Estimation of rock block size distribution for determination of Geological Strength Index (GSI) using discrete fracture networks (DFNs)

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.

In-Situ Block Characterization of Jointed Rock Exposures Based on a 3D Point Cloud Model

“…The 3DEC was used here due to availability. The geometrical characteristics of discontinuities were adopted as the input properties; a series of discrete, planar, and disc-shaped discontinuities of finite size were created; and then, the cubic-shaped rock mass model was developed for block system generation [2,19,27,69,70].…”

“…Kluckner et al [70] introduced a boundary criterion for model size determination. Kim et al [69] arranged a virtual borehole extending through the model domain, and the model size was selected based on each discontinuity liner frequency. Macciotta et al [71] suggested that the model size could be assumed to be approximately four to five times the maximum fragment sizes (e.g., fragmented rockfall block sizes) surveyed in each dimension.…”

“…Macciotta et al [71] suggested that the model size could be assumed to be approximately four to five times the maximum fragment sizes (e.g., fragmented rockfall block sizes) surveyed in each dimension. Here, the virtual borehole criterion [69] was adapted for determination of the model size. To eliminate border effects, the blocks dissected by the model boundaries were excluded (Figure 10).…”

Advancements in rock block volume calculation by analytical method for geological engineering applications

“…-For field data collection, several digital mapping techniques such as photogrammetry and laser scanning have been developed to improve the quality of data and minimize the time of data collection (Slob 2010;Assali et al 2014;Elmo et al 2014;Riquelme et al 2014;Kim et al 2015;Gaich et Pischinger 2016;Buyer et al 2018;Vazaios et al 2018;Li et al 2019;Buyer et al 2020;Azarafza et al 2021). Based on these techniques, a number of general methods for determining the rock blocks volume, have been proposed (Lopes et Lana 2017).…”