McGaughey, WJ 2019, 'Data-driven geotechnical hazard assessment: practice and pitfalls', in J Wesseloo (ed.), Proceedings of the First International Conference on Mining Geomechanical Risk
, Australian Centre for Geomechanics, Perth, pp. 219-232.
Geomechanical risk in mining is universally understood to depend on many apparently disparate factors acting together such as stress, stiffness, mine geometry, rock mass character, rock type, structure, excavation rate and volume, blasting, and seismicity. We have worked on many case studies over the years in both underground and open pit mines with the objective of discovering and documenting the correlation of such factors with the experience of geomechanical failure. Whether that failure is slope failure, strainbursting, fault slip-induced rockbursting, roof fall, or any other of many possible failure types, statistical correlations among the different classes of data can be found, and predictive rules for understanding geohazard based on their quantitative combination can be established and deployed in day-to-day operations. This data-driven approach requires application of methods and avoidance of pitfalls that can be standardised into a universally applicable workflow. We discuss the workflow and the pitfalls in analysis to be avoided through case study examples.
Keywords: geomechanical hazard assessment, data-driven analysis, data fusion, machine learning, artificial intelligence (AI), predictive analytics, rockburst, roof fall, slope failure
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