A Statistical Review of Rock Mass Classification With Some Surprising Results

The main classification systems that estimate rock mass properties include variants of the Rock Mass Rating (RMR) System, Rock Quality Designation (RQD), the Norwegian Tunneling Index (Q) (Barton), and the Geological Strength Index (GSI).The RMR system is based on intact strength, Rock quality designation, fracture frequency, and discontinuity condition.Typical distributions for these parameters may be approximated by normal, lognormal, or Weibull distributions and discrete distributions for fracture conditions.Water and pore pressure are sometimes included but should generally be treated as a boundary condition.RQD is a single-parameter system describing the percentage of sound rock core with a length greater than 10 cm.The Barton system has three main terms to describe a relative measure of the block size, an indicator of the inter-block shear strength, and a third quotient to describe "active stresses." One of the primary findings of the statistical study is that two parameter systems, such as blockiness and joint condition, are better differentiators between rock mass classes; however, these systems still smear parameters.For example, a GSI range of 25-55 each indicates anything from blocky to disintegrated rock mass conditions.As a result, strength estimators based on GSI are not reliable for estimating rock mass strength and, in turn, the factor of safety.RMR and GSI results result in values on a normal distribution scaled from 0-100, typically centered on 50.The models are insufficient to describe the tail end of the distribution where rock mass failure typically occurs.The Q system significantly improves when the terms are derived independently but suffers from the same normalization issue when calculated from an RMR value.The surprising finding is that single-parameter systems, including Fracture Frequency and Volumetric joint count, are better estimators (and differentiators) or properties across the range of rock mass conditions when looked at individually.Always understand the range of the rock mass parameters used and how that can impact the design.The paper will evaluate the need to develop strength estimators based on fracture frequency and volumetric joint count.Rock mass strength estimates should be developed from the underlying input data for classification rather than from normalized sums of multiple parameters.