Research on the Fatigue Life Prediction for a New Modified Asphalt Mixture of a Support Vector Machine Based on Particle Swarm Optimization

Abstract

SMC (Styreneic Methyl Copolymers) is a novel normal temperature asphalt modifier with superior performance. It has the advantages of a low construction temperature, good road performance, good energy savings and an emission reduction effect, and can improve the performance of an asphalt mixture. The fatigue performance of an asphalt mixture is one of the important technical parameters in the structural design of asphalt pavements. The fatigue performance of an asphalt mixture under specific traffic and environmental conditions has an important guiding significance and normative function for the design, construction, and maintenance of asphalt pavement. In this paper, the mixture of an SMC normal-temperature-modified asphalt and styrene–butadiene styrene block copolymer (SBS)-modified asphalt (SMCSBS) compound-modified asphalt was investigated, and an SMCSBS composite modified asphalt mixture with a different SMC content was prepared. A semi-circular bending fatigue test (SCB) was conducted to analyze and compare the fatigue properties of the modified asphalt mixture. On this basis, this paper proposes a fatigue life prediction model of an SMCSBS composite modified asphalt mixture based on a particle swarm optimization support vector machine (PSO-SVM). SMC content (SMC accounts for the mass percentage of SMCSBS composite modified asphalt)/%, asphalt aggregate ratio, stress ratio and loading frequency/Hz were used as training data to establish the prediction model, and RMSE and R2 were used to evaluate the performance of the model. Experimental results show that the prediction results of the PSO-SVM method are more accurate than the experimental observation data and can effectively improve the prediction accuracy of the model. Compared with the M5′ model tree (M5′), artificial neural network (ANN), and support vector machine (SVM) method, the PSO-SVM method can achieve better prediction performance and a better prediction effect.