Workflow for Discontinuity Set Characterization in Rock Masses Using 3D Digital Outcrop Models Derived from UAV Survey in an Iron Ore Mine Slope
Bibliographic record
Abstract
ABSTRACT The rapid development of unmanned aerial vehicle (UAV) technology has enabled numerous open pit and underground mining companies to leverage this tool for slope stability assessment and the construction of geotechnical models. The use of aerophotogrammetry techniques with UAVs enables geotechnical and geological engineers to work in a safe and agile manner, maintaining a safe distance from hazardