Automation of Underground Void and Support Deformation Monitoring – Effective Decision-Making Data and Tools Without the Penalty in Time and Effort

ABSTRACT: Despite the potential of Light Detection and Ranging (LiDAR) technology for tracking underground mining voids, its adoption has been slow because associated conventional data processing is excessively time consuming, and requires training, upskilling, and devotion by geotechnical engineers. This often becomes a bottleneck to bringing this otherwise highly advantageous method into use. Our paper introduces a new solution of fully automated LiDAR point cloud data processing dedicated to void deformation tracking that 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 much better insights than is possible with conventional manual processing tools and methodology. Critically, automatically generated 3D files for localized deformation assessment as well as automatically generated summary reports presenting key deformation tracking analysis outcomes to decision makers facilitate the detection and understanding of • In situ support system capacity and capacity degradation • Ground support behavior for dynamic conditions and squeezing ground • Cost efficiency of ground support 1. INTRODUCTION The introduction of LiDAR technology allows a quantitative assessment across the entire excavation volume and its rock surface by collecting a full three-dimensional (3D) image of the entirety of an excavation such as an underground drive, decline, stope or more generally a tunnel as also a core feature for civil engineering. Comparisons between epochs of complete 3D data coverage allows for change detection over time that doesn't feature otherwise typical omissions. Such omissions are a fundamental flaw of single-point measurement systems including bolt-mounted laser sensors, extensometers, or wall-mounted prisms surveyed using total stations. Point-wise systems are functional mature technologies and accurate if used correctly, but they will never provide a representative measure of underground drive or tunnel deformation. This is because depending on how many single-point sensors are in use, which will never match the density of a LiDAR point cloud, they inevitably miss at least the worst, if not all deformations actually occurring in-situ. Fig. 1 illustrates this issue.