An explainable deep learning approach to enhance the prediction of shield tunnel deviation

An explainable deep learning approach to enhance the prediction of shield tunnel deviation

Although machine learning models have achieved high enough accuracy in predicting shield position deviations, their “black box” nature makes the prediction mechanisms and decision-making processes opaque, leading to weaker explanations and practicability. This study introduces a novel explainable deep learning framework comprising the Informer model with enhanced attention mechanisms (EAMInfor) and deep learning important features (DeepLIFT), aimed at improving the prediction accuracy of shield position deviations and providing interpretability for predictive results. The EAMInfor model attempts to integrate channel attention, spatial attention, and simple attention modules to improve the Informer model's performance. The framework is tested with the four different geological conditions datasets generated from the Xiamen metro line 3, China. Results show that the EAMInfor model outperforms the traditional Informer and comparison models. The analysis with the DeepLIFT method indicates that the push thrust of push cylinder and the earth chamber pressure are the most significant features, while the stroke length of the push cylinder demonstrated lower importance. Furthermore, the variation trends in the significance of data points within input sequences exhibit substantial differences between single and composite strata. This framework not only improves predictive accuracy but also strengthens the credibility and reliability of the results.