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

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.