A spatiotemporal deep learning method for excavation-induced wall deflections

Abstract

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects. However, most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points. Besides, most models lack flexibility in providing predictions for multiple days after monitoring activity. This study proposes a sequence-to-sequence (seq2seq) two-dimensional (2D) convolutional long short-term memory neural network (S2SCL2D) for predicting the spatiotemporal wall deflections induced by deep excavations. The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory (LSTM) layers. The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure. The excavation depth, which has a significant impact on wall deflections, is also considered using a feature fusion method. An excavation project in Hangzhou, China, is used to illustrate the proposed model. The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D (one-dimensional) models. The prediction model demonstrates a strong generalizability when applied to an adjacent excavation. Based on the long-term prediction results, practitioners can plan and allocate resources in advance to address the potential engineering issues.

Keywords

• Braced excavation
• Wall deflections
• Deep learning
• Convolutional layer
• Long short-term memory (LSTM)
• Sequence to sequence (seq2seq)