Quantifying uncertainty in mining geomechanics design

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

Mine portal design: considerations, methods and practices

“…The aim should be to reduce uncertainty of the inputs to a point where the natural variability is understood. This has previously been discussed by various authors (Hadjigeorgiou & Harrison 2011;Hadjigeorgiou 2012;Fillion & Hadjigeorgiou 2013;Dunn 2013Dunn , 2015Dunn , 2019.…”

Substantiation of Geotechnical Risk Assessment Criterion for Mining Systems with Uncontrolled Caving of Ore and Surrounding Formations

“…[17]. The acceptable reliability level of the input data identified by Read and Stacey (2009) can be determined through the technological solution implementation stage [18][19]. Significance coefficients for the key rock mass parameters used in the calculation of the hydraulic radius, such as the rock mass breakdown point, jointing, and stress ratio, can be determined by the mathematical simulation of their impact on the hydraulic radius value based on the actual mining and geological data [23].…”

Stochastic Continuous Modeling for Pillar Stress Estimation and Comparison with 2D Numerical, and Analytical Solutions in an Underground Stone Mine

“…Figure 8 shows two WSM data points located over the same geological province (Valley and Ridge) where the mine is located. Point A is a data point obtained using earthquake focal mechanisms measured in Giles county, 11 Table 3 presents a summary of the orientations of the two selected points from the stress map. Both values indicate a maximum horizontal stress ( H ) oriented towards north west ranging in its azimuth from 35° to 55°, with a mean between 40° and 45°.…”

“…This means that characterizing the variability of a measurable parameter (e.g., intact rock strengths, discontinuities orientation, rock density) helps to reduce uncertainty (or increase our degree of knowledge). Dunn discussed the impact of geotechnical uncertainty on underground mine design, and the importance of adequately characterizing it to improve safety through an adequate risk assessment process [11]. In this work, a simplified stochastic approach is used to evaluate the effect of rock mass elastic properties' variability on pillar stress distribution and variability.…”