater the
friction between the slider and the test surface (due to the
microtexture), the more the swing is retarded and the larger
the BPN reading.
Previous Studies on Tire-Pavement Interaction
Many researchers have studied tire-pavement interaction in
the recent past and Kummer (1966) established a relationship
between friction components (adhesion and hysteresis) and
pavement texture properties. They related adhesion to molec-
ular texture and hysteresis to macrotexture properties. Another
set of experimental studies was carried out by Henry (1968)
to establish statistical relationships among skid numbers (SN)
at different speeds, BPN, and MTD of pavements. In these
relationships, the SN at zero speed is directly correlated to
microtexture properties, and the rate of change of the SN with
the speed is correlated to the macrotexture properties. Conse-
quently, SN at 63.36 km/h (40 mi/h) was expressed as
SN = k(BPN) ⫹k(MTD) ⫹k(1)
40 1 2 3
where k
1
,k
2
, and k
3
= constants.
Although scientific literature on tread wear versus pavement
texture is sparse, a significant contribution was made by
Lowne (1971). The tread wear of passenger car tires was mea-
sured on a series of test pavements at the Transportation and
Road Research Laboratory in the United Kingdom. These tests
showed that the microtexture was the dominant factor in de-
termining tread wear. Lowne (1971) showed that tread wear
Wwas given by a multiple regression equation of the form
W=⫺9.2 ⫹90(S)⫹18T(2)
50
where S
50
= wet cornering friction coefficient at 50 km/h for
a smooth no-tread pattern tire; and T= macrotexture depth
parameter (mm).
On the other hand, researchers have investigated highway
pavement roughness using spectral analysis. In this regard, an
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JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000 / 47
FIG. 2. Sample Pavement Surface
FIG. 1. SURTRONIX 3ⴙProfilometer
attempt by Marcondes et al. (1991) to develop better power
spectral density (PSD) models for the prediction of truck-bed
acceleration level in relation to transportation hazards such as
shocks and vibrations is significant. Profile height measure-
ment for this study was conducted using the Michigan De-
partment of Transportation profilometer. Profilometer readings
were taken at 7.5 cm (3 in.) intervals over 300.96 m (0.19 mi)
sections, and the pavement elevation PSDs were computed up
to a maximum spatial frequency of 2.36 cycles/m (Marcondes
et al. 1988).
EXPERIMENTAL PROGRAM
Measurement of Surface Texture
Because the pavement skid resistance and tire wear cannot
be described by the macrotexture properties alone, it was de-
cided to use the profilometer tracing technique to measure both
microtexture and macrotexture properties. The SURTRONIX
3⫹profilometer (Fig. 1) manufactured by Rank Taylor Hob-
son Inc., Des Plaines, Ill., was selected for profile measure-
ments. The relevant specifications for SURTRONIX 3⫹are
indicated below:
• Horizontal resolution = 1 ␮m (40 ␮in.)
• Vertical resolution = 0.001 ␮m (0.04 ␮in.)
• Traverse length = 25.4 mm (1 in.)
In addition, the grease patch test was also used in this ex-
perimental program to compare its results with macrotexture
portions of profilometer measurements.
Friction/Wear Testing Machine
The essence of this experimental program was the measure-
ment of the friction coefficient and tire wear rates on different
pavement sections. Because these measurements had to be car-
ried out at specific speeds, a machine with a belt-driven ro-
tating disk providing both horizontal as well as vertical load
measuring mechanisms was developed to achieve this objec-
tive. This 55-cm-diameter disk is furnished with a 5-cm-wide
ring to form concrete pavement samples to a depth of 5 cm
(Fig. 2). The disk can be spun around a vertical axis using a
speed reduction pulley arrangement. The spinning motion is
smoothened by fixing the disk to a trailer hub. The pulley
arrangement consists of two multiradii pulleys with diameters
of 5, 7.5, 10, and 12.5 cm. While one pulley is driven by the
motor, the disk can be rotated at a specified speed by using
the other pulley as an idle wheel. The above described ar-
rangement is clearly shown in Figs. 2 and 3.
