Health monitoring of ground support system through point-cloud processing: rockbolts extraction phase

Abstract Safety in underground mining operations relies on understanding the geological and geotechnical properties of the site. The creation of an underground void for mining induces instability in the rock structure, resulting in deformation. The compressive strength of the rocks is maintained by the tensioning of ground support, such as rockbolts. Monitoring and predicting the condition of the mining ground support system is crucial for ensuring the safety of operations. Inspecting the mining tunnels poses challenges due to their large span and ongoing production activities. Light Detection and Ranging (LiDAR) technology can scan physical structures and generate point cloud data, which is valuable for creating applications like topographic mapping and spatial models. Extracting rockbolt information from point cloud data from underground mines can offer comprehensive mine coverage. This information can be utilised to monitor the condition of the rockbolts over time. Extracted rockbolt data can assist in the health monitoring of ground support, indicating deformation due to geostatic pressure. This paper proposes a method for extracting rockbolt spatial information from point cloud datasets collected via LiDAR technology to facilitate Prognostics and Health Management for ground support in underground mining.