Using PSO-SVR Algorithm to Predict Asphalt Pavement Performance
Zhe Li, Jiupeng Zhang, Tao Liu, Yichun Wang, Jianzhong Pei, Pei Wang
Abstract
Because of the relatively low accuracy of the current asphalt pavement performance prediction, a new pavement performance prediction model was established based on the particle swarm optimization (PSO) algorithm and support vector machine regression (SVR) algorithm. First, the SVR algorithm was introduced into the model to deal with the nonlinear regression. Then the PSO algorithm was applied to improve the searching efficiency and parameter continuity of the SVR algorithm. The pavement inspection data of an expressway in eastern China from 2006 to 2015 were used to verify the results, proving the feasibility of the PSO-SVR prediction model. The research results show that the model using particle swarm optimization has a fast convergence speed, and the optimized support vector machine has better rutting prediction performance and perfect generalization, and the prediction accuracy and reliability are higher than those of unoptimized support vector machine model.