Quantifying uncertainty in mining geomechanics design

Authors: Dunn, MJ

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DOI https://doi.org/10.36487/ACG_rep/1905_23_Dunn

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Dunn, MJ 2019, 'Quantifying uncertainty in mining geomechanics design', in J Wesseloo (ed.), MGR 2019: Proceedings of the First International Conference on Mining Geomechanical Risk, Australian Centre for Geomechanics, Perth, pp. 391-402, https://doi.org/10.36487/ACG_rep/1905_23_Dunn

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Abstract:
Uncertainty in mining geomechanics and geotechnical engineering is a broad term that accounts for natural variability, lack of data, and lack of knowledge. Reducing uncertainty is a key component of the mining study process and in managing geomechanical/geotechnical risk. Understanding and reducing uncertainty is also a key activity in the design process to ensure that designs are robust and resilient. A variety of methods are used in geomechanical design including empirical, analytical and numerical modelling. All design methods require inputs, and these are based on data from core logging, mapping, laboratory testing, field observations, and monitoring. This data then must be compiled and interpreted so that meaningful and reliable design inputs with a reliability that is commensurate with the level of design (scoping through to operational) can be derived. This includes the development of the geomechanical or geotechnical model. The uncertainty of the geotechnical model is often described in terms of confidence or reliability. Currently, very little quantitative guidance exists in the literature on assessing the confidence level of geotechnical studies and design, although there have been attempts by various authors (Haile 2004; Haines et al. 2006; Read 2009; Dunn et al. 2011) to qualitatively describe what level of geotechnical data is required. Several authors have outlined methods that could be applied to assess the reliability of geotechnical data (Read 2013; Fillion & Hadjigeorgiou 2013; Dunn 2015). Data from a range of projects are reviewed and summarised and an attempt made to quantify the uncertainty for some data, and illustrate the impact this can have on designs and commonly used design acceptance criteria.

Keywords: uncertainty, risk, reliability

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