Analysis and Calibration of DEM Parameters for Rock Materials Based on Interpretable Machine Learning

The accuracy of discrete element method (DEM) simulations depends on the appropriate selection of microscopic parameters. However, conventional calibration using biaxial tests requires repeated adjustment of microscopic parameters until the macroscopic responses of numerical specimens match the target responses, making the process cumbersome and computationally demanding. In this regard, this study presents an interpretable, surrogate-assisted machine-learning framework for analyzing and calibrating DEM parameters of brittle rock materials represented by the Hertz–Mindlin bonded contact model. Decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were selected to compare single-tree and ensemble-tree regression approaches for capturing the nonlinear relationships between three microscopic parameters—friction coefficient μ, bond tensile strength Tb, and bond cohesion Cb—and two macroscopic parameters, bulk cohesion Co and internal friction angle φ. SHapley Additive exPlanations (SHAP) was subsequently employed to quantify and interpret the contributions of individual microscopic parameters. XGBoost showed the best performance for predicting the macroscopic parameters, whereas Tb was less reliably predicted than μ and Cb. SHAP indicated that Cb and Tb contributed most strongly to Co, while μ dominated φ. Finally, differential evolution (DE) was combined with the forward prediction model to search for feasible microscopic parameter combinations reproducing prescribed macroscopic responses.