Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network

The objective of this study is to investigate the relationship between long-term skid resistance of epoxy asphalt mixture (EAM) and multiple engineering parameters involving mixture design parameters, construction parameters and operation parameters. Firstly, a database of 124 data sets was obtained, including optimal binder content, aggregate gradation characteristics, bulk specific gravity, air-void content and load repetitions for input parameters, and long-term skid resistance of EAM simulated by an accelerated pavement test for output. Secondly, using the database, an optimized GA-BP neural network model (i.e. GA-BP model) was established to predict the long-term skid resistance, and then a comprehensive sensitivity analysis was conducted to explore the effect of input parameters on the skid-resistant evolution based on the trained neural network. Results show that the optimized GA-BP model can effectively predict the long-term skid resistance of EAM, and the long-term skid resistance has a significant negative correlation with binder content and shape characteristic of aggregate gradation. In addition, bulk specific gravity is the most important factor influencing the long-term skid resistance, and also has the most remarkable interaction effect with other input parameters.