Data-Driven Estimation of Cerchar Abrasivity Index Using Rock Geomechanical and Mineralogical Characteristics

The Cerchar Abrasivity Index (CAI) is essential for predicting tool wear in mechanized tunneling and mining, but direct measurement requires time-consuming laboratory procedures. We developed a data-driven framework to estimate CAI from standard geomechanical and mineralogical properties using 193 rock samples covering igneous, metamorphic, and sedimentary lithologies. After evaluating 278 feature combinations with multicollinearity constraints (VIF < 10.0), we identified an optimal four-variable subset: brittleness index B1, density, Equivalent Quartz Content (EQC), and Uniaxial Compressive Strength (UCS), with rock type indicators. CatBoost achieved the best performance (Test R2 = 0.907, RMSE = 0.420), and SHAP analysis confirmed that density and EQC are primary drivers of abrasivity. Additionally, symbolic regression derived an explicit formula using only three variables (density, EQC, B1) without rock type classification (Test R2 = 0.720). The proposed framework offers a practical approach for assessing rock abrasivity at early project stages.