Dynamic prediction for attitude and position of shield machine in tunneling: A hybrid deep learning method considering dual attention

Abstract

In constructing long-distance shield tunnels, it is a difficult challenge to maintain the tunneling trajectory consistent with the design tunnel axis. The accurate prediction of the attitude and position during tunneling can reap the advantage of optimizing the tunneling operation parameters in advance leading to the best tunneling trajectory. This study investigates a framework based on a hybrid deep learning model for attitude and position prediction of the shield machine. This prediction framework contains comprehensive feature evaluation method, ensemble empirical mode decomposition (EEMD), convolutional neural network (CNN), and gate recurrent unit (GRU). The introduction of channel attention and temporal attention in CNN and GRU further strengthens the spatial and temporal feature extraction ability of the model. The performance of the prediction framework is verified through a case study with data collected from the Shanghai urban railway tunnel section. Results reveal that the proposed model with dual attention significantly outperforms other models in prediction accuracy and speed. The bias of feature data can be alleviated by introducing channel attention, and using temporal attention can capture long-distance temporal feature data. The model can support shield construction safety by adjusting the operation parameters, and an application example is used to demonstrate the feasibility of the proposed approach.