Empirical Evaluation of RMR, GSI, and Q for Underground Excavations

Over the past few decades, numerous authors have published empirical correlations among rock mass classification systems, conducted under specific geological conditions. As a result, the validity of these correlations heavily relies on the knowledge of the original data. In this study, a comprehensive analysis was systematically carried out using field data obtained from various underground excavation sites, having a range of rock mass qualities, from very good to poor. The collected data were categorized into different classes based on rock mass conditions, providing a more specific analysis of correlation patterns. The RMR₁₄ has been correlated with several key parameters, including Q, Q_c, lnQ, and GSI, within the context of underground excavation. Regression analysis was utilized to ascertain the correlation coefficients and assess the statistical significance of the relationships. The results revealed a greater regression coefficient when RMR₁₄ was correlated with ln Q and Q_c for the dataset representing very good class rock mass, indicating a stronger relationship within this specific category. When comparing the equations derived from the overall dataset, the correlation with Q_c demonstrated the highest Pearson's r value across all cases, indicating statistical reliability. The inclusion of rock strength using Q_c makes this correlation especially suitable for support selection in underground excavation.