Application of CNN-LSTM model based on spatiotemporal correlation characteristics in deformation prediction of excavation engineering

In order to predict the complex time series in excavation engineering field more accurately, a combined neural network model of CNN and LSTM is proposed, which takes the multi-dimensional time series composed of monitoring data from multiple monitoring points as the input. Firstly, the CNN is used to extract the spatial features of the input monitoring data, and the multiple time series composed of spatial features are the output. Secondly, the LSTM is used to learn the time series and predict the future state of the spatial features. Finally, the spatial features are integrated through the fully connected layer, and the predicted monitoring values are the output. This method is used to predict the ground settlement of the deep excavation of Yunling shaft in Shanghai. The results show that the accuracy of the combined model considering temporal and spatial correlation is higher than that of the single LSTM model considering temporal correlation only.