文献类型：期刊文献

英文题名：Monitoring and Prediction of Highway Foundation Settlement Based on Particle Swarm Optimization and Support Vector Machine

作者：Yang, Rui[1];Yuan, ShengLi[1]

通讯作者：Yang, R[1]

机构：[1]Xinxiang Univ, Dept Civil Engn & Architecture, Xinxiang 453000, Peoples R China

第一机构：新乡学院机电工程学院

通讯机构：[1]corresponding author), Xinxiang Univ, Dept Civil Engn & Architecture, Xinxiang 453000, Peoples R China.|[1107111]新乡学院机电工程学院;[11071]新乡学院;

年份：2022

卷号：2022

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20223512647294);Scopus(收录号：2-s2.0-85136590507);WOS:【SCI-EXPANDED(收录号:WOS:000883008100002)】；

语种：英文

外文关键词：Forecasting - Highway engineering - Particle swarm optimization (PSO) - Support vector machines - Velocity control

摘要：Highway construction has always been an important strategy in Chinas construction projects. However, because the soil in the construction area belongs to the soft soil zone, there will often be large vertical deformation in the construction process, which will seriously affect the engineering quality, so the highway FS (foundation settlement) prediction is particularly important. In order to improve the accuracy of highway stability prediction and ensure the safety of highway engineering, a prediction model based on PSO_SVM (support vector machine for particle swarm optimization) is proposed. By using the particle velocity and its position in the PSO algorithm to correspond to the kernel function parameters and penalty factors of the parameters in the model, the optimal parameters are found and substituted into the SVM prediction model to obtain the PSO_SVM. The results show that the MAD of section A# and section B # of PSO_SVM is 0.8991 and 1.3027 for different monitoring points. Conclusion. PSO_SVM has a strong learning and generalization ability, high prediction accuracy, stability, and adaptability, and can reflect the overall change information of highway FS data, which has practical application value.