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<p>This review article consolidates 37 rock mass classification (RMC) systems used in underground construction, focusing on the widely adopted Rock Mass Rating (RMR), Tunnel Quality Index (Q-System), and Geological Strength Index (GSI). It explores their origins, modifications, and limitations. The article also provides a concise overview of recent advances in soft computing methods, including artificial intelligence, machine learning, and deep learning, relevant to RMC in tunneling. Additionally, the study compiles and analyzes correlations developed by researchers for estimating RMR from Q, RMR from GSI, GSI from RMR, and GSI from the Q-System. Descriptive statistics reveal that parameter 'A' ranges from − 9.19 to 15.5, 0.83 to 2.38, 0.48 to 1.3, and 1.44 to 4.44, while parameter 'B' varies from 26.01 to 60.8, − 54.93 to 18.93, − 20.19 to 38.19, and 42.99 to 55.65, across the RMR-GSI-Q interrelationships. Furthermore, a regression-based analysis is conducted to establish best-fit equations for these correlations, revealing that the RMR-Q relationship follows a logarithmic trend with an accuracy of R<sup>2</sup> = 0.7688, while the RMR-GSI correlation exhibits a linear fit with R<sup>2</sup> = 0.7956, and the GSI-Q relationship follows a logarithmic trend with R<sup>2</sup> = 0.6878. The material constants 'A' and 'B' for these regression models fall within the upper and lower bounds observed in previous studies. This review provides a comprehensive resource on RMC systems, their empirical correlations, and their evolving role in underground engineering, helping geologists and engineers efficiently apply classification systems across different rock masses.</p>

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Systematic Review of RMR, Q-System, and GSI in Tunnel Classification: Origin, Advancement, and Limitations

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Md Shayan Sabri,
  • Amit Jaiswal,
  • Amit Kumar Verma,
  • T. N. Singh

摘要

This review article consolidates 37 rock mass classification (RMC) systems used in underground construction, focusing on the widely adopted Rock Mass Rating (RMR), Tunnel Quality Index (Q-System), and Geological Strength Index (GSI). It explores their origins, modifications, and limitations. The article also provides a concise overview of recent advances in soft computing methods, including artificial intelligence, machine learning, and deep learning, relevant to RMC in tunneling. Additionally, the study compiles and analyzes correlations developed by researchers for estimating RMR from Q, RMR from GSI, GSI from RMR, and GSI from the Q-System. Descriptive statistics reveal that parameter 'A' ranges from − 9.19 to 15.5, 0.83 to 2.38, 0.48 to 1.3, and 1.44 to 4.44, while parameter 'B' varies from 26.01 to 60.8, − 54.93 to 18.93, − 20.19 to 38.19, and 42.99 to 55.65, across the RMR-GSI-Q interrelationships. Furthermore, a regression-based analysis is conducted to establish best-fit equations for these correlations, revealing that the RMR-Q relationship follows a logarithmic trend with an accuracy of R2 = 0.7688, while the RMR-GSI correlation exhibits a linear fit with R2 = 0.7956, and the GSI-Q relationship follows a logarithmic trend with R2 = 0.6878. The material constants 'A' and 'B' for these regression models fall within the upper and lower bounds observed in previous studies. This review provides a comprehensive resource on RMC systems, their empirical correlations, and their evolving role in underground engineering, helping geologists and engineers efficiently apply classification systems across different rock masses.