Prediction model of asphalt pavement functional and structural performance using PSO-BPNN algorithm

The rapid expansion of expressway mileage has resulted in a significant maintenance workload. Accurately predicting pavement performance is an effective approach to cost-saving, maintaining the pavement in optimal condition, and prolonging its service life. However, the current asphalt pavement performance prediction model demonstrates low accuracy and fails to fully capture the actual changes in pavement performance under varying traffic and environmental conditions. In this study, we propose an enhanced back propagation neural network (BPNN) prediction model based on the particle swarm optimization (PSO) algorithm for predicting six performance parameters of asphalt pavement, encompassing both functional and structural aspects. The influential factors considered as inputs for our model include surface layer thickness, traffic load, and climate conditions, while the output is represented by specific pavement performance indices. To evaluate the effectiveness of our proposed model, three performance indices - root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) - were employed, leading us to obtain an optimized PSO-BPNN prediction model structure. We validated our results using road surface detection data from a Chinese highway spanning from 2016 to 2020, thereby demonstrating the feasibility of our PSO-BPNN prediction model. Our case study reveals that compared to the pre-optimized BPNN model as a whole, the optimized BPNN model achieved a higher R2 value by 17.43%, with an average R2 value exceeding 0.878 across all performance indices, additionally maintaining MAPE within 5%. These findings indicate that our optimized model exhibits faster convergence speed along with improved predictive capability and generalization effect. Consequently, this developed model can be effectively integrated into a pavement management system for generating accurate and timely maintenance plans.