What question did this study set out to answer?

To establish an accurate, interpretable data-driven framework for converting engineering classification metrics between the rock mass Q-system and rock mass rating (RMR).

September 18, 2026Rock Mechanics and Rock EngineeringOpen Access

Toward Practical Rock Mass Classification and Conversion: An Interpretable Data-Driven Framework for Mapping Q-System Parameters to RMR

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Authors

SHShuai Huang JZJian Zhou MKManoj Khandelwal

Discussion

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Overview

Comparative modeling demonstrates accurate machine-learning conversion from Q-system to rock mass rating in tunnel case histories, indicating nonlinear behaviors at extreme rock qualities.

Key Points

To establish an accurate, interpretable data-driven framework for converting engineering classification metrics between the rock mass Q-system and rock mass rating (RMR).
Assembled a database of N=448 paired Q-system and RMR records from field measurements in Zimbabwe alongside published tunnel case histories.
Evaluated classical conversion relationships against statistical reference models, including recalibrated linear and spline regressions.
Trained and compared multiple regression algorithms, optimizing an extreme gradient boosting model with the opposition-based gravitational-search algorithm (OGSA–XGBoost) and analyzing parameter importance.
The recalibrated linear relationship RMR = 6.70ln(Q) + 46.31 outperformed traditional formulas, though spline modeling uncovered distinct nonlinearity at both low and high Q extremes.
The OGSA–XGBoost model achieved coefficients of determination (R2) of 0.9506 on the validation set and 0.9382 on the test set, exhibiting error residuals dominated by random scatter rather than systematic bias.
Rock quality designation (RQD) and joint roughness (Jr) exerted positive effects on predicted RMR, whereas joint alteration (Ja) and the stress reduction factor (SRF) exerted adverse effects that can counteract the benefits of high RQD.

Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/6aad0bfcde0393d728b8a7f3 https://doi.org/10.1007/s00603-026-05959-1

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September 18, 2026Rock Mechanics and Rock EngineeringOpen Access

Toward Practical Rock Mass Classification and Conversion: An Interpretable Data-Driven Framework for Mapping Q-System Parameters to RMR

View Full Paper

Ask AI

Bookmark

Authors

SHShuai Huang JZJian Zhou MKManoj Khandelwal

Discussion

Member takes

Overview

Comparative modeling demonstrates accurate machine-learning conversion from Q-system to rock mass rating in tunnel case histories, indicating nonlinear behaviors at extreme rock qualities.

Key Points

To establish an accurate, interpretable data-driven framework for converting engineering classification metrics between the rock mass Q-system and rock mass rating (RMR).
Assembled a database of N=448 paired Q-system and RMR records from field measurements in Zimbabwe alongside published tunnel case histories.
Evaluated classical conversion relationships against statistical reference models, including recalibrated linear and spline regressions.
Trained and compared multiple regression algorithms, optimizing an extreme gradient boosting model with the opposition-based gravitational-search algorithm (OGSA–XGBoost) and analyzing parameter importance.
The recalibrated linear relationship RMR = 6.70ln(Q) + 46.31 outperformed traditional formulas, though spline modeling uncovered distinct nonlinearity at both low and high Q extremes.
The OGSA–XGBoost model achieved coefficients of determination (R2) of 0.9506 on the validation set and 0.9382 on the test set, exhibiting error residuals dominated by random scatter rather than systematic bias.
Rock quality designation (RQD) and joint roughness (Jr) exerted positive effects on predicted RMR, whereas joint alteration (Ja) and the stress reduction factor (SRF) exerted adverse effects that can counteract the benefits of high RQD.

Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/6aad0bfcde0393d728b8a7f3 https://doi.org/10.1007/s00603-026-05959-1

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