JOURNAL OF ROCK MECHANICS

JOURNAL OF ROCK MECHANICS

Modification of RMR Rock Mass Classification System for TBM Performance Prediction in Various Rock Types

Document Type : Original Article

Authors
Ameneh Dardashti 1
Jamal Rostami 2
Jafar Hassanpour 3
Rasoul Ajalloeian 1
1 The University of Isfahan
2 Colorado School of Mines
3 The University of Tehran
10.22034/irsrm.2025.507117.0198
Abstract
Since various rock mass classification systems are the most commonly used methods in many rock engineering projects, they serve as a suitable approach for estimating TBM performance due to their global acceptance and the availability of effective parameters. Comparing the most common rock mass classification systems, the RMR classification system demonstrates a better correlation with TBM penetration rate, which is attributed to its incorporation of uniaxial compressive strength (UCS) of rock as an input parameter. As the RMR classification system was developed for analyzing rock mass stability in tunnels and designing support systems, its input parameters ratings have been accordingly structured. It appears that by modifying the rating values of the input parameters and adjusting the internal weighting of each parameter, an optimized RMR can be achieved that aligns with the objective of this study—predicting TBM performance in hard rock. The objective of this study is to develop new relationships for estimating TBM performance in various rock types based on the input parameters of the RMR system and, ultimately, to adjust the RMR system to establish a new model for predicting TBM performance in rock. To this end, data from 10 tunneling projects under different geological conditions were collected in a database. Finally, using artificial intelligence algorithms, the RMRTBM classification is proposed with the aim of predicting TBM performance in hard rock. In RMRTBM, each input parameter is assigned a rating based on the relevant tables and charts, and the sum of these ratings, on a scale of 0 to 100, determines the RMRTBM value. The corresponding RMRTBM category is then identified, and based on the provided table, the rock mass FPI range, excavability class, excavability description, and tunnel stability conditions are predicted. This model can be useful during the design and planning stages of tunneling projects.
Keywords
TBM Performance
RMR Classification System
RMRTBM System
Machine Learning
Subjects
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JOURNAL OF ROCK MECHANICS
Volume 9, Issue 2
Summer 2025
Pages 71-82