ROCK MASS CLASSIFICATION - REVIEW | ROCK TUNNELLING
The most frequently used RMCS are all at least
25-years old, except for the more recent Q-slope system. In contrast to constitutive modelling, where newer
models tend to require more input parameters (Zhang et al. 2021), RMCS that were developed in the past decade do not show such a trend. For example, both the Q-system and RMR were developed in the 1970s and require six input parameters and recent developments such as Q-slope (Barton and Bar 2015), ARMR (Saroglou et al. 2019) or A-BQ (Guo et al. 2020) also require, respectively, six, six, or two input parameters. In terms of parameter assessment, most RMCS
rely heavily on visual estimations, and some newer developments even highlight that they do not require any measured input quantities (e.g., SSAM from McQuillan et al. 2018). While the practice of relying on subjective, visual assessments is convenient as it exempts rock engineers from having to deal with new technology, it also hinders technology-driven progress in rock engineering. Skretting et al. (2023) for example showed highly user-dependent rock mass classifications if the same tunnel faces are visually assessed by multiple experts. Furthermore, most RMCS introduce some form
of bias related to problems, such as: subjective, low dimensional assessment of parameters; limited or selective choice of case studies used for the system development; or, narrow fields of applications that should not be exceeded. Examples include: (i)
(ii) SRMR was introduced based on case studies from only two locations (Robertson 1988); SSAM was developed specifically for coal mine slopes (McQuillan et al. 2018).
Many technical and regulatory factors could be discussed for why there is such a plethora of RMCS today, including: (i)
(ii) a possible disagreement in the community; (iii) limited or no governmental regulation and standardisation;
(iv) different international experts bringing their preferred system;
(v) different geological requirements.
In a bigger picture, however, most RMCS that were developed in the past decade are not conceptually or technologically very different from the ones developed half a century ago. Consequently, some of the new RMCS may not properly address problems in the existing systems, and fundamental research is needed to significantly improve the existing rock mass classification system paradigm. The low adoption rate of new RMCS can, therefore, be related to not offering sufficiently improved results in comparison to their predecessors. The introduction of modern technology, such as
RQD is subject to sampling bias since it is directionally dependent based on the drilling direction (Deere 1989) and also suffers from other limitations related to its assessment in general (Pells et al. 2017);
geophysics, drilling data, scanning, and remote sensing for site investigations, has greatly increased the amount of available data as a possible input for rock mass classification and characterisation. While some of these methods, like geophysics, only provide indirect information about the rock mass conditions, they still contribute to give operators a more holistic
the simultaneous use of multiple RMCS for cross- validation on projects;
Table 2: Comparison of shared input parameters for selected RMCS RMCS Intact rock strength Discontinuity density Discontinuity conditions Discontinuity orientation Faulting/ fault zones Water Environment Stress Ground Disturbance X X
RQD C
RMR XX X Q XX X MRMR
X X
X X
X
RMS XX X SMR XX X C-SMR XX X CSMR XX X
SRMR XX X SSPC XX X M-RMR XX X BQ XX X GSI
XX GSPI XX X X
ARMR XX SSAM XX X A-BQ
Q-slope X
XX X
X X X X XXX X X
X X
X X X X
X
X XXX X X X X X
X X X
X X
X X
X X X
X X
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