Authors: Chen, B; Harrington, T; Ayres, P; Gelinas, L-P

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DOI https://doi.org/10.36487/ACG_repo/2035_03

Cite As:
Chen, B, Harrington, T, Ayres, P & Gelinas, L-P 2020, 'Artificial intelligence assisted technology for ground support construction', in J Wesseloo (ed.), UMT 2020: Proceedings of the Second International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Perth, pp. 137-152, https://doi.org/10.36487/ACG_repo/2035_03

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Abstract:
The typical underground mining development and reconciliation process across the world utilises the common design, construct, verify and rework methodology. The primary focus of a mining development contractor is to meet the required development schedule. Hence, the development cycle is often designed and optimised to reduce the cycle time and increase the advance rate. The reconciliation of development headings is time consuming, and often a manually intensive process of verifying the installation against design via survey. Hence, this is often left as a secondary task with long delays between any feedback to the development crews. Leveraging the latest in artificial intelligence technology, high density LiDAR and high speed computing systems can provide the ability for development crews to receive real-time in-cycle feedback on their ground support construction and also to monitor the effectiveness of the ground support. This has potential to significantly increase the efficiency and quality of reinforcement, whilst reducing wastage in development.

Keywords: AI technology, LiDAR, shotcrete thickness, convergence, effectiveness of ground support

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