Preparation of Pavement Surfaces
Although the pavement strength is not a governing factor
in this testing program, test pavement surfaces were designed
according to the following American Concrete Institute (ACI)
specification (ACI 211.1-91):
• Compressive strength = 20 MPa
• Slump = 75–25 mm
• Water-to-cement ratio = 0.62
The weight fraction of each component in the mixture is as
follows:
• Cement = 0.14
• Course aggregate = 0.43
• Fine aggregate = 0.35
• Water = 0.08
To obtain different texture characteristics, the particle size
of the fine aggregate was varied while keeping the other at-
tributes the same. Selected particle sizes and labels assigned
for pavement surfaces are as follows:
• Pave 40—Fine aggregates passing 1.18 mm sieve and
retained on 0.425 mm sieve
• Pave 60—Fine aggregates passing 0.425 mm sieve and
retained on 0.25 mm sieve
• Pave 100—Fine aggregates passing 0.25 mm sieve and
retained on 0.15 mm sieve
• Pave 200—Fine aggregates passing 0.15 mm sieve and
retained on 0.075 mm sieve
Then, profile measurements were performed on the above
sample pavement surfaces over a traverse length of 25 mm.
Measurement of Tread-Rubber Wear
The wear rate can be measured either as a weight loss or a
change of volume in the tread rubber. Of several distinct wear
criteria proposed by previous researchers (Moore 1972), the
gravimetric wear rate K
W
, was employed in this research
⌬W
K= (3)
W
LA
a
where ⌬W= weight loss of the rubber block; and Land A
a
=
distance slid along the pavement surface and the apparent area
of the rubber block, respectively.
Friction Measurements
During each wear test the dry rubber/concrete friction force
was measured using the load cell arrangement shown in Fig.
4. After each wear test, the wet rubber/concrete friction was
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48 / JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000
FIG. 3. Friction/Wear Testing
FIG. 4. Horizontal and Vertical Load Measuring Load Cells
also measured by flooding the pavement using a steady water
supply applied in front of the rubber block. The sequence of
texture, friction, and rubber tread wear measurements for each
pavement is illustrated in Fig. 5.
ANALYSIS OF TEST RESULTS
Analysis of Profile Measurements
Spectral analysis techniques such as the fast Fourier trans-
form (FFT) technique are commonly adopted to analyze pave-
ment profiles because their statistical characteristics resemble
those of random signals. If z(x) is the surface profile height
expressed as a function of longitudinal distance x, the corre-
sponding finite-length Fourier transform can be written
L
Z(k)= z(x)exp(⫺ikx)dx (4)
冕
0
where k=2␲f
m
and f
m
= spatial frequency components of the
surface roughness. It can be assumed that the surface profile
data set consists of Nvalues of z(x) that are measured at
equally spaced intervals ⌬xover a total length of L(=N⌬x).
If these discrete height data are adjusted to have a zero mean
value denoted by z(n), then the mean square roughness ␤
2
is
given by
N⫺1
1
22
␤=z(n) (5)
冘
N
n=0
The highest surface spatial frequency resolved in the mea-
surement process, which is the Nyquist frequency, is f
c
=
(2⌬x)
⫺1
. Hence, it can be seen that the measurement interval
⌬xlimits the bandwidth of frequencies.
Using standard FFT methods, the digital equivalent of (3)
yields the Fourier transform Z(m)as
ˆ
Z(m)=⌬xZ(m), ⫺N/2 ⱕmⱕN/2 (6)
where
N⫺1
ˆ
Z(m)= z(n)exp(⫺2␲imn/N), ⫺N/2 ⱕmⱕN/2 (7)
冘
n=0
Then, a plot of the real component of will clearly
ˆ
Z(m)
exhibit the wavelength (or frequency) spectrum of the surface
profile.
On the other hand, the PSD of the profile can be expressed
as
2
兩Z(k)兩
PSD = (8)
L
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JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000 / 49
FIG. 5. Texture,Wear, and Friction Testing Methodology
The digital equivalent of (7) can be written
2
N⫺1
P(m)= z(n)exp(⫺2␲imn/N) (9)
N
冏冘 冏
n=0
Finally, (9) can be simplified to
⌬xˆˆ
P(m)= (Z(m)⫻(Z(m))*) (10)
N
N
where = complex conjugate of It is seen that
ˆˆ
(Z(m))* Z(m).
