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Definition and measurement of pavement surface roughness
127
Wear, 57 (1979) 127 - 136 @ Elsevier Sequoia S.A., Lausanne - Printed in the Netherlands
DEFINITION AND MEASUREMENT ROUGHNESS*
OF PAVEMENT
SURFACE
RUDOLPH R. HEGMON Research Mechanical Engineer, Office of Research, HRS-1.2, Federal Highway Administration, Washington, D.C. 20590 (U.S.A.) (Received June 11,1979)
Summary Pavement roughness is divided into three scales; roughness, macrotexture and microtexture. All three are surface irregularities of random nature. The dividing lines between them are based on functional considerations such as traffic safety and ride quality. Roughness is the largest scale with characteristic wavelengths of 0.1 - 100 m and amplitudes of 1.0 100 mm. The two texture scales have wavelengths and amplitudes of the same order of magnitude covering a range of 0.01 - 10 mm with a dividing line at about 0.25 mm; their function is generally related to pavement-tire traction characteristics. The two texture scales, their functional significance and methods of measurement and analysis are discussed. It is shown that current technology does not meet the needs of the highway engineer. Some details are given about measurement methods under development which have the potential to be operated from a vehicle moving at normal travel speeds.
1. Introduction The term “engineering surfaces” can readily be interpreted to include pavements. In fact a great deal of structural and material engineering goes into the design of pavements and pavement surfaces must meet a number of conflicting requirements. A pavement surface is far from smooth and the various scales of roughness, their causes and methods of measuring them will be discussed. Generally pavement roughness is divided into three scales: roughness, texture and microtexture. All three are surface irregularities of random nature. The dividing lines between them are based on functional considerations such as traffic safety and ride quality. Roughness is the largest scale *Presented at the International Conference Engineering Surfaces, Leicester, April 18 - 20,1979.
on Metrology and Properties of
128
with characteristic wavelengths of 0.1 - 100 m and amplitudes of 1.0 100 mm and affects primarily vehicle dynamics. The two texture scales have wavelengths and amplitudes of the same order of magnitude covering a range of 0.01 - 10 mm with a dividing line at about 0.25 mm; their function is generally related to pavement-tire traction characteristics. A number of texture measurement methods are available, none of which are very satisfactory because of the large dynamic range. All these methods can be classified into two groups. One group of relatively simple measurements provides an average value over an area several times larger than the largest texture dimension and requires only limited data processing. The second group of measurements provides profile records which are amenable to analysis by standard statistical methods with some adaptation to the specific needs of traffic safety. Current research is directed toward high speed non-contacting measurements which can be performed from a vehicle moving at normal traffic speed. In addition a measure of texture can be obtained indirectly through friction measurements. Analysis of texture data ranges from simple mean values to more comprehensive statistical methods which are designed to give a full description of pavement surfaces with a minimum of parameters.
2. Scales of pavement
surface roughness
Roughness of pavement surfaces covers an enormous dynamic range. Expressed in terms of wavelength and amplitudes the ranges are from about 1O-2 to lo6 mm and from 10V2 to lo2 mm respectively. These ranges are subdivided into three scales, each covering a different function in terms of pavement performance. The dividing lines are necessarily somewhat arbitrary because of the gradual transition between the roughness effects. The largest scale is termed road roughness (or waviness) with wavelength and corresponding amplitudes as characteristic dimensions. Road roughness primarily affects ride quality and dynamic pavement loads. The range of wavelengths is about 0.1 - 100 m and the range of amplitudes is 1.0 - 100 mm. Normally the larger amplitudes are associated with the longer wavelengths. Road roughness is discussed elsewhere [1] and will not be treated here. The remaining two scales are termed microtexture and macrotexture: the former spans a scale of 0.01 - 0.25 mm, the latter a scale of 0.25 10 mm. In the case of texture no distinction is made between amplitude and wavelengths because both are of the same order of magnitude. Thus macrotexture overlaps the road roughness amplitudes, but in terms of the wavelength there is a gap (minimum wavelength about 100 mm, maximum macrotexture about 10 mm). Pavement irregularities of wavelengths between 10 and 100 m are rarely of a recurrent nature but occur mostly as singularities such as cracks and joints. The exception is an artifically grooved pavement in which transverse grooves may be spaced about 20 mm or more apart.
129
3.
