Quantifying uncertainty in rock mass properties: Implications for GSI, RMi, and RMR assessments

Probability-based empirical methods were employed as an alternative approach to predicting uncertainties associated with rock mass properties. The focus was on developing probabilistic spreadsheets to forecast rock mass classification indexes. Histograms were constructed to describe the best distribution in predicting rock mass properties. The developed models also offer utility in predicting the impact of discontinuities within the rock mass on rock strength and rock mass classification systems. Statistical analyses identified volumetric joint count, joint spacing, joint frequency, and rock strength as the most influential parameters. Moreover, the statistical analysis revealed varying degrees of correlation among different rock mass properties. While some properties demonstrated significant correlations suitable for modelling, others did not align well with any correlation model. The results highlight the need for a comprehensive approach to rock mass characterization, considering multiple factors beyond volumetric joint count. Geological complexities, including tectonic activity and weathering processes, may obscure direct correlations. These results emphasize the importance of empirical modelling and detailed site investigations for accurate assessment of rock mass quality and stability in the Himalaya.