P
N
(m) is a combination of both real and imaginary components
of and it thus contains more information on the fre-
ˆ
Z(m),
quency spectrum than alone.
ˆ
Z(m)
Furthermore, one can integrate the PSD over the Nyquist
bandwidth limits of surface spatial frequency to obtain the area
under the PSD curve. Parseval’s theorem (Gunaratne and Ban-
dara 1997) states that this area is equal to the mean-square
roughness ␤
2
of the pavement. Therefore it can be seen that
the PSD plot of a surface profile can be employed to describe
both frequency and roughness characteristics in terms of the
spatial frequencies and the area under the plot, respectively.
The other conventional texture parameters are the arithmetic
mean of the departures of the profile from the mean line R
a
and root-mean-square parameter (RMS) expressed, respec-
tively, by
L
1
R=兩z(n)兩dx (11)
a
冕
L
0
L
1
2
RMS = (12)
z(n)dx
冑冕
L
0
As the horizontal resolution of the SURTRONIX 3⫹pro-
filometer is 1 ␮m, each sample profile consists of 25,000 data
points.
Then, the FFT technique was used to obtain the PSD of
profiles. This task was facilitated by a MATLAB program that
is also capable of generating PSD plots and determining other
associated parameters using FFT algorithms.
Fig. 6 shows a sample profile plot obtained from one pave-
ment sample (Pave 60). Because the horizontal resolution of
the profile measurement is 1 ␮m in this testing program, the
Nyquist frequency, which is equal to (1/2⌬x)
⫺1
, is as high as
0.5 cycles/␮m. The FFT versus spatial frequency plot and the
corresponding PSD plot for Pave 60 are given in Figs. 7 and
8, respectively.
In Figs. 7 and 8, it is clear that the profile is composed of
several sinusoidal waves with different frequencies (wave-
lengths) of which only a few are predominant. Furthermore,
the plots can also be used to differentiate the pavement mi-
crotexture and macrotexture at the demarcation wavelength of
0.5 mm, which corresponds to a frequency of 2 cycles/mm.
Then, using the microtexture/macrotexture demarcation, the
roughness contributions from pavement microtexture and
macrotexture can be easily deduced from the respective areas
under the PSD plot.
To account for the randomness along the given test pave-
ment sections, two locations were selected on each test pave-
ment, and two profile measurements were taken at each lo-
cation. Thus, an average PSD curve was obtained for each test
pavement based on all four profiles.
Roughness Correlations
By performing regression analysis on the data, the following
relation was observed between the MTD and the macrotexture:
Macro(PSD) = ⫺69.78 = 11,058 ⫻MTD (13)
(⫺0.39) (8.45)
where Macro(PSD) = area under the macrotexture portion of
the PSD curve (␮m
2
). Values in the parentheses give the t
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50 / JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000
FIG. 6. Surface Profile for Pave 60
FIG. 7. FFT versus Spatial Frequency Plot (Pave 60)
statistics for the individual variables, and the Fstatistic for the
complete model is 71.41 with R
2
being 0.71. Fig. 9 shows the
predicted and observed area under the macrotexture portion of
the PSD curve versus MTD.
Correlation of Tread-Rubber Pavement Friction to
Pavement Texture Properties
The measured dry and wet friction coefficients on different
pavement surfaces were also correlated to the corresponding
pavement texture properties. These dry and wet friction coef-
ficients were determined by averaging the time history of the
friction coefficients obtained in each 7.92 km/h (5 mi/h) test.
