Tunnel deformation prediction during construction: An explainable hybrid model considering temporal and static factors

This paper presents a novel hybrid model designed for predicting mountain tunnel deformation during construction, incorporating both temporal and static factors. Utilizing a boosting ensemble technique, the model effectively integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network-acclaimed for its proficiency with time-series data-with the Light Gradient Boosting Machine (Light GBM) model-recognized for its adept handling of tabular data. In the prediction procedure, the Bi-LSTM and Light GBM modules are engaged to process time-dependent and static factors, respectively. The model's performance was evaluated against seven other established machine learning models using data from the Liangwangshan Tunnel, whose outcomes demonstrated the superiority of our model in terms of its local and overall reduction in prediction errors. By introducing static factors associated with each monitoring section via the Light GBM, our model offers a robust solution to the issue of time delayed prediction-a challenge inadequately addressed in time series prediction. Finally, we used the SHAP (SHapley Additive exPlanations) method to interpret the hybrid model's decisionmaking mechanisms. The findings reveal a significant correlation between the prediction rules employed by our model and the fundamental principles of tunnel engineering and physical mechanics, thereby underlining its reliability and potential applicability in practical scenarios.