Friction Prediction in Asphalt Pavements: The Role of Separated Macro- and Micro-Texture Parameters Under Dry and Wet Conditions

The characteristics of pavement texture are key determinants of skid resistance, directly affecting tire-pavement interactions. This study examines the relationship between separated pavement textures and friction coefficients under dry and wet conditions. Using 3D laser scanning, texture data were collected from 40 asphalt pavement sections in Nanchang. The data were processed through Fourier Transform and Butterworth filtering, enabling separation of macro- and micro-textures. Based on ISO 25178-2, 16 parameters—including Sa (Arithmetic Mean Height), Str (Texture Aspect Ratio), Vmc (Core Material Volume), and Ssk (Skewness)—were selected to represent macro- and micro-texture features. These parameters were analyzed against dry and wet friction coefficients, and regression models were developed to predict FDry and FWet. The results show significant effects of both macro- and micro-texture parameters on friction coefficients. Among macro-texture parameters, Sa and Vmc strongly correlate with FDry, suggesting that greater surface roughness and core material volume enhance friction in dry conditions. Conversely, Ssk negatively correlates with FDry, indicating that negatively skewed profiles improve skid resistance. Other macro-texture parameters also influence FDry to varying extents. For micro-texture, Sdc (Material Height Difference), Spd (Peak Density), and Vvv (Valley Void Volume) primarily affect FWet, with all showing significant positive correlations. This indicates that sharp peaks and void structures in micro-texture enhance skid resistance in wet conditions. The regression models effectively predict both friction coefficients, reducing field testing complexity and cost. These models provide an efficient tool for evaluating skid resistance and supporting pavement performance and maintenance management. This study highlights the distinct roles of macro and micro-texture in skid resistance, offering insights for optimizing pavement design and maintenance.