Franke, J, de Almeida Pires Souza, B, Pereira Campos, M, Horng Liu, J & Gonzalez, C 2026, 'Managing challenging ground conditions: a Cuiabá mine case study on 
utilising rapid automated deformation tracking for ground support optimisation', in D Cumming-Potvin & J Wesseloo (eds), Ground Support 2026: Proceedings of the 11th International Conference on Ground Support in Mining, Australian Centre for Geomechanics, Perth, pp. 1-14, https://doi.org/10.36487/ACG_repo/2625_22
(https://papers.acg.uwa.edu.au/p/2625_22_Franke/)
Abstract: AngloGold Ashanti operates a number of underground mines in different regions, including Cuiabá Mine in the Mineração complex in Brazil. Cuiabá is a significant gold producer in Brazil and one of the company’s key assets in the Americas. Its underground operation is known for its rheology contrast across banded iron formation, Meta-Adesites, requiring advanced safety measures for mining and environmental control. With depth come increased challenges, including geotechnical ground conditions as laid out in its paper. Cuiabá operational team have a long-standing tradition and many success stories of adopting cutting-edge technologies to manage such challenges.
This paper details how one such technology of utilising automated ground support tracking of in situ rockbolts on the basis of LiDAR scan cloud collection is applied to not only identify deterioration in ground support, but also to accurately determine its remaining capacity. A case study based on dense spatial and temporal data from rapidly collected scans within days of each other tracked a severe deformation in high detail. This was only possible because of the high degree of automation of the system in use, which eliminated the need for personnel to process any of the raw data, and allowed for thorough and timely geotechnical analysis instead.
One of the case study outcomes as detailed in the paper was the ability to better define representative in situ rockbolt capacities than what is possible to estimate with laboratory or pull testing of ground support elements in isolation. Such remaining in situ ground support capacity data can then be used as quantified inputs into preventative and therefore safer rehabilitation planning for other areas with the same ground support in use.

Keywords: LiDAR, deformation/convergence monitoring, instrumentation and monitoring, automated point cloud processing, ground support optimisation, determining ground support capacity, ground support rehabilitation