基于PLAXIS-BP神经网络的暗挖隧道地表沉降预测
Prediction of Surface Settlement in Underground Tunnels Based on PLAXIS-BP Neural Network
张文旭 1李程 2陈辉 3邵浩 1魏东洋2
作者信息
- 1. 安徽建筑大学 土木工程学院,安徽 合肥 230601
- 2. 淮南市交通工程质量监督站,安徽 淮南 232002
- 3. 南陵县交通运输综合管理服务中心,安徽 芜湖 241300
- 折叠
摘要
为研究浅埋暗挖隧道施工过程中多因素联合作用对地表沉降的影响,准确评估施工风险,基于离心模型试验实测数据,将有限元软件PLAXIS3D与BP神经网络学习算法相结合,建立了饱和黏土地层隧道暗挖施工地表沉降预测模型.通过调节BP 神经网络中的隐含层层数和节点数,得到最优神经网络结构,通过增加验证集和敏感性分析进行二次验证,并进行了特征重要性分析,量化各因素对最大地表沉降的影响程度.结果表明:通过调节BP 神经网络超参数,所建模型误差小于5%,满足工程精度要求,且预测沉降变化趋势符合工程实际.
Abstract
To study the impact of multiple factors on surface settlement during the construction process of shallow buried and underground excavated tunnels,and accurately evaluate construction risks.Based on the measured da-ta from centrifugal model experiments,a prediction model for surface settlement during tunnel excavation in satu-rated clay layers is established by combining the finite element software PLAXIS3D with the BP neural network learning algorithm.By adjusting the number of hidden layers and nodes in the BP neural network,the optimal neural network structure is obtained,and secondary validation is conducted by increasing the validation set and sensitivity analysis.Then,feature importance analysis is performed to quantify the impact of each factor on the maximum surface subsidence.The research results indicate that by adjusting the hyperparameters of the BP neu-ral network,the error of the constructed model is less than 5%,which meets the engineering accuracy require-ments,and the predicted settlement trend is in line with the actual engineering situation.
关键词
数值模拟/浅埋暗挖/BP神经网络/地表沉降Key words
numerical simulation/shallow buried subterranean excavation/BP neural network/surface subsid-ence引用本文复制引用
出版年
2024
NETL
NSTL
万方数据