An intelligent construction model in whole process for shield tunnelling

Abstract

Predicting shield tunnelling parameters in the whole construction process is of great importance, which can effectively control ground stability and improve tunnelling efficiency. A novel deep learning method is developed considering transfer learning, incremental learning and Bi-LSTM fusing with available data of the next ring to be excavated (ADNRE) to predict shield tunnelling parameters in the whole process. Before construction, transfer learning uses data from similar projects to determine initial network parameters, then solve the insufficient data in the prophase of the project. As the shield machine begins to excavate, incremental learning is used to continuously accept new data and adjust model parameters in real time during the whole process. A feature fusion module in Bi-LSTM is proposed to integrate ADNRE and data of the adjacent excavated rings. The proposed Bi-LSTM method can consider the mutation of stratum conditions during tunnelling. The applicability of the proposed method is explored by predicting the shield cutter head torque of a tunnel project in Qingdao, China. The influence of fine-tuning epochs and project similarity on model performance is further discussed. Overall, the proposed method can provide reasonable whole process prediction for shield tunnelling parameters, which improves construction safety and efficiency.