Authors: Arndt, S; Villa, D; Khodayari, F; Ndlovu, B

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Arndt, S, Villa, D, Khodayari, F & Ndlovu, B 2022, 'Investigating economic and risk metrics using design of experiments in fully coupled caving geomechanics simulation', in Y Potvin (ed.), Caving 2022: Fifth International Conference on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 939-950,

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Technology for fully coupled simulation of the caving process, typically accounting for the flow of material using cellular automata (CA3D) and using non-linear stress-strain analysis (finite element method, FEM) for cave propagation, has been emerging over the last decade. The highest level of autonomy in this domain is achieved with automated model construction and meshing capabilities directly driven from data pertaining block model, geotechnical domains, drawpoints and production schedule, as is the case using a block cave model in the PCBC software from Dassault Systèmes. This process automation enables encapsulating the simulation into advanced ‘design space exploration’ tools such as design of experiments (DoE) – referring to the ability to quantify the influence matrix of a large range of individual parameters and metrics and search the results space for behaviours of interest or optimise for desired outcomes. Examples of such investigations can include understanding the mechanisms causing cave propagation to stall with the risk of creating an airgap or comparing alternative schedules with different directional scenarios for cave establishment and its impact both on geomechanics, such as fault activation, as well as project net present value (NPV). The encapsulation can extend to more complex downstream processing, such as detailed grade analysis, linking block model information to processing parameters or energy consumption. Importantly, integration with business drivers such as de-carbonisation and sustainability becomes possible.

Keywords: caving geomechanics, cellular automata, finite element analysis, integrated simulation, optimisation, design of experiments, risk

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