岩土工程学报2024，Vol.46Issue(z2)：236-241,6.DOI:10.11779/CJGE2024S20014
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
摘要
Abstract
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Key words
excavation of foundation pit/deep learning/convolutional neural network/long short-term memory network/gated recurrent unit分类
建筑与水利引用本文复制引用
唐浩然,胡垚,雷华阳,路军富,刘婷,王凯..基于深度学习的基坑开挖引起地表位移时序预测[J].岩土工程学报,2024,46(z2):236-241,6.基金项目
国家自然科学基金项目(42307260) （42307260）
四川省自然科学基金项目(2023NSFSC0882) （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) （SKLGP2023K024）
