Antiskid decay prediction of asphalt mixtures based on aggregate mechanical properties and gradation fractals

Through comprehensive data collection, along with the coarse aggregate mechanical index, fractal dimension, and British pendulum number (BPN), a pavement friction prediction model was proposed on the basis of backpropagation neural networks (BPNNs) and support vector machine (SVM). An accelerated attenuation test was conducted to examine the antiskid performance of the asphalt mixture and aggregates at different wearing cycles. Subsequently, BPN was fitted using an exponential model. Gray relational and correlation analyses were performed to evaluate the factors influencing pavement skid resistance. According to the principal component analysis results, six schemes were prepared for the training, validation, and testing of BPNN and SVM algorithms. Test results indicate that different aggregates exhibit different antiskid properties. Quartz sandstone is the most suitable, followed by basalt and limestone. The polished stone value has the highest correlation with the attenuation model of asphalt antiskid performance. BPNN is more stable, with an R2 value of approximately 0. 8.