Automated discontinuity extraction software versus manual virtual discontinuity mapping: Performance evaluation in rock mass characterization and rock

Ground control failures are one of the main causes of accidents in the underground stone mining industry. Some of the fundamental tools for rockfall hazard identification are related to rock mass characterization and geotechnical discontinuity mapping. Recent technological advances in these methods are related to remote sensing techniques and pointcloud processing software for automated discontinuity mapping. Remote sensing techniques, such as LiDAR and photogrammetry, generate multimillion-point clouds with millimetric precision, capturing the structure of the rock mass. The automated point-cloud processing tools offer alternative algorithm-based methods to characterize and map these discontinuities. However, their applicability is constrained by multiple factors, such as site-specific conditions of the rock mass and the parameters used within the mapping algorithms. This paper evaluates the performance of automated discontinuity extraction software compared with manual virtual discontinuity mapping. Sampling windows from laser-scanned sections in an underground limestone mine are defined and mapped using the Discontinuity Set Extractor (DSE) opensource software. Results from the virtual discontinuity software are compared with manually extracted fractures from I-Site based on reviewing orientation, trace length, spacing, number of extracted discontinuities, and processing time.