Authors: Elmo, D; Stead, D; Yang, B; Tsai, R; Fogel, Y

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Elmo, D, Stead, D, Yang, B, Tsai, R & Fogel, Y 2020, 'Can new technologies shake the empirical foundations of rock engineering?', in J Wesseloo (ed.), UMT 2020: Proceedings of the Second International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Perth, pp. 107-116,

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The past decade has witnessed an increasing interest in applications of machine learning (ML) to solve mining and geotechnical problems. This is largely due to an increased use of high-level programming languages, development of user-friendly and open source ML libraries, improved computational power, and increased cloud storage capacity to handle large and complex data sets. The benefit of incorporating ML in rock engineering design are apparent, including the reduction in the time required to sort and characterise field data and the capability to find mathematical correlations between overly complex sets of input data. However, when applied to geotechnical engineering, the question arises as to whether ML can truly provide objective results. In geotechnical engineering, where the medium considered is typically heterogenous and only limited information is spatially available, experience and engineering judgement dominate the early stage of the design process. However, experience and engineering judgement alone cannot reduce data uncertainty. It is also true that the inherent variability of natural materials cannot be truly captured unless sufficient field data is collected in an objective manner. This paper investigates the readiness of the technical community to integrate ML in rock engineering design at this time. To fully realise the potential and benefits of ML tools, the technical community must be willing to accept a paradigm shift in the data collection process and, if required, abandon empirical systems that are considered ‘industry standards’ by virtue of being commonly accepted despite acknowledging their limitations.

Keywords: cognitive biases, rock mass classification systems, uncertainty and variability

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