54th U.S. Rock Mechanics/Geomechanics Symposium,
28 June - 1 July,
physical event cancelled
2020. American Rock Mechanics Association
3 in the last 30 days
20 since 2007
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In this study, a stochastic modelling approach is used to evaluate the potential risk of failure for an open pit mine in Quebec, Canada. More than 200 km of geotechnical borehole data drilled and logged in the pit area is used to develop 3D block models of Rock Mass Rating (RMR) using stochastic Sequential Gaussian Simulation (SGS) method. The pit design was excavated into the 3D RMR block models and the models were sectorized into zones based on the relative orientation of the pit walls. The 3D RMR block models were then converted to the block models of Slope Mass Rating (SMR) by applying joint orientation factors, and excavation method factor to the RMR blocks of each zone. Unlike the RMR block model, the 3D SMR model allows incorporating the influence of joint orientation and pit wall excavation method on the pit slope stability analysis. The method enables probabilistic analysis of geotechnical risks by identifying potential weak zones on the pit walls. The analysis could serve as a guide to make appropriate plans for mitigating the geotechnical risks.
A good understanding of the spatial variation of rock mass geomechanical properties is essential for pit slope design and optimization. This information can be used for identification of high-risk zones along pit walls (Ghasempour et al., 2018). Eivazy et al. (2016) discussed technical challenges in spatial modeling of geomechanical attributes. However, both geostatistical estimation and simulation techniques have been successfully used for modelling the spatial variability of rock mass geomechanical properties (Marinoni, 2003; Stavropoulou et al., 2007; Yu, 2010; Egaña and Ortiz 2013; Eivazy et al., 2017; Madani et al., 2018). Due to smoothing effect associated with geostatistical estimation technique such as kriging methods, geostatistical simulation technique is mostly preferred in spatial variability modelling (Isaaks & Srivastava, 1989; Chiles and Delfiner, 1999). Although kriging is the best unbiased linear estimator which gives exact predictions of variables, simulation techniques provide multiple realizations of variables based on probability distribution function (Journel & Huijbregts, 1978; Isaaks and Srivastava, 1989; Ferrari, 2014). Among the simulation techniques, Sequential Gaussian Simulation (SGS) which is built on conditional simulation algorithm is the most valuable method in modelling the variability of geomechanical properties (Srivastava, 2013).
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