Significance of pavement texture
Whereas roughness is undesirable and detrimental to a good road, pavement texture is essential. A pavement ~thout texture would be very unsafe under wet conditions. In the United States pavements are wet about 10% of the time and in Great Britain the percentage of time they are wet is probably greater. Therefore in designing new pavement or in resurfacing old ones the highway engineer will provide adequate texture. At present texture is not specified explicitly but is provided implicitly by specifying the construction method and the materials to be used in the pavement surface. Macrotexture is provided through the construction process. Basically there are two pavement types, flexible and rigid. Flexible pavements are constructed with bituminous mixes. The mix design and the gradation of the aggregate used together with the degree of compaction determine the magnitude of the macrotexture. In rigid (concrete) pavements macrotexture is provided by the finishing process of the pavement surface. This is done with various tools just before the concrete hardens. Microtexture is primarily a materials property. The aggregate used in the top layers of bituminous pavements is selected for its suitability to provide and retain microtexture under continued exposure to the polishing+ action of traffic. In concrete pavements microtexture is provided through the inclusion of fine hard particles in the concrete mix, Macrotexture together with tire treads relieves the hydrodynamic pressure generated between the tire and the pavement surface. The water can escape through the channels formed by texture and tire tread. The tie available for water removal is short, of the order of a few milliseconds, and decreases with speed. Thus macrotexture becomes more important at higher driving speeds. However, even with the best macrotexture a thin water film will always adhere to the surface. The function of microtexture is to pierce this water film and provide direct tire-pavement contact at as many points as possible. Thus the right combination of microtexture and macrotexture is essential for safe driving during rain and until the pavement is dry.
4. Measurement of pavement texture For a texture measurement to be valid and useful to the highway engineer it must meet several requirements. It should be easy to use and interfere as little as possible with traffic. It must obviously also be representative of the pavement surface on which it is taken. This last requirement is easier stated than met since only small samples can often be measured for several miles of road. Measurement methods in use today can be classified as indirect, averaging and profiling. The indirect methods are based on the different functions of microtexture and macrotexture. Both textures help to provide tirepavement contact under wet conditions and thus provide the friction forces
130
necessary for safe driving. A friction measurement can therefore be used as an indirect measure of pavement texture. Analysis of experimental data has shown that low speed friction correlates with microtexture while macrotexture correlates with friction-speed gradients at higher speeds [ 2, 31. Friction measurements are relatively easy to perform; the major drawback is poor repeatability. Averaging methods provide a texture value over an area large enough to be considered representative of the road surface. One method uses a known volume of material which can easily be spread over the surface [4]. It must be fine enough to fill the cavities in the surface. The area covered by the material is measured and the mean depth is given by the ratio of volume to area. A number of variations on this method are in use. Another averaging method uses a known volume of water which is allowed to drain between a rubber seal and the pavement surface [ 51. Clearly the coarser the surface the faster the water will escape. Thus the outflow time is a measure of texture. These measurements are crude and can be made within a few minutes. For increased confidence in the results a number of replicate measurements must be made along the road. Improved averaging methods are being developed, but their ultimate utility is still in doubt. One such method is based on the principle of light depolarization [6] . Linearly polarized light is directed toward the pavement and the reflected light is received by a photodiode (Fig. 1). The light source and receiver are arranged in specular geometry, i.e. at equal angles to the vertical. The light reflected from the randomly textured pavement is no longer linearly polarized. In other words the alignment of the electric field vector, which was unidirectional for the incident light, was changed after reflection to trace out an ellipse in space. The degree of ellipticity is a function of pavement texture and is measured by the ratio of the minor to the major axes of the ellipse. By using the ratio instead of absolute values the effect of varying reflectivity is eliminated, at least in theory. Such a depolarization system has been mounted on a car and used at regular travel speeds. Repeatability is excellent and the results are independent of speed. Correlation of this
Fig. 1. Optical
diagram
of the depolarization
system
for texture
measurement.
131
method with other texture measurements is close to 0.8 and work is now under way to improve this. The primary effort is toward the use of narrowband light. We expect that a wavelength can be used which will improve the correlation. Another averaging method which has shown some promise as measure of macrotexture is the level of tire noise. The fact that tire noise varies with texture has been es~b~shed by a number of researchers [ 7,8]. It was therefore proposed that analysis of the noise of a standard tire could be a unique descriptor of pavement texture. Experiments have been conducted on a number of pavements with different macrotextures {93 . Spectral analysis of the noise records showed that above 1600 Hz the sound pressure level becomes a function of texture with a total difference of about 10 dB for the eight pavements tested (Fig, 2). This sensitivity is not adequate and efforts to achieve better resolution will probably be made as part of ongoing research. The method is attractive because it is passive, Le. no energy need be transmitted to the pavement. Tire inflation pressure and speed will have to be controlled very closely.
FREOUENCY.
kHz
Fig. 2. Tire noise spectra obtained for eight surfaces at 66 km h-l.
