基于深度学习的特殊岩土隧道围岩变形预测研究  

Research on Deformation Prediction o f Surrounding Rock in Special Geotechnical Tunnels Based on Deep Learning

作　　者：吕擎峰[1] 李钰 牛荣 续晓花 毛宁 康卿瑜 LÜQingfeng;LI Yu;Niu Rong;XU Xiaohua;MAO Ning;KANG Qingyu(College of Civil Engineering and Mechanics,Lanzhou University,Lanzhou 730000,China;Gansu Road and Bridge Highway Investment Co.,Ltd.,Lanzhou 730000,China)

机构地区：[1]兰州大学土木工程与力学学院,甘肃兰州730000 [2]甘肃路桥公路投资有限公司,甘肃兰州730000

出　　处：《应用基础与工程科学学报》2023年第6期1590-1600,共11页Journal of Basic Science and Engineering

基　　金：甘肃省交通运输厅科研项目(2023-13);甘肃省科技厅重点研发计划项目(21YF5FA002)。

摘　　要：泥岩、粉质黏土和碎石土等特殊岩土隧道围岩变形量大、变形速度快且影响因素复杂.基于施工监控量测数据,采用加权最大互信息系数(WMIC)和长短期记忆网络(LSTM)理论方法建立综合考虑不同影响因子共同作用下的WMIC-LSTM模型进行深度学习,预测隧道围岩变形的趋势变化.研究结果表明:特殊岩土隧道中围岩变形与地下水条件相关性最大(WMIC=0.21),初始输入步长(step)的选择对模型预测结果影响显著;相比传统的LSTM模型,WMIC-LSTM在长时间序列模型中对不同变形趋势的隧道变形曲线的预测误差更小,最优平均绝对误差(MAE)达到0.079;循环神经网络算法下的深度学习模拟预测符合预期结果,可以为隧道智能化监测和安全施工提供技术手段和预警依据.Special geotechnical tunnels,such as mudstone,silty clay,and crushed stone soil,have large deformation amounts,fast deformation rates,and complex influencing factors.Based on construction monitoring measurement data,a WMIC-LSTM model is established using weighted maximum mutual information coefficient(WMIC)and long short-term memory network(LSTM)to comprehensively consider the combined effects of different influencing factors for deep learning and predict the trend changes of tunnel rock deformation.The results show that the deformation of surrounding rock in special rock and soil tunnels has the greatest correlation with groundwater conditions(WMIC=0.21),and the selection of initial input step has a significant impact on the model prediction results.Compared with traditional LSTM models,WMIC-LSTM has smaller prediction errors for tunnel deformation curves with different deformation trends in long-term series models,with the optimal mean absolute error(MAE)reaching 0.079.The deep learning simulation prediction based on recurrent neural network algorithm meets the expected results and can provide technical means and early warning basis for intelligent monitoring and safe construction of tunnels.

关 键 词：特殊岩土隧道 监控量测 深度学习 WMIC 长短期记忆网络 变形预测 

分 类 号：U456.3[建筑科学—桥梁与隧道工程]