基于深度学习的基坑开挖引起地表位移时序预测  

Time series prediction of surface displacement induced by excavation of foundation pits based on deep learning

作　　者：唐浩然 胡垚 雷华阳[3] 路军富[1,2] 刘婷 王凯 TANG Haoran;HU Yao;LEI Huayang;LU Junfu;LIU Ting;WANG Kai(State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection,Chengdu University of Technology,Chengdu 610059,China;College of Environment and Civil Engineering,Chengdu University of Technology,Chengdu 610059,China;School of Civil Engineering,Tianjin University,Tianjin 300350,China;PowerChina Northwest Engineering Corporation Limited,Xi’an 710065,China;East China Branch of China Railway Construction Group Co,Ltd.,Kunshan 215300,China)

机构地区：[1]地质灾害防治与地质环境保护国家重点实验室(成都理工大学),四川成都610059 [2]成都理工大学环境与土木工程学院,四川成都610059 [3]天津大学建筑工程学院,天津300350 [4]中国电建集团西北勘测设计研究院有限公司,陕西西安710065 [5]中铁建设集团有限公司华东分公司,江苏昆山215300

出　　处：《岩土工程学报》2024年第S02期236-241,共6页Chinese Journal of Geotechnical Engineering

基　　金：国家自然科学基金项目(42307260);四川省自然科学基金项目(2023NSFSC0882);地质灾害防治与地质环境保护国家重点实验室开发基金(SKLGP2023K024)。

摘　　要：为更精准预测基坑工程中数据的时间特性,结合卷积神经网络CNN模型与两种单一时间序列神经网络模型长短期记忆网络LSTM模型、门控循环单元GRU模型,建立混合时间序列神经网络CNN-LSTM模型、CNN-GRU模型。基于杭州某邻近既有车站基坑开挖工程,采用滚动预测方法建立基坑开挖引起邻近地铁车站地表沉降数据集。通过平均绝对误差MAE、平均相对误差MAPE和均方根误差RMSE3种评价指标对预测结果进行评价。结果表明:CNN-GRU模型预测效果最优,CNN-LSTM模型次之,其次是GRU模型,最后是LSTM模型。CNN-LSTM混合网络模型相较于LSTM模型对3种评价指标分别降低了24.4%,53.8%,4.1%,CNN-GRU混合网络模型相较于GRU模型分别降低了13.9%,49.1%,1%。To predict the time characteristics of data more accurately in foundation pit engineering,two single time series neural network models are combined,the convolutional neural network(CNN)and long short-term memory network(LSTM),as well as the gated recurrent unit(GRU),to establish a hybrid time series neural network model CNN-LSTM and CNN-GRU.An excavation project of a foundation pit adjacent to an existing station in Hangzhou is selected,and a rolling prediction method is used to create a dataset of surface settlement caused by excavation of the foundation pit in the adjacent subway stations.The predicted results are evaluated by three evaluation indexes:mean absolute error(MAE),mean relative error(MAPE)and root mean square error(RMSE).The results demonstrate that the CNN-GRU has the best prediction effects,followed by the CNN-LSTM,GRU and LSTM.Compared with the LSTM model,the CNN-LSTM hybrid network model reduces the three evaluation indexes by 24.4%,53.8%and 4.1%,respectively,and the CNN-GRU hybrid network model decreases by 13.9%,49.1%and 1%,respectively,compared with the GRU model.

关 键 词：基坑开挖 深度学习 卷积神经网络 长短期记忆网络 门控循环单元 

分 类 号：TU43[建筑科学—岩土工程]