Indirect and averaging methods are relatively simple because texture is described by a single number. This may be adequate if only values relative to other pavements or changes over time are to be established. For a full description more than a single number is required. Detailed information on surfaces can be obtained by microscope methods which have been reviewed briefly by Thomas [lo]. However, these are at best laboratory methods and not very useful to the highway engineer. A somewhat related method was developed by Schonfeld [ll].It uses s~~ophoto~aphs, which can be taken with relative ease on the pavement, which are then analyzed under appropriate magnification. None of the measurement methods reviewed so far have the potential of solving the ~x~re-me~urement problem for the highway engineer. For this reason most of our effort goes into the development of profiling methods. These have the advantage of producing an analog electrical signal
132
which can be processed by standard analysis methods. Mechanical profile tracers have been used successfully. For macrotexture profiling a relatively simple home-made instrument was used with a stylus radius of about 0.125 mm. A Gould Surface Analyzer was adapted for microtexture profiling. The total range covered by these two instruments is from 5 pm to over 50 mm (Fig. 3) with the two traces overlapping as shown in the figure. A non-contacting tracer (AUTECH Corporation Laser Gauge) was also used. The traverse speed was about 1.25 mm s-l. A typical record is shown in Fig. 4. Signal blackouts may occur such as the spike in the upper trace. Before computer processing of the signal, which was digitized at a rate corresponding to about 6 pm, such spikes were removed (center trace) by a special editing program. The resulting power spectral density (PSD) plot (lower trace) shows a resolution limit of over 50 000 cycles m-l. However, in calibrating the system on a sinusoidal surface wavelengths of 1 mm were distorted. Therefore the practical upper limit of this instrument in its present configuration is not better than 1000 cycles m-l.
Fig. 3. PSD plot of microtexture,
macrotexture
and a combination
of both.
The mechanical profile traces are satisfactory and provided a good basis for texture analysis in terms of interest to the highway engineer. The success of this initial work prompted us to look for non-contacting profiling methods to be used from a vehicle traveling at speeds of 60 - 80 km h-’ . Initial feasibility studies have identified the problems and have resulted in a concept which is now being developed [ 121. Many problems have to be overcome. At regular travel speeds the measuring system moves at about 20 000 mm s-l in the direction of travel. Recording of texture of characteristic dimensions of 1 mm or less at this speed would require a bandwidth of about 100 kHz which is beyond the range of most frequency modulated recorders even at the highest tape speeds. Digital recording speeds also fall short of the requirements although analog-to-digital conversion rates would be adequate. Measurement of microtexture, which is smaller by a factor of 100, will clearly have to be static.
133
Measurement from a moving system, even at low travel speeds, appears to be beyond the current state of the art. Another problem is vehicle bounce. A smooth riding car may bounce at about 1 Hz with peak-to-peak amplitudes of say 10 mm. Therefore any optical system, including lasers, would be out of focus most of the time. In addition the bounce effect would have to be removed from the measurement so as to separate vehicle motion from texture. This could probably be done in the data processing by using an acceleration measurement to determine bounce frequency and magnitude. This method is used successfully in roughness measurements in which, however, the required resolution is far less than for texture. A promising method now under development uses a series of flash images instead of a continuous recording. The principle is shown in Fig. 5. The light source is a stroboscope and the flash is projected through the optical system into a fan-shaped thin beam of pulsed light. This beam illuminates the surface at an angle and produces a shadow picture of the texture which is recorded by a Vidicon television camera. Thus the profile is available as digital recording for further processing. Each individual profile is about 100 mm long and samples can be taken at a rate of up to ten per second or every 2 m. Such a high sampling rate may not be necessary. The pulse duration is about 5 ps, during which time the system moves about 0.1 mm. Thus some smear will occur and we may have to compromise on the speed unless the pulse duration can be further shortened.
Fig. 4. PSD plot of pavement texture profile recorded by laser. Fig. 5. Schematic diagram of texture-recording
system.
