XGBoost-based global sensitivity analysis of ground settlement caused by shield tunneling in dense karst areas

Abstract

Predicting ground settlement and identifying key influential factors during shield tunneling in dense karst areas presents a significant engineering challenge due to irregular geological conditions and the complex nonlinear interactions among multiple factors. Traditional computational methods and existing machine learning models often lack either accuracy or interpretability, limiting their practical application in such environments. To address this gap, a novel global sensitivity analysis (GSA) framework has been developed, specifically tailored for dense karst areas. This framework integrates eXtreme Gradient Boosting (XGBoost) as an interpretable metamodel enhanced with SHAP analysis and combines it with the Sobol method for comprehensive sensitivity quantification. In addition, this framework incorporates integrated detection methods and karst structural parameters to ensure its applicability in dense karst construction environments. By applying this framework to actual data from the Shenzhen Metro Line 14 project, key tunneling parameters such as synchronous grouting pressure, actual excavation volume, karst cross-section total area, and karst-to-tunnel distance were accurately identified as having a significant impact on ground settlement. This approach fills a critical research gap by providing an interpretable and accurate tool for shield tunneling in dense karst areas, ultimately improving safety and efficiency in these challenging environments.