The two mechanisms in which tire-pavement friction is gen-
erated are adhesion and hysteresis. The adhesion component
of the friction depends on the pavement microtexture, whereas
the hysteresis component primarily depends on pavement
macrotexture. To test this dependency, multiple regression
models were developed for wet and dry friction coefficients in
terms of texture properties. The developed models can be de-
scribed as follows:
␮= 1.015 ⫹0.251 Log(Micro) ⫺0.302 Log(Macro) (14)
wet
(33.36) (8.93) (27.36)
where ␮
wet
= friction coefficient at wet condition at 7.92
km/h (5 mi/h); Micro = area under the microtexture portion
of the average PSD curve (␮m
2
); and Macro = area under the
macrotexture portion of the average PSD curve (␮m
2
). The
values in the parentheses are Fstatistics for the individual
variables, and the Fstatistic for the model is 14.02 with R
2
being 0.609. The model can be simplified as shown below
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JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000 / 51
FIG. 8. PSD Plot (Pave 60)
FIG. 9. Predicted and Observed Area under MacrotexturePor-
tion of PSD Curve versus MTD FIG. 10. Plot of Predicted ␮
dry
versus Observed ␮
dry
FIG. 11. Plot of Predicted ␮
wet
versus Observed ␮
wet
0.251
10.35(Micro)
␮= Log (15)
wet
再冎
0.302
(Macro)
Also, for dry friction
␮= 2.618 ⫹0.481 Log(Micro) ⫺0.701 Log(Macro) (16)
dry
(38.75) (4.44) (23.74)
where ␮
dry
= friction coefficient at dry condition at 7.92
km/h (5 mi/h). The values in the parentheses are Fstatistics
for the individual variables, and the Fstatistic for the model
is 11.97 with R
2
being 0.52. The model can be simplified as
shown below
0.481
414.96(Micro)
␮= Log (17)
dry
再冎
0.701
(Macro)
As expected, (15) and (17) clearly show the stronger de-
pendency of the dry friction coefficient on pavement micro-
texture compared to the wet friction coefficient. This is be-
cause dry friction is mostly based on adhesion, which is
governed by microtexture. Figs. 10 and 11 show the plot of
predicted versus observed values for dry and wet friction co-
efficients, respectively.
Variation of Tread-Rubber/Pavement Friction
with Speed
The variation of tread-rubber/pavement friction with speed
was also analyzed in this experimental program. The drytread-
rubber/pavement friction was measured at three different
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52 / JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000
FIG. 12. Variation of Friction Coefficient with Speed for Sam-
ple Pavements
FIG. 15. Plot of Predicted ␮
dry
versus Observed ␮
dry
for New
Pavement
FIG. 14. Comparison of R
a
/RMS Values of New Pavement with
Test Pavements
FIG. 13. Plot of Predicted K
W
versus Observed K
W
speeds: 7.92 km/h (5 mi/h), 15.84 km/h (10 mi/h), and 23.76
km/h (15 mi/h). It is a well-known fact that the friction co-
efficient decreases with increasing speed. A similar trend is
seen in the current testing program as well. Fig. 12 shows the
variation of dry friction coefficient with speed for the four
sample pavements.
Correlation of Tread-Rubber Wear Rates to Pavement
Texture Properties
Another objective of this research was to develop a corre-
lation between tread-rubber wear rates and pavement texture
properties. Extremely high wear rates were observed on newly
constructed pavements as new pavements are abrasive. Hence,
these initial observations for each pavement were discarded,
and the analysis was performed only on subsequent data. The
following regression equation was obtained:
Log(K)=⫺0.959 ⫹0.365 Log(Micro) ⫺0.486 Log(Macro)
W
(15.93) (10.89) (39.81)
(18)
where K
w
= gravimetric wear rate at 7.92 km/h (5 mi/h); Micro
= area under the microtexture portion of the PSD curve (␮m
2
);
and Macro = area under the macrotexture portion of the PSD
curve (␮m
2
). R
2
and the Fstatistic for the model are 0.72 and
20.29, respectively. The model can be rewritten
0.365
(Micro)
K= (19)
W0.486
69.375(Macro)
As seen in (19), the wear rate is directly proportional to the
microtexture and inversely proportional to the macrotexture.
The microtexture is caused by the roughness of the individual
aggregate particles, whereas the assembly of asperities in a
given surface form the macrotexture. Hence, when the macro-
texture increases on a given pavement, it reduces the contact
between the tire and individual aggregate particles, thus re-
ducing the wear. On the other hand, when microtexture in-
creases, the surface becomes rougher owing to the increased
contact between the tire and individual particles thus increas-
ing tire wear. Both these phenomena are amply illustrated by
(19). Fig. 13 shows the variation of predicted K
W
versus ob-
served K
W
.