134
5. Analysis and correlation
of texture
data
Texture data have to be related to pavement performance to be of use to the highway engineer. Thus if profile records are used instead of averaging methods they must be reduced to meaningful measures. A computer program has been written to compute the power spectrum from digitized texture profiles and to correlate this with pavement friction properties. Let the pavement profile be represented by the sequence (s,,) where n, denotes the discrete value of the corresponding continuous variable s at the nth sampling interval. The total number of samples is N. The sequence is high-pass filtered at any desired lower frequency to remove long wavelengths and also low frequency vehicle motions (in the case of measurements from a moving vehicle). Let the filtered sequence be (r,) and let it be partitioned into K sections of equal length L. These K new sequences are weighted by a function w(Z) which results in a new sequence {zk, 11= { wlrl+(k-l)LI Transforming accomplished
l
l
each of the K sequences into the frequency by the following operation: zk,
n
exp{--j( 2n/L)(n - 1)O
The computer implementation uses the fast Fourier spectral density is then given by
domain
is
l
The power
where JI (I) is a shorthand notation for $ (fi), fi = Z/L and 2; (I) is the complex conjugate of Z,(Z). Each power spectral density function consists of L/2 + 1 independent spectral estimates centered at frequency fi with a bandwidth defined by the original sampling rate for the s, and the selected value OfL. The relation between spectral density estimates and the corresponding frictional properties can be determined by a matrix analysis:
where c is a matrix of frictional properties for p pavements and at q speeds, g is the spectral density matrix for the p pavement surfaces divided into convenient bands from f, to f, and W is a matrix of weighting factors assigned to each frequency band and testing speed. The solution is given by w = (\k -- % )-Wr-_ Correlations were computed using the skid number SN defined as 100 times the coefficient of friction and the skid number-speed gradient dSN/dV.
135
Because of the relatively low correlations obtained, the concept of the percentage normalized gradient PNG which has been previously shown to correlate better with macrotexture [ 131 was used. It is defined as PNG =
100 dSN SN, dV
where SN, is the level of friction and is primarily a function of microtexture. Microtexture raises the level of friction at low speeds but its effect diminishes at higher speeds and therefore steeper gradients are obtained. The correlations were computed and found to be above 0.95. Finally, in order to evaluate the performance of the simple averaging method r.m.s. values were computed from the profiles and correlated with texture depth computed from the volumetric measurement as
where V is the known volume and the Di are the measured diameters. The correlations were better than 0.9 which was gratifying considering the crudeness of this method.
6. Summarizing
remarks
Texture is a necessary feature of pavement surfaces and makes pavements safe for driving under wet conditions. Pavement texture is subdivided into macrotexture and microtexture. The former helps to drain the tirepavement contact area while the latter penetrates the remaining water film to establish semi-dry contact. Pavement texture measurements have been made for many years using relatively crude averaging methods. These are useful and automated versions such as the depolarization method are being developed. Low speed profiling methods have been used successfully for detailed characterization of texture. The data have been analyzed by standard techniques and the results have demonstrated that pavement friction properties are highly correlated with texture. Thus texture parameters which can be obtained in many ways or can be synthesized in the laboratory can be used to predict the frictional properties of pavements. Development of a high speed profiling method is in progress. References 1 J. C. Wambold, The measurement and data analysis used to evaluate highway roughness, Wear, 57 (1979) 117. 2 M. C. Leu and J. J. Henry, Prediction of skid resistance as a function of speed from pavement texture, paper presented at the 57th Annual Meeting of the Transportation Research Board, Jan. 1978, Washington, D.C., Tronsp. Res. Rec. 666, Natl. Acad. Sci., Washington, D.C., 1978, pp. 7 - 13.
136 3 E. D. Howerter and T. J. Rudd, Computer evaluation of pavement texture, Vol. 1, Summary Rep. FHWA-RD-78-36, Federal Highway Administration, Washington, D.C., 1973. 4 W. P. Chamberlin and D. E. Amsler, Me~uring surface texture of concrete pavement by the sand-patch method, Rep. FHWA-NY-78-RR62, Federal Highway Administration, Washington, D.C., 1978. 5 J. J. Henry and R. R. Hegmon, Pavement texture measurement and evaluation, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975, pp. 3 - 17. 6 S. Gee, W. L. King and R. R. Hegmon, Pavement texture measurement by laser; a feasibility study, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975, pp. 29 - 41. 7 R. K. Hillquist and P. C. Carpenter, A basic study of automobile tire noise, Sound Vibr., 8 (Feb. 1974) 26 - 28. 8 W. A. Leasure, The cost and safety aspects of quiet tire use, Sound Vibr., 1 I (Feb. 1977) 18 - 23. 9 R. E. Veres, J. J. Henry and J. M. Lawther, Use of tire noise as a measure of pavement macrotexture, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975. 10 T. R. Thomas, Recent advances in the measurement and analysis of surface microgeometry, Wear, 33 (1975) 205 - 233. 11 F. Holt and G. Musgrove, Skid Resistance: Photo interpreters Manual, Ontario Ministry of Transportation and Communication, Canada, Nov. 1977. 12 C. Cantor, Specification of a single line instrument for measuring highway surface texture, Rep. FHWA-~~-R-76-8E, Federal Highways Admin~tration, Washington, D.C., 1976. 13 M. E. Meyer, R. R. Hegmon and T. D. Gillespie, Locked wheel skid tester correlation and calibration techniques, NCHRP Rep. 151, Natl. Res. Council, Washington, D.C., 1974.
