One of the important applications of DFN models is the quantification of rock mass quality through rock mass classification systems such as Geological Strength Index (GSI, Hoek et al., 1995). For instance, Cai et al. (2004) presented a quantitative method to assist in the use of the GSI system for rock mass classification, introducing the concept of equivalent block volume and considering the impact of fracture persistence. However, the analysis was limited to a simple conceptual rock mass and did not account for the complex configurations in term of fracture intersections and connectivity that would arise when considering natural fracture networks. In this context, DFN models allow more realistic representation of 3D network of fractures and better prediction of block fragmentation of rock mass. This paper discusses an alternative method of quantifying the variation of GSI by using the quantification method of Cai et al. (2004) in combination with the block volume cumulative density function (CDF) plots obtained from DFN models. Since DFN approaches exploits the use of fracture data collected from mapping of exposed surfaces and boreholes, including direct physical mapping and indirect mapping by remote sensing techniques, the authors believe the proposed method would provide a better and more objective quantification of GSI variability.

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