A novel Bi-LSTM method fusing current and historical data for tunnelling parameters of shield tunnel

Abstract

Reasonable shield tunnelling parameters play a crucial role in controlling ground stability and enhancing tunnelling efficiency. Predicting shield tunnelling parameters before excavation is of paramount importance. A novel deep learning method is introduced, integrating bidirectional long short-term memory (Bi-LSTM) layers, and fully connected (FC) layers to fuse current and historical data for shield tunnelling parameters prediction. Historical data captures the impact of excavated sections on the current predicted ring, while current data considers present conditions. A feature fusion method eliminates dimensional differences between historical and current data. The resulting tensor, encompassing both data types, is fed into the FC layer to generate predictions. The effectiveness of the method is demonstrated by predicting shield cutter head torque for Qingdao Metro Line 4 in China, outperforming traditional Bi-LSTM, MLP and RF methods significantly. Ablation studies further analyze the impact of different component modules and structural parameters on model performance. Overall, this innovative approach offers accurate shield tunnelling parameters prediction, enhancing ground stability and tunnelling efficiency.