Laboratory investigation of multi-scale wear trends of fine-grained asphalt mixture pavement

This study systematically evaluates the evolution of surface texture in asphalt mixtures through linear correlation analysis and multi-scale wear simulation experiments. The results indicate that the structural similarity indices based on grey image and texture depth (SSIG and SSID) outperform traditional texture indicators in characterising wear, such as mean texture depth (MTD) and mean profile depth (MPD). Multi-scale analysis reveals that SSIG is more sensitive to wear at larger scales (≥0.6 mm), clearly distinguishing different wear stages (initial fluctuation, stability and sharp decline), whereas SSID is more suitable for detecting subtle differences between mixture types. The study proposes a dual-indicator multi-scale approach for comprehensive wear monitoring, with SSIG being effective for macrotexture loss and SSID for mid-to-micro-scale surface variations. These findings provide a new methodology for evaluating the wear durability of asphalt pavements and highlight the importance of selecting appropriate scales based on material characteristics.