Multi-objective robust optimization for enhanced safety in large-diameter tunnel construction with interactive and explainable AI

Abstract

Robust optimization is an ideal solution for enhancing safety in tunnel construction in the presence of unpredictable soil conditions, especially in large-diameter tunnel construction, since it requires the least amount of information about uncertainties. However, the application of robust optimization to real-world projects is greatly hampered by its dependence on mathematical models. To address this issue, this study builds a pipeline machine learning model to forecast tunnel-induced damage that can be addressed using the robust optimization (RO) algorithm with high accuracy. The optimization process is integrated into a building information modeling (BIM) platform and analyzed using the Shapley Additive ExPlanations (SHAP) technique, allowing the designer to understand and interact with the algorithm. The average improvement of testing samples using an ellipsoidal uncertainty set with a size of 0.05 is 23.8 and 4.9% on the two selected criteria, which is more conservative than using deterministic optimization (DO) and stochastic optimization (SO). This study establishes an interactive and explainable optimization platform that enables designers to make judgments under the most unfavorable soil conditions with the least amount of accessible information about the uncertainties during tunneling.