A deep transfer learning model for the deformation of braced excavations with limited monitoring data

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The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes. To address this issue, this study proposes a transfer learning model based on a sequence-to-sequence two-dimensional (2D) convolutional long short-term memory neural network (S2SCL2D). The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded. In the absence of adjacent excavation data, numerical simulation data from the target project can be employed instead. A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism. To illustrate the proposed methodology, an excavation project in Hangzhou, China is adopted. The proposed deep transfer learning model, which uses either adjacent excavation data or numerical simulation data as the source domain, shows a significant improvement in performance when compared to the non-transfer learning model. Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations. The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.