To study texture of pavement surfaces and its effect on pavement friction, this article features a field experiment conducted on in-service pavements to characterize surface texture by spectral analysis to substantiate links to friction values. Pavement friction was measured using a Mu-meter and British pendulum tester whereas texture data was collected using a stationary laser profilometer. Texture spectra were analyzed and expressed in third octave bands. The correlation between texture spectral levels and friction values at different speeds are discussed in the paper. Results show that the texture level, including spectral band levels, can well represent texture characteristics, as texture spectral levels have a good correlation with friction coefficient, especially the texture level of texture wavelengths at 1.25–12.5 mm versus SFC_sl (representing the slope of the side force coefficient (SFC) versus speed), i.e., the slope of friction versus speed. This friction parameter gives better correlations with texture parameters than friction values at different speeds, which is believed to be an effect of the drainage caused by texture in that wavelength range.

Surface segregation of asphalt mixtures is a common problem encountered when determining segregation level, which is a criterion of pavement quality evaluation. A method to evaluate segregation levels of asphalt pavement surface was presented based on the concave multifractal characteristic in a binary image of a pavement surface, which was obtained by digital image processing technology and mathematical morphology. The practical value of the proposed method was verified in a newly built asphalt pavement, and the segregation level was divided into five sections based on the recommended texture ratio in practical engineering. Results show that the multifractal spectrum width (Δα) quantifies the uniformity of the concave distribution. The pixel percentage of the concave (P) characterizes the ratio of the occupied area. The product of Δα and P (PWP) was quantified as the surface segregation level, which has good linear relevance with texture depth evaluation results. The proposed evaluation technique (PWP) can be used as an alternative to the sand patch method.

The surface texture of aggregate is extremely related to the functional performance of asphalt pavement. The textures on different wavelength scales make varying contributions to tire-pavement interaction, especially the rubber friction on a rough road surface. This study develops a two-scaling-range approach based on self-affine to demarcate the wavelengths of texture. A piecewise-linear fitting model was established to determine the cut-off wavelengths employed to separate the window-textures by filtering methods. Afterwards, the window-roughness was introduced to characterise the roughness of texture in a specific scale range. The relationship between the window-roughness and the rubber frictions at different slip speeds was analysed using Pearson correlation. As a result, the self-affine approach is excellent to demarcate the scales without loss of similar characteristics of surface texture. The surface texture of aggregate significantly performs a two-scaling-range self-affine characteristic. The two critical wavelength values respectively vary within 0.10–0.25 mm and 1.0–4.0 mm due to the types of aggregates. Moreover, a strong correlation between the friction and the small-scale roughness will gradually decrease with the increasing slip speed. In contrast, the opposite change is obvious on the larger scales. The method provides guidance for determining the corresponding key wavelength ranges of road surface texture under a rubber slip speed.

Pavement mean texture depth (MTD) and mean profile depth (MPD) are common evaluation indicators for characterizing the pavement surface. Clarifying the relationship between the two contributes to coordinate various measurement methods and evaluation criterions. The calculation methods of MTD and MPD were first introduced to trace the similarity between the indicators. Subsequently, the tortuosity area of surface texture was observed using x-ray Computer Tomography (CT). To further determine the model parameter, over 3000 sets of field data in Research Institute of Highway Ministry of Transport (RIOH) track were measured. Meanwhile, the field results of another track and existing recommended model were also used to validate the model. The results show that the shift relationship between MTD and MPD is theoretically unified into a linear model with a slope of 1 and an intercept related to the surface tortuosity. These tortuous areas, the main difference source between the two indicators, would lead to a greater equivalent texture depth under the coarser gradation or lower the compactness of the asphalt mixture. Even for the different track roads, the determined model (MTD = MPD + 0.03) has a lower mean relative estimation error, indicating good applicability. This study provides theoretical explanation and empirical references for the automated laser-based texture detection.

Accurate characterization of the true tire–pavement contact state is essential for understanding pavement friction; yet conventional texture indicators and nominal contact assumptions cannot directly represent the actual interfacial interaction between rubber and pavement. This study proposes a rapid and non-destructive method for measuring three-dimensional tire–pavement true contact texture under different loads. A materials testing system was used to apply controlled loads to a rubber pad–carbon paper–pavement assembly, and the resulting imprints were combined with three-dimensional laser profilometer data and support-curve-based slicing to determine the real contact area ratio, penetration texture depth, and self-affine fractal dimension. Tests on nine asphalt pavement samples under loads from 5 to 20 kN showed that the real contact area ratio increased with load but remained below 40% at 20 kN. The predicted contact area from the reconstructed 3D texture agreed well with the imprint-based results, with an absolute error not exceeding 2.59%. Penetration texture depth showed a stronger relationship with skid resistance than fractal dimension. The proposed method provides a practical means of capturing effective tire–pavement contact parameters and offers useful inputs for laboratory-based skid resistance evaluation and texture-informed friction modeling.