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 RMR(14 )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.

Automated summary

This study conducted a comprehensive analysis using field data from various underground excavation sites to determine the correlations between different rock mass quality parameters and RMR(14). The results showed that the correlation coefficients were highest when lnQ and Q(c) were correlated with RMR14 for the very good class rock mass dataset. Moreover, the correlation with Q(c) demonstrated the highest statistical reliability across all cases.