AI-Enhanced Surrounding Rock Parameter Determination of Deeply Buried Underground Laboratory in Jinping, China

Rock mechanical parameters are essential to design, stability analysis, and safety construction in rock underground engineering. Inverse analysis is an effective tool for determining the mechanical properties of rock masses in deep underground engineering. Given that conventional methods cannot accurately solve such problems, proxy models are widely used. This study proposes a novel inverse analysis framework integrating the CatBoost algorithm and Simplicial Homology Global Optimization (SHGO) to overcome limitations of conventional methods. CatBoost efficiently constructs a proxy model, replacing time-consuming numerical simulations. SHGO then searches for optimal rock parameters using this proxy. The method was validated in the D2 laboratory of the second phase project of the Jinping Underground Laboratory (CJPL–II) in China and applied to invert surrounding rock parameters using field displacement monitoring data and numerical simulations. Investigations examined inversion accuracy under varying excavation steps, numbers of monitoring points, and wider parameter ranges. Results show inverted parameters converge towards true values as excavation steps and monitoring points increase. Crucially, even within the most extensive parameter range, relative errors between inversion results and true values remain below 20%. This integrated CatBoost–SHGO framework provides a feasible, scientific, and promising approach for determining rock mechanical parameters.