ST-GC-GRU: A Hybrid Deep Learning Approach for Shield Attitude Prediction Based on a Spatial–Temporal Graph

The accurate estimation of shield attitude deviation is related to the quality of tunnel construction. However, the existing recurrent neural network (RNN)-based methods are unable to efficiently capture the spatial correlation between different timestamps (DT) and have poor prediction performance when handling drastically changing attitude data, which makes it difficult to estimate attitude deviation when attitude changes are frequent. This study proposes a shield machine attitude prediction model (ST-GC-GRU) based on a spatial–temporal graph. Different from the traditional attitude prediction methods, the method firstly introduces an improved GCN (ST-GCN: spatial–temporal graph) and the time decomposition technique to enhance its representation of the attitude change information, thus more rationally modeling the comprehensive spatial–temporal dependence of the shield structure operation data. The method demonstrates better prediction performance than previous methods under a large number of real data tests and effectively improves the low-confidence predictions of the prediction model when dealing with large attitude changes. The results indicate that the proposed method is better than the other seven prediction models in four attitude deviation values. The model and the research results can provide a reference for developing adaptive control technology in shield tunnel construction.