A review of non-contact approach for pavement skid resistance evaluation based on texture

Measuring the skid resistance of asphalt pavement by pavement texture using non-contact measurement technology has attracted attention in recent years. However, there was no consensus about evaluation method for capturing and characterizing texture of asphalt pavement. This gives rise to the different prediction result of skid resistance for asphalt mixture from the perspective of pavement texture. On this basis, this paper provides an up-to-date review of pavement skid resistance prediction by non-contact approach. In the first section, the Bibliometric analysis was used to analyze the research trend. Subsequently, the friction mechanism and the role of texture under different condition were reviewed. In this direction, the characterization of texture relating to skid resistance was summarized. Finally, the texture-based skid resistance evaluation model was reviewed. The result indicates that the effective contact texture is occurred within 1 to 3mm or 20%-90% of top topography surface. Non-contact approach enables to capture the texture/skid resistance of pavement in a labor-slight, time-saving manner. It is consistent with the demand of intelligentization of transportation infrastructure. The high-resolution point cloud data provided by non-contact approach is the basis for evaluation of skid resistance accurately. Note that, the geometric parameter provides the indicator basis for multi-scale characterization derived from spectral and fractal approach. In addition, deep learning model is a promising method and in line with the development of artificial intelligence era. The finite element model considers the coupling effect of tire-water-road-temperature especially water film. As another important method, energy-based model allows to reflect the viscoelastic behavior of rubber and the multi-scale fractal features of pavement. To sum up, this paper offered some positive guidance for predicting the skid resistance by using non-contact methods.