The Geological Strength Index (GSI) is a crucial tool for assessing jointed rock masses, but it is often hindered by subjectivity in visual assessments. In this study, we propose a novel quantitative GSI method wherein 3D laser-scanning point clouds are used to quantitatively derive empirical rock mass indices (SR and SCR) to estimate mechanical parameters. By integrating the GSI with the Rock Block Index (RBI) and joint spacing, a framework for quantifying the Structural Rating (SR) is established. Furthermore, the Analytic Hierarchy Process (AHP) is employed to assign weights to Surface Condition Rating (SCR) factors. The results indicate that infilling materials have the most significant impact on SCR (weight 0.6334), followed by weathering (0.2605) and roughness (0.1061). This method was applied to evaluate rock masses at depths of −915 to −960 m in the Sanshandao Gold Mine. The GSI values calculated for the foot wall, ore body, and hanging wall were 38.5, 33.8, and 37.8, respectively. Validation against conventional quantitative methods demonstrated high accuracy, with a maximum relative GSI difference of 1.5 and a deformation modulus difference of only 0.227 GPa. This data-driven approach effectively reduces subjectivity and provides a reliable tool for automated geotechnical parameter estimation.
Li et al. (Thu,) studied this question.