Performance evaluation of highway pavement based on particle swarm optimization - DUAN Meidong; CHEN Zheng; WANG Lin; WAN Yingying; LIU Zhaohui; ZHAO Quanman (Shenzhen da xue xue bao. Li gong ban, 2024)
In order to improve the scientific and accurate evaluation of expressway pavement performance, we construct a pavement performance evaluation index system based on the measured data from the Jinan section of the Beijing-Shanghai Highway in 2022. A back propagation (BP) neural network pavement performance evaluation algorithm based on particle swarm optimization (PSO), referred to as the PSO-BP algorithm, is proposed. The results show that the PSO-BP algorithm achieves the prediction accuracy of 99.7% on the training set and 99.4% on the test set, which is 19.2% and 19.1% higher than that of the traditional BP neural network, respectively. This indicates that using the particle swarm optimization algorithm to optimize the initial weights and thresholds of the BP neural network can improve the prediction ability and accuracy of the model. The prediction results of the PSO-BP algorithm are highly consistent with the actual evaluation, demonstrating good reliability and stability. The PSO-BP algorithm can accurately evaluate and predict the performance grade of highway asphalt pavement, providing an important basis for highway maintenance decision-making.