Shield tunneling efficiency and stability enhancement based on interpretable machine learning and multi-objective optimization

作者：Wenli Liu, Yang Chen, Yang Chen, Tianxiang Liu, Wenzhao Liu, Jue Li, Yangyang Chen, Yangyang Chen · 发表于：Underground Space · 年份：2025 · DOI：10.1016/j.undsp.2025.01.001 · 被引用次数：15 · 研究领域：Tunneling and Rock Mechanics、Geotechnical Engineering and Analysis、Geotechnical Engineering and Underground Structures

• A new multi-objective optimization framework proposed for shield construction parameters. • A hybrid model SSA-LGBM proposed for the prediction of ground settlement. • SHAP analysis is used to make the prediction model interpretable. • Combine SHAP analysis with LGBM information entropy feature importance analysis for feature selection. • A practical case of tunnel construction in China for demonstration purposes. Adequate control of shield machine parameters to ensure the safety and efficiency of shield construction is a difficult and complex problem. To address this problem, this paper proposes a hybrid intelligent optimization framework that combines interpretable machine learning, intelligent optimization algorithms, and multi-objective optimization and decision-making methods. The nonlinear relationship between the input parameters and ground settlement (GS) is fitted based on the light gradient boosting machine (LGBM), and the effect of the input parameters on GS is analysed based on SHapley additive exPlanation for further feature selection. Subsequently, the hyperparameters of LGBM were determined based on the sparrow search algorithm (SSA) to better fit the input–output relationship. On this basis, a multi-objective intelligent optimization model is established to solve the optimized operating parameters of shield machine by non-dominated sorting genetic algorithm II and technique for order preference by similarity to ideal solution to reduce GS and improve drillin...