Developing projects using CaveLogicTM

Authors: Arancibia, M; Soto, F Download Paper Open access courtesy of:

DOI https://doi.org/10.36487/ACG_rep/1815_31_Arancibia

Cite As:
Arancibia, M & Soto, F 2018, 'Developing projects using CaveLogicTM', in Y Potvin & J Jakubec (eds), Caving 2018: Proceedings of the Fourth International Symposium on Block and Sublevel Caving, Australian Centre for Geomechanics, Perth, pp. 407-418, https://doi.org/10.36487/ACG_rep/1815_31_Arancibia



Abstract:
This paper presents a new long-term planning software tool to develop projects exploited by mass caving methods. There are no easy to use applications currently available on the market. The software packages available commonly act as black boxes, are complex to use, and generally require highly specialised knowledge and training. The fundamental idea behind CaveLogicTM was to develop a friendly and intuitive tool, leaving aside the black boxes, to allow the user to concentrate on cave mine planning and not the software learning process. The application provides tools for the entire cycle from the block model deposit to the generation of long-term production plans. CaveLogicTM integrates the possibility of generating footprints through a genetic algorithm, incorporating restrictions and caving parameters, as well as evaluating multiple scenarios such as multiple undercut level planning. CaveLogicTMenables the simulation of previously caved levels, allowing the planner to interactively modify the optimal solutions presented by the software. This paper involves a case study for a mining project planned for a fictitiousmine transitioning from open pit to mining by caving, enhancing the management of large analysed nodes and block volumes. CaveLogicTM uses an algorithm based on image processing and geomorphology applications, applying genetic algorithm guidelines. The algorithm accomplishes the commissioned task successfully and has a processing time per envelope in the order of seconds. The software includes tools to simulate previously caved sectors, and enables the mine engineer to interactively modify the solutions considered optimal by the software.

Keywords: caving, mine planning, software, genetic algorithm

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