VERIFICATION OF DEVELOPED RELATIONS
To verify the above results, another set of similar experi-
ments was conducted on a pavement with texture properties
different from those used for formulating the relations. This
pavement was constructed using a mixture of fine aggregates
employed for the preparation of the previous test pavements.
Hence the roughness of the new pavement was within the
range of roughness encompassed by the previously tested
pavements. This is clearly seen by the R
a
/RMS values of the
new pavement (Fig. 14).
Using developed models, dry and wet friction coefficients
and wear rates were determined. Figs. 15–17 show the re-
spective plots of the observed values of dry friction, wet fric-
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JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000 / 53
FIG. 16. Plot of Predicted ␮
wet
versus Observed ␮
wet
for New
Pavement
FIG. 17. Plot of Predicted K
W
versus Observed K
W
for New
Pavement
TABLE 1. Measured Friction and Texture Characteristics of
Luke Air Force Base Runway 03R/21L
Section
(1)
Length from
21L end
(m)
(2)
␮
wet
(63.36 km/h)
(3)
␮
wet
(95.04 km/h)
(4)
MTD
(mm)
(5)
A 300–600 0.40 0.36 0.308
B 600–1,200 0.35 0.26 0.285
C 2,400–2,700 0.37 0.26 0.272
TABLE 2. Estimated Wet FrictionCoefficients at 7.92 km/h
Section
(1) ␮
wet
at 7.92 km/h
(2)
A 0.481
B 0.589
C 0.686
TABLE 3. Estimated Wear Rates for Different Pavement Sec-
tions
Section
(1)
Area under
macrotexture portion
of PSD curve
(␮m
2
)
(2)
Area under
microtexture portion
of PSD curve
(␮m
2
)
(3)
Wear rate
K
W
(g/cm
2
)
(4)
A 3,336.08 129.29 0.1648
B 3,081.75 316.05 0.2368
C 2,937.99 727.99 0.3296
tion, and wear rate against those predicted by (15), (17) and
(19).
These plots show that (for smooth concrete pavements
within the tested roughness range) friction and wear can be
predicted to a reasonable accuracy using the developed rela-
tions.
APPLICATION
Finally, the developed relations were used along with avail-
able Mu-meter readings and grease patch measurements at
Luke Air Force Base, Ariz., to predict the wear rate of a run-
way. LukeAir Force Base possesses two runways: (1) Runway
03L/21R consisting of an asphalt surface; and (2) runway 03R/
21L consisting of a concrete surface. Prediction of the wear
rate is possible only for runway 03R/21L, because the above
developed models are applicable for concrete pavements.
Wet friction coefficients had been measured on both run-
ways in an earlier study (HQ AFCESA 1992). Each runway
is divided into three sections, and the friction measurements
for each section are provided in Table 1.
To obtain the corresponding texture parameters for this run-
way from (15), one needs to extrapolate the ␮value at 7.92
km/h (5 mi/h) from those at 63.36 km/h (40 mi/h) and 95.04
km/h (60 mi/h) (Table 1). The following equation, which ex-
presses the variation of wet friction with speed (Leu and Henry
1978), can be used for this purpose:
PNG
SN = SN exp ⫺V(20)
0
冋冉 冊册
100
where SN
0
= skid number (or ␮⫻100) at zero speed; PNG
= percent normalized gradient of the SN versus speed Vcurve;
and SN = skid number at speed V. Table 2 shows the accord-
ingly estimated wet friction coefficients at 7.92 km/h (5 mi/h)
for above pavement sections. Then, the ␮values in Table 2,
MTD values in Table 1, and the values of (13) and (15) can
be used to determine the respective areas under the macrotex-
ture portion and microtexture portion of the PSD curves, for
pavement sections A, B, and C. These results are shown in
Table 3. Finally, the above-estimated texture parameters can
be used to predict the wear rates K
w
of the above pavement
sections using (19), as indicated in Table 3.
CONCLUSIONS
This research study was initiated with the goal of establish-
ing reliable friction and tire wear prediction methods based on
actual pavement texture properties. A state-of-the-art electro-
mechanical profilometer was used to record the pavement pro-
file heights up to an accuracy of 0.01 ␮m ensuring that the
measured profile data incorporates both microtexture and ma-
crotexture properties of the pavement surfaces. The FFT tech-
nique was used for the decomposition of pavement profiles
into individual frequency components and the subsequent con-
struction of the PSD plots. The PSD plots are used to obtain
the pavement texture properties related to microtexture and
macrotexture.
A laboratory tire wear simulator was developed to measure
tire wear as well as dry and wet friction levels on a number
of pavement samples with different fine aggregate sizes. Dif-
ferent texture characteristics could be produced on the pave-
ment surface by changing the particle size of the fine aggregate
portion. This simulator also facilitates the performance of
rubber wear tests and rubber-pavement friction measurements
under a range of normal loads and speeds.
Reasonably good correlations were obtained between the
tire-pavement friction and profile texture parameters such as
the macrotexture and microtexture portions of the PSD plot.
J. Mater. Civ. Eng. 2000.12:46-54.
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54 / JOURNAL OF MATERIALS IN CIVIL ENGINEERING / FEBRUARY 2000
Remarkably, these relationships tend to explain the respective
roles of adhesion and hysteresis components of tire-pavement
friction. A strong correlation was also obtained between tire
wear rates and macrotexture and microtexture portions of
pavement roughness. Tire wear rates are seen to be directly
proportional to the microtexture and inversely proportional to
the macrotexture. On the other hand, neither the tire wear nor
the tire-pavement friction shows a significant correlation to the
currently used texture parameter of MTD. Hence these results
indicate the importance of pavement texture profile parameters
on the development of more meaningful tire wear and tire-
pavement friction relationships. Moreover, the developed re-
lationships will be useful in the design of pavements with ac-
ceptable tire wear rates with a sufficient level of skid
resistance. This can be achieved by selecting suitable mix de-
signs that provide the optimum combination of microtexture
and macrotexture.
Furthermore, the newly developed correlations can also be
used to predict the tire wear rate for a given pavement based
on regular Mu-meter measurements and grease patch test data.
This is clearly demonstrated by predicting the tire wear rates
for different sections of a concrete runway at Luke Air Force
Base.
ACKNOWLEDGMENT
The writers wish to acknowledge the technical assistance of Dr. Gray
Mullins, Assistant Professor of Civil Engineering, University of South
Florida, Tampa, in assembling the tire-wear simulator.
APPENDIX. REFERENCES
Gunaratne, M., and Bandara, N. (1997). ‘‘Correlations of pavement tex-
ture characteristics to tire wear and tire-pavement friction.’’ Final Rep.
Submitted to Wright-Patterson AFB, Dayton, Ohio.
Henry, J. J. (1968). ‘‘Tire wet-pavement traction measurement:A state-of-
the-art review.’’ The tire pavement interface, ASTM STP 929,M.G.
Pottinger and T. J. Yager, eds., ASTM, West Conshohocken, Pa., 3–25.
HQ AFCESA. (1992). ‘‘Runway friction characteristics evaluation, Luke
AFB, AZ.’’ Rep. No. DSN 523-6429, Headquarters Air Force Civil
Engineering Support Agency, Pavement Surface Effects Team, Tyndale
Air Force Base, Fla.
Kummer, H. W. (1966). ‘‘Unified theory of rubber friction.’’ Engrg. Res.
Bull. B-94, Penn State University, State College, University Park, Pa.
Leu, S. J., and Henry, J. J. (1978). ‘‘Prediction of skid resistance as a
function of speed from pavement texture.’’ Transp. Res. Rec. 666,
Transportation Research Board, Washington, D.C., 38–43.
Lowne, R. W. (1971). ‘‘Effect of road surface texture on tire wear.’’
Rubber Chem. and Technol., 44(5), 1159–1160.
Marcondes, J., et al. (1991). ‘‘Spectral analysis of highway pavement
roughness.’’ J. Transp. Engrg., ASCE, 117(5), 540–549.
Moore, D. (1972). The friction and lubrication of elastomers. Pergamon,
New York.
J. Mater. Civ. Eng. 2000.12:46-54.
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