市政技术2026，Vol.44Issue(1)：193-201,9.DOI:10.19922/j.1009-7767.2026.01.193
融合静动态特征的高铁隧道进口段拱顶沉降预测模型研究
Research on the Prediction Model for Arch Settlement at the Entrance Section of High-speed Rail Tunnels with Static and Dynamic Features
摘要
Abstract
In order to solve the challenge of long-term prediction of arch settlement for the tunnel entrance section under complex geological conditions,static geomechanical parameters such as surrounding rock grade,tunnel burial depth,cohesion,and internal friction angle,and 11 influencing factors including the initial arch settlement se-quence after excavation(A2～A8),a prediction model based on convolutional neural network(CNN)and long short term memory(LSTM)is proposed.Taking the entrance section of a high-speed railway tunnel as the engineering background,the monitoring characteristics of the arch settlement and peripheral convergence of Class Ⅲ,Ⅳ,and V surrounding rocks were first analyzed.The results showed that the deformation generally exhibited a time history of rapid increase-slow increase-and tend to be stable,and the influence of surrounding rock grade on the cumulative settlement value was the most significant.Subsequently,on the same dataset,the proposed CNN-LSTM model was compared with traditional models of LSTM and ANN-LSTM in terms of predictive performance.The results showed that the CNN-LSTM model exhibited significant advantages in the determination coefficient R2,mean square error MSE,and mean absolute percentage error MA PE indicators.The CNN-LSTM model performed the best in indepen-dent validation tests under four different operating conditions,with all R2 values above 0.93,indicating its strong generalization ability.The contribution ranking of various influencing factors were revealed by sensitivity analysis:the rock mass grade and cohesion had the most prominent contribution to the final settlement,followed by the time series of internal friction angle,tunnel burial depth,and initial arch crown settlement during excavation.The re-search results indicate that the time series of initial arch crown settlement and static geomechanical parameters were synergistically characterized in a deep network,which is possible to predict the final settlement of the tunnel en-trance section arch in advance.And then,it can provide quantitative theoretical support for the optimization design of support,the refinement of monitoring schemes,and risk warning decisions for engineering sites.关键词
高铁隧道/拱顶沉降/静动态特征/CNN-LSTM/敏感性分析Key words
high-speed rail tunnel/arch settlement/static and dynamic characteristics/convolutional neural net-work and long short-term memory(CNN-LSTM)/sensitivity analysis分类
交通工程引用本文复制引用
卢振忠..融合静动态特征的高铁隧道进口段拱顶沉降预测模型研究[J].市政技术,2026,44(1):193-201,9.基金项目
中铁十八局集团有限公司2023年度科技发展计划课题(G23-47) （G23-47）
