Abstract
Rock mass classification provides fundamental data for a numerical stability analysis of rock structures. Among rock mass classification systems, the RMR and Q systems often are used for rock support system selection and the Geological Strength Index (GSI) system for estimating rock mass strength and deformation parameters. Moreover, the GSI system is the only rock mass classification that is directly linked to engineering design parameters such as the Mohr–Coulomb or Hoek–Brown strength parameters or the rock mass modulus. However, the original application of the GSI system requires long-term experience and a careful approach because of the fact that its use is a subjective decision. A quantitative approach to assist a less experienced engineer in assigning representative GSI values was presented. It employed the rock block volume and joint conditions as quantitative characterisation factors. Their approach is founded on the linkage between descriptive geological terms and measurable field parameters, such as joint spacing and joint roughness. In this study, a discrete fracture network (DFN) model incorporated with stochastic simulation is applied to characterise rock block size distribution for determination of the GSI. The fracture frequency obtained from the core logging data is analysed and provided to the DFN model as input data. Realisation of the DFN and its verification are conducted to establish the joint systems corresponding to the original fracture frequency. As a result, the stochastic simulation can successfully provide the information on the rock block size distribution to the procedure of the GSI determination.
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