Wear, 57 (1979) 127 - 136 @ Elsevier Sequoia S.A., Lausanne - Printed in the Netherlands
DEFINITION AND MEASUREMENT ROUGHNESS*
OF PAVEMENT
SURFACE
RUDOLPH R. HEGMON Research Mechanical Engineer, Office of Research, HRS-1.2, Federal Highway Administration, Washington, D.C. 20590 (U.S.A.) (Received June 11,1979)
Summary Pavement roughness is divided into three scales; roughness, macrotexture and microtexture. All three are surface irregularities of random nature. The dividing lines between them are based on functional considerations such as traffic safety and ride quality. Roughness is the largest scale with characteristic wavelengths of 0.1 - 100 m and amplitudes of 1.0 100 mm. The two texture scales have wavelengths and amplitudes of the same order of magnitude covering a range of 0.01 - 10 mm with a dividing line at about 0.25 mm; their function is generally related to pavement-tire traction characteristics. The two texture scales, their functional significance and methods of measurement and analysis are discussed. It is shown that current technology does not meet the needs of the highway engineer. Some details are given about measurement methods under development which have the potential to be operated from a vehicle moving at normal travel speeds.
1. Introduction The term “engineering surfaces” can readily be interpreted to include pavements. In fact a great deal of structural and material engineering goes into the design of pavements and pavement surfaces must meet a number of conflicting requirements. A pavement surface is far from smooth and the various scales of roughness, their causes and methods of measuring them will be discussed. Generally pavement roughness is divided into three scales: roughness, texture and microtexture. All three are surface irregularities of random nature. The dividing lines between them are based on functional considerations such as traffic safety and ride quality. Roughness is the largest scale *Presented at the International Conference Engineering Surfaces, Leicester, April 18 - 20,1979.
on Metrology and Properties of
128
with characteristic wavelengths of 0.1 - 100 m and amplitudes of 1.0 100 mm and affects primarily vehicle dynamics. The two texture scales have wavelengths and amplitudes of the same order of magnitude covering a range of 0.01 - 10 mm with a dividing line at about 0.25 mm; their function is generally related to pavement-tire traction characteristics. A number of texture measurement methods are available, none of which are very satisfactory because of the large dynamic range. All these methods can be classified into two groups. One group of relatively simple measurements provides an average value over an area several times larger than the largest texture dimension and requires only limited data processing. The second group of measurements provides profile records which are amenable to analysis by standard statistical methods with some adaptation to the specific needs of traffic safety. Current research is directed toward high speed non-contacting measurements which can be performed from a vehicle moving at normal traffic speed. In addition a measure of texture can be obtained indirectly through friction measurements. Analysis of texture data ranges from simple mean values to more comprehensive statistical methods which are designed to give a full description of pavement surfaces with a minimum of parameters.
2. Scales of pavement
surface roughness
Roughness of pavement surfaces covers an enormous dynamic range. Expressed in terms of wavelength and amplitudes the ranges are from about 1O-2 to lo6 mm and from 10V2 to lo2 mm respectively. These ranges are subdivided into three scales, each covering a different function in terms of pavement performance. The dividing lines are necessarily somewhat arbitrary because of the gradual transition between the roughness effects. The largest scale is termed road roughness (or waviness) with wavelength and corresponding amplitudes as characteristic dimensions. Road roughness primarily affects ride quality and dynamic pavement loads. The range of wavelengths is about 0.1 - 100 m and the range of amplitudes is 1.0 - 100 mm. Normally the larger amplitudes are associated with the longer wavelengths. Road roughness is discussed elsewhere [1] and will not be treated here. The remaining two scales are termed microtexture and macrotexture: the former spans a scale of 0.01 - 0.25 mm, the latter a scale of 0.25 10 mm. In the case of texture no distinction is made between amplitude and wavelengths because both are of the same order of magnitude. Thus macrotexture overlaps the road roughness amplitudes, but in terms of the wavelength there is a gap (minimum wavelength about 100 mm, maximum macrotexture about 10 mm). Pavement irregularities of wavelengths between 10 and 100 m are rarely of a recurrent nature but occur mostly as singularities such as cracks and joints. The exception is an artifically grooved pavement in which transverse grooves may be spaced about 20 mm or more apart.
129
3.
