An investigation of texture-friction relationship with laboratory ring-shaped asphalt mixture specimens via close-range photogrammetry

Adequate pavement surface friction is important for highway safety and reducing traffic accidents and fatalities. The current asphalt mixture design does not directly consider skid resistance durability. The objective of this study was to develop a practical image-based method for friction measurement and to evaluate the skid resistance using small-scale ring-shaped specimens compacted by a Superpave gyratory compactor during mixture design. This approach aimed to replace traditional large-scale rectangular slabs for measurement. An image-based, close-range photogrammetry (CRP) technique was employed to construct surface models for both ring-shaped and slab-based asphalt mixture specimens. The collected texture images of asphalt mixture specimens were digitized into three-dimensional (3D) texture models. Skid resistance was measured using a dynamic friction tester (DFT) in conjunction with a circular track meter (CTM). The Persson friction model was then used to investigate the interaction between the surface texture and the friction. Next, the calculated coefficient of friction (COF) from Persson friction model was calibrated by the measurement obtained via DFT. The mean profile depth (MPD) parameter, calculated from the digital 3D model and measured by CTM, was analyzed to assess the effectiveness of the proposed CRP method. Moreover, parameters calculated from the 3D model were used to correlate with COF. Result indicates that the relative error of mean profile depth (MPD) between CRP and CTM is 2.88 %, demonstrating the promising benefits of the proposed method. The R² of the calculated COF from Persson model and the measured COF via DFT is 0.82, which indicates a strong correlation between the predicted and measured values of COF. The single texture parameter: Root mean square height (S_q), demonstrates the ability to predict the COF with confidence. Hence, the presented work provides valuable insights into the application of image-based methods for skid resistance evaluation.