Franke, J & Gonzalez, C 2026, 'Automated ground support deformation tracking as a rehabilitation planning tool: 
replacing qualitative with quantitative metrics', 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. 1-13, https://doi.org/10.36487/ACG_repo/2625_20
(https://papers.acg.uwa.edu.au/p/2625_20_Franke/)
Abstract: One of the regular tasks of geotechnical and mining engineers is the measurement and management of deformation in underground excavations. Although other tools have historically been used to do so, LiDAR (light detection and ranging) has emerged as the most suited technology for this purpose as it caters for quantitative and omission-free rather than merely qualitative and sporadic tracking. Regular scanning has shown to be valuable for mines with squeezing ground conditions, and those exposed to rapid deformation through seismic events. 
The authors introduced a novel method to automatically generate meaningful and actionable underground void deformation tracking results and reports from LiDAR data at the previous Ground Support 2023 in Perth. This paper presents progress and significant enhancements made since then to further streamline its methodology for broad adoption by operational geotechnical personnel, and to expand its application as a ground support rehabilitation planning tool. Examples for squeezing ground conditions are provided herein.
Descriptions on further streamlining of automated processing include a series of novel procedures to fully automate scan cloud registration or georeferencing, a step that when done manually as is current state-of-the-art, can often lead to notable spatial error bias in deformation tracking. Any such bias dilutes reliability of the method as a whole but has been overcome with the automated methodology described.
A key priority for geotechnical practitioners is the ability to better plan rehabilitation of ground support so as to ensure continued safety of personnel and access by mobile equipment. Current best practice is reactive rather than preventative, because universally effective and accurate measurements to track capacity degradation of ground support have been lacking. Preventative rehabilitation planning has now become possible and is routinely applied in practice as presented in this paper through fully automated accurate repeat tracking of typically thousands of in situ rockbolt and plate features contained in a single scan. This is achieved by automatically correlating tracked bolt and plate deformation over time with applicable capacity limits, and automatically highlighting areas nearing or having already exceeded these, such that rehabilitation planning is based on sound quantitative metrics.

Keywords: LiDAR, deformation/convergence monitoring, ground support capacity, instrumentation and monitoring, ground support rehabilitation