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This study intends to predict the long-term skid resistance of steel slag asphalt mixture (SSAM) from the mineral composition of the aggregates. The polished stone value (PSV) and mineral composition of the aggregates were assessed using the accelerated polishing test and X-ray diffraction, respectively. The hardness (H) and surface texture richness (STR) of the aggregates were calculated from the mineral composition of the aggregates, and then a multivariate linear model was established between PSV and H and STR. The British pendulum number (BPN) and three-dimensional morphology of the SSAM were then evaluated using a British pendulum and a pavement laser scanner, respectively. Finally, an exponential relationship was established between BPN, aggregate PSV, and various aggregate amounts of SSAM. The results show that steel slag with H, STR, and PSV was better than natural aggregates and can significantly improve the skid resistance of pavement, but the relationship between steel slag content and long-term skid resistance of SSAM was not linear, and SSAM with 50% steel slag content had the best skid resistance. The mathematical model developed can predict the long-term skid resistance of SSAM from the mineral composition of the aggregates. The model can be used by designers to predict the long-term skid resistance of steel slag asphalt pavements at the design stage and thus better determine the proportion of steel slag to other aggregates.

Determining the Long-Term Skid Resistance of Steel Slag Asphalt Mixture Based on the Mineral Composition of Aggregates