江西建材Issue(1)：104-107,4.
基于PSO-BP神经网络的地下空间结构深基坑地表沉降预测研究
Prediction onSurface Settlementina Deep Foundation Pit in Underground Space Structure Based on PSO-BP Neural Network
摘要
Abstract
In this research,based on Huangmugang large-scale transportation hub underground in Shenzhen City,monitoring and predictionon the ground surface settlement isstudied.Firstly,the long-term settlement(up to 140 periods)of the 10-axis was monitored and analyzed,and its early warning status was evaluated.Then,the back propagation(BP)neural network model and Particle Swarm Optimization-BP(PSO-BP)neural network model for surface settlement were constructed according to 140 monitoring data,and the cumulative settlement of founda-tion pit in the following 10 periods was predicted to compare and verify the effectiveness of the two models.The results indicate that both neural network models can meet the construction requirements.Also,compared to the BP neural network model,the predicted values of the PSO-BP neural network model are more consistent with the measured values.The research results can provide valuable reference for predictionof surface settlement indeep foundation pit.关键词
深基坑/地表沉降/PSO-BP神经网络模型Key words
Deepfoundationpit/Surface settlement/Particle Swarm Optimization-BP neural network model分类
建筑与水利引用本文复制引用
莫永春..基于PSO-BP神经网络的地下空间结构深基坑地表沉降预测研究[J].江西建材,2024,(1):104-107,4.基金项目
江西省教育厅科学技术研究项目《磁流变阻尼器强化传热机理及优化设计方法研究》(项目编号:GJJ170398). （项目编号:GJJ170398）