Significance of pavement texture
Whereas roughness is undesirable and detrimental to a good road, pavement texture is essential. A pavement ~thout texture would be very unsafe under wet conditions. In the United States pavements are wet about 10% of the time and in Great Britain the percentage of time they are wet is probably greater. Therefore in designing new pavement or in resurfacing old ones the highway engineer will provide adequate texture. At present texture is not specified explicitly but is provided implicitly by specifying the construction method and the materials to be used in the pavement surface. Macrotexture is provided through the construction process. Basically there are two pavement types, flexible and rigid. Flexible pavements are constructed with bituminous mixes. The mix design and the gradation of the aggregate used together with the degree of compaction determine the magnitude of the macrotexture. In rigid (concrete) pavements macrotexture is provided by the finishing process of the pavement surface. This is done with various tools just before the concrete hardens. Microtexture is primarily a materials property. The aggregate used in the top layers of bituminous pavements is selected for its suitability to provide and retain microtexture under continued exposure to the polishing+ action of traffic. In concrete pavements microtexture is provided through the inclusion of fine hard particles in the concrete mix, Macrotexture together with tire treads relieves the hydrodynamic pressure generated between the tire and the pavement surface. The water can escape through the channels formed by texture and tire tread. The tie available for water removal is short, of the order of a few milliseconds, and decreases with speed. Thus macrotexture becomes more important at higher driving speeds. However, even with the best macrotexture a thin water film will always adhere to the surface. The function of microtexture is to pierce this water film and provide direct tire-pavement contact at as many points as possible. Thus the right combination of microtexture and macrotexture is essential for safe driving during rain and until the pavement is dry.
4. Measurement of pavement texture For a texture measurement to be valid and useful to the highway engineer it must meet several requirements. It should be easy to use and interfere as little as possible with traffic. It must obviously also be representative of the pavement surface on which it is taken. This last requirement is easier stated than met since only small samples can often be measured for several miles of road. Measurement methods in use today can be classified as indirect, averaging and profiling. The indirect methods are based on the different functions of microtexture and macrotexture. Both textures help to provide tirepavement contact under wet conditions and thus provide the friction forces
130
necessary for safe driving. A friction measurement can therefore be used as an indirect measure of pavement texture. Analysis of experimental data has shown that low speed friction correlates with microtexture while macrotexture correlates with friction-speed gradients at higher speeds [ 2, 31. Friction measurements are relatively easy to perform; the major drawback is poor repeatability. Averaging methods provide a texture value over an area large enough to be considered representative of the road surface. One method uses a known volume of material which can easily be spread over the surface [4]. It must be fine enough to fill the cavities in the surface. The area covered by the material is measured and the mean depth is given by the ratio of volume to area. A number of variations on this method are in use. Another averaging method uses a known volume of water which is allowed to drain between a rubber seal and the pavement surface [ 51. Clearly the coarser the surface the faster the water will escape. Thus the outflow time is a measure of texture. These measurements are crude and can be made within a few minutes. For increased confidence in the results a number of replicate measurements must be made along the road. Improved averaging methods are being developed, but their ultimate utility is still in doubt. One such method is based on the principle of light depolarization [6] . Linearly polarized light is directed toward the pavement and the reflected light is received by a photodiode (Fig. 1). The light source and receiver are arranged in specular geometry, i.e. at equal angles to the vertical. The light reflected from the randomly textured pavement is no longer linearly polarized. In other words the alignment of the electric field vector, which was unidirectional for the incident light, was changed after reflection to trace out an ellipse in space. The degree of ellipticity is a function of pavement texture and is measured by the ratio of the minor to the major axes of the ellipse. By using the ratio instead of absolute values the effect of varying reflectivity is eliminated, at least in theory. Such a depolarization system has been mounted on a car and used at regular travel speeds. Repeatability is excellent and the results are independent of speed. Correlation of this
Fig. 1. Optical
diagram
of the depolarization
system
for texture
measurement.
131
method with other texture measurements is close to 0.8 and work is now under way to improve this. The primary effort is toward the use of narrowband light. We expect that a wavelength can be used which will improve the correlation. Another averaging method which has shown some promise as measure of macrotexture is the level of tire noise. The fact that tire noise varies with texture has been es~b~shed by a number of researchers [ 7,8]. It was therefore proposed that analysis of the noise of a standard tire could be a unique descriptor of pavement texture. Experiments have been conducted on a number of pavements with different macrotextures {93 . Spectral analysis of the noise records showed that above 1600 Hz the sound pressure level becomes a function of texture with a total difference of about 10 dB for the eight pavements tested (Fig, 2). This sensitivity is not adequate and efforts to achieve better resolution will probably be made as part of ongoing research. The method is attractive because it is passive, Le. no energy need be transmitted to the pavement. Tire inflation pressure and speed will have to be controlled very closely.
FREOUENCY.
kHz
Fig. 2. Tire noise spectra obtained for eight surfaces at 66 km h-l.
Indirect and averaging methods are relatively simple because texture is described by a single number. This may be adequate if only values relative to other pavements or changes over time are to be established. For a full description more than a single number is required. Detailed information on surfaces can be obtained by microscope methods which have been reviewed briefly by Thomas [lo]. However, these are at best laboratory methods and not very useful to the highway engineer. A somewhat related method was developed by Schonfeld [ll].It uses s~~ophoto~aphs, which can be taken with relative ease on the pavement, which are then analyzed under appropriate magnification. None of the measurement methods reviewed so far have the potential of solving the ~x~re-me~urement problem for the highway engineer. For this reason most of our effort goes into the development of profiling methods. These have the advantage of producing an analog electrical signal
132
which can be processed by standard analysis methods. Mechanical profile tracers have been used successfully. For macrotexture profiling a relatively simple home-made instrument was used with a stylus radius of about 0.125 mm. A Gould Surface Analyzer was adapted for microtexture profiling. The total range covered by these two instruments is from 5 pm to over 50 mm (Fig. 3) with the two traces overlapping as shown in the figure. A non-contacting tracer (AUTECH Corporation Laser Gauge) was also used. The traverse speed was about 1.25 mm s-l. A typical record is shown in Fig. 4. Signal blackouts may occur such as the spike in the upper trace. Before computer processing of the signal, which was digitized at a rate corresponding to about 6 pm, such spikes were removed (center trace) by a special editing program. The resulting power spectral density (PSD) plot (lower trace) shows a resolution limit of over 50 000 cycles m-l. However, in calibrating the system on a sinusoidal surface wavelengths of 1 mm were distorted. Therefore the practical upper limit of this instrument in its present configuration is not better than 1000 cycles m-l.
Fig. 3. PSD plot of microtexture,
macrotexture
and a combination
of both.
The mechanical profile traces are satisfactory and provided a good basis for texture analysis in terms of interest to the highway engineer. The success of this initial work prompted us to look for non-contacting profiling methods to be used from a vehicle traveling at speeds of 60 - 80 km h-’ . Initial feasibility studies have identified the problems and have resulted in a concept which is now being developed [ 121. Many problems have to be overcome. At regular travel speeds the measuring system moves at about 20 000 mm s-l in the direction of travel. Recording of texture of characteristic dimensions of 1 mm or less at this speed would require a bandwidth of about 100 kHz which is beyond the range of most frequency modulated recorders even at the highest tape speeds. Digital recording speeds also fall short of the requirements although analog-to-digital conversion rates would be adequate. Measurement of microtexture, which is smaller by a factor of 100, will clearly have to be static.
133
Measurement from a moving system, even at low travel speeds, appears to be beyond the current state of the art. Another problem is vehicle bounce. A smooth riding car may bounce at about 1 Hz with peak-to-peak amplitudes of say 10 mm. Therefore any optical system, including lasers, would be out of focus most of the time. In addition the bounce effect would have to be removed from the measurement so as to separate vehicle motion from texture. This could probably be done in the data processing by using an acceleration measurement to determine bounce frequency and magnitude. This method is used successfully in roughness measurements in which, however, the required resolution is far less than for texture. A promising method now under development uses a series of flash images instead of a continuous recording. The principle is shown in Fig. 5. The light source is a stroboscope and the flash is projected through the optical system into a fan-shaped thin beam of pulsed light. This beam illuminates the surface at an angle and produces a shadow picture of the texture which is recorded by a Vidicon television camera. Thus the profile is available as digital recording for further processing. Each individual profile is about 100 mm long and samples can be taken at a rate of up to ten per second or every 2 m. Such a high sampling rate may not be necessary. The pulse duration is about 5 ps, during which time the system moves about 0.1 mm. Thus some smear will occur and we may have to compromise on the speed unless the pulse duration can be further shortened.
Fig. 4. PSD plot of pavement texture profile recorded by laser. Fig. 5. Schematic diagram of texture-recording
system.
134
5. Analysis and correlation
of texture
data
Texture data have to be related to pavement performance to be of use to the highway engineer. Thus if profile records are used instead of averaging methods they must be reduced to meaningful measures. A computer program has been written to compute the power spectrum from digitized texture profiles and to correlate this with pavement friction properties. Let the pavement profile be represented by the sequence (s,,) where n, denotes the discrete value of the corresponding continuous variable s at the nth sampling interval. The total number of samples is N. The sequence is high-pass filtered at any desired lower frequency to remove long wavelengths and also low frequency vehicle motions (in the case of measurements from a moving vehicle). Let the filtered sequence be (r,) and let it be partitioned into K sections of equal length L. These K new sequences are weighted by a function w(Z) which results in a new sequence {zk, 11= { wlrl+(k-l)LI Transforming accomplished
l
l
each of the K sequences into the frequency by the following operation: zk,
n
exp{--j( 2n/L)(n - 1)O
The computer implementation uses the fast Fourier spectral density is then given by
domain
is
l
The power
where JI (I) is a shorthand notation for $ (fi), fi = Z/L and 2; (I) is the complex conjugate of Z,(Z). Each power spectral density function consists of L/2 + 1 independent spectral estimates centered at frequency fi with a bandwidth defined by the original sampling rate for the s, and the selected value OfL. The relation between spectral density estimates and the corresponding frictional properties can be determined by a matrix analysis:
where c is a matrix of frictional properties for p pavements and at q speeds, g is the spectral density matrix for the p pavement surfaces divided into convenient bands from f, to f, and W is a matrix of weighting factors assigned to each frequency band and testing speed. The solution is given by w = (\k -- % )-Wr-_ Correlations were computed using the skid number SN defined as 100 times the coefficient of friction and the skid number-speed gradient dSN/dV.
135
Because of the relatively low correlations obtained, the concept of the percentage normalized gradient PNG which has been previously shown to correlate better with macrotexture [ 131 was used. It is defined as PNG =
100 dSN SN, dV
where SN, is the level of friction and is primarily a function of microtexture. Microtexture raises the level of friction at low speeds but its effect diminishes at higher speeds and therefore steeper gradients are obtained. The correlations were computed and found to be above 0.95. Finally, in order to evaluate the performance of the simple averaging method r.m.s. values were computed from the profiles and correlated with texture depth computed from the volumetric measurement as
where V is the known volume and the Di are the measured diameters. The correlations were better than 0.9 which was gratifying considering the crudeness of this method.
6. Summarizing
remarks
Texture is a necessary feature of pavement surfaces and makes pavements safe for driving under wet conditions. Pavement texture is subdivided into macrotexture and microtexture. The former helps to drain the tirepavement contact area while the latter penetrates the remaining water film to establish semi-dry contact. Pavement texture measurements have been made for many years using relatively crude averaging methods. These are useful and automated versions such as the depolarization method are being developed. Low speed profiling methods have been used successfully for detailed characterization of texture. The data have been analyzed by standard techniques and the results have demonstrated that pavement friction properties are highly correlated with texture. Thus texture parameters which can be obtained in many ways or can be synthesized in the laboratory can be used to predict the frictional properties of pavements. Development of a high speed profiling method is in progress. References 1 J. C. Wambold, The measurement and data analysis used to evaluate highway roughness, Wear, 57 (1979) 117. 2 M. C. Leu and J. J. Henry, Prediction of skid resistance as a function of speed from pavement texture, paper presented at the 57th Annual Meeting of the Transportation Research Board, Jan. 1978, Washington, D.C., Tronsp. Res. Rec. 666, Natl. Acad. Sci., Washington, D.C., 1978, pp. 7 - 13.
136 3 E. D. Howerter and T. J. Rudd, Computer evaluation of pavement texture, Vol. 1, Summary Rep. FHWA-RD-78-36, Federal Highway Administration, Washington, D.C., 1973. 4 W. P. Chamberlin and D. E. Amsler, Me~uring surface texture of concrete pavement by the sand-patch method, Rep. FHWA-NY-78-RR62, Federal Highway Administration, Washington, D.C., 1978. 5 J. J. Henry and R. R. Hegmon, Pavement texture measurement and evaluation, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975, pp. 3 - 17. 6 S. Gee, W. L. King and R. R. Hegmon, Pavement texture measurement by laser; a feasibility study, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975, pp. 29 - 41. 7 R. K. Hillquist and P. C. Carpenter, A basic study of automobile tire noise, Sound Vibr., 8 (Feb. 1974) 26 - 28. 8 W. A. Leasure, The cost and safety aspects of quiet tire use, Sound Vibr., 1 I (Feb. 1977) 18 - 23. 9 R. E. Veres, J. J. Henry and J. M. Lawther, Use of tire noise as a measure of pavement macrotexture, Am. Sot. Test. Mater. Spec. Tech. Publ. 583, 1975. 10 T. R. Thomas, Recent advances in the measurement and analysis of surface microgeometry, Wear, 33 (1975) 205 - 233. 11 F. Holt and G. Musgrove, Skid Resistance: Photo interpreters Manual, Ontario Ministry of Transportation and Communication, Canada, Nov. 1977. 12 C. Cantor, Specification of a single line instrument for measuring highway surface texture, Rep. FHWA-~~-R-76-8E, Federal Highways Admin~tration, Washington, D.C., 1976. 13 M. E. Meyer, R. R. Hegmon and T. D. Gillespie, Locked wheel skid tester correlation and calibration techniques, NCHRP Rep. 151, Natl. Res. Council, Washington, D.C., 1974.