Abstract:

The existing fatigue life prediction of asphalt mixtures is mostly based on traditional fatigue equation fitting; however, due to the multidirectionality of pavement structure and the complexity of materials, the prediction accuracy is often not satisfactory. Therefore, this article establishes an optimized neural network-based model for predicting the strength and fatigue life of asphalt mixtures using indoor indirect tensile tests and verifies the accuracy of the prediction model. The experimental results show that the accuracy of Genetic Algorithm-Back Propagation neural network to predict the fatigue mechanical properties for asphalt mixture is within 4%, which is far superior to traditional fatigue prediction equations and can be used as an effective method to obtain data on the fatigue characteristics of asphalt mixtures.

Key words: traffic engineering, asphalt mixture, deep-learning model, strength prediction, fatigue life prediction