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 decision-making 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.

本文提出了一种新颖的混合模型，旨在预测施工过程中的山岭隧道变形，结合了时间因素和静态因素。该模型利用 boosting ensemble 技术，有效地将双向长短期记忆 (Bi-LSTM) 网络（因其对时间序列数据的熟练程度而闻名）与轻梯度增强机 (Light GBM) 模型（因其擅长处理时间序列数据而闻名）集成在一起。表格数据的处理。在预测过程中，Bi-LSTM 和 Light GBM 模块分别用于处理时间相关因素和静态因素。使用梁王山隧道的数据，与其他七个已建立的机器学习模型相比，对该模型的性能进行了评估，其结果证明了我们的模型在减少局部和整体预测误差方面的优越性。通过 Light GBM 引入与每个监测部分相关的静态因素，我们的模型为时间延迟预测问题提供了一个强大的解决方案——这是时间序列预测中尚未充分解决的挑战。最后，我们使用SHAP（SHapley Additive exPlanations）方法来解释混合模型的决策机制。研究结果揭示了我们的模型所采用的预测规则与隧道工程和物理力学的基本原理之间的显着相关性，从而强调了其在实际场景中的可靠性和潜在适用性。