Automated ground and support deformation monitoring: a novel method with expanded features for geotechnical engineers

Authors: Franke, J; Gonzalez, C Download Paper Open access courtesy of:

DOI https://doi.org/10.36487/ACG_repo/2465_23

Cite As:
Franke, J & Gonzalez, C 2024, 'Automated ground and support deformation monitoring: a novel method with expanded features for geotechnical engineers', in P Andrieux & D Cumming-Potvin (eds), Deep Mining 2024: Proceedings of the 10th International Conference on Deep and High Stress Mining, pp. 429-444, https://doi.org/10.36487/ACG_repo/2465_23



Abstract:
One of the regular tasks of geotechnical and mining engineers is the measurement and management of deformation in underground mine excavation networks. Although other methods have historically been used to do so, light detection and ranging (LiDAR) has emerged as the most suited technology for this application because it caters for quantitative and omission-free rather than merely qualitative tracking. Regular scanning has shown to be highly advantageous for mines with swelling or squeezing ground and those with rapid deformation, which is common for many deep mines. Our paper provides an update on improved capabilities and expanded features of a novel solution of fully automated LiDAR point cloud data processing dedicated to drive and other void deformation tracking that was first introduced at the Australian Centre for Geomechanics Ground Support 2023 conference. This update covers new features to automatically extract and track rockbolt deformation. Our system not only enables geotechnical engineers to avoid having to learn unrelated skills, but also provides immediate output of sophisticated reporting deliverables not available elsewhere. This opens up the opportunity to monitor many more excavation volumes at a higher frequency and gain better insights than is possible with conventional manual processing tools and methodology. This in turn can make the difference between managing rather than missing the risk of rockfall and ultimately fatalities. More specifically, we present how our processing methodology utilises latest generation cloud-based data storage and processing infrastructure to seamlessly integrate and automatically provide standard geotechnical reporting, accessible from anywhere, that relieves geotechnical engineers from having to compile this material manually. Such reporting includes sophisticated and comprehensive automated detection and tracking of ground support to aid cost efficient rehabilitation planning, which can lead to significant cost savings for deep and other sites experiencing dynamic conditions and squeezing ground. Case study material illustrates a range of these topics.

Keywords: LiDAR, automated rockbolt tracking, deformation monitoring, convergence monitoring, instrumentation and monitoring

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