Prediction for segment strain and opening of underwater shield tunnel using deep learning method

Predicting the future mechanical behaviors of tunnel structure is essential to prevent disasters and maintain the long-term stability. Due to the underwater shield tunnels are usually constructed in complicated geological conditions, it is a challenge for traditional models to accurately predict the future behaviors considering multiple influence factors comprehensively. In this study, a multi-learning model termed as GC-GRU was presented on the basis of deep learning algorithm to predict the future mechanical behaviors of tunnel structure, which was formalized on the structural health monitoring data obtained from the Wuhan Yangtze River tunnel. Based on GC-GRU, temporal dependencies of historical performance and the spatial correlations among different monitoring indicators were captured, and the segment strain and opening in next 45 days were predicted. In addition, a series of experiments were conducted to discuss the predictive capability of the presented model, including the comparison to single indictor prediction model and some widely used classical prediction models, such as GRU, LSTM, XGboost, LR, and RNN. The comparison results denoted that GC-GRU performed best among all models especially when the prediction time scale reaches 20 days. The predicted errors of GC-GRU deduce at least 0.02 mm and 8.62με for joint opening and segment strain respectively, and model learning capability improves 2.2% for both of them. Therefore, it is reliable to introduce GC-GRU model to predict the future mechanical behaviors of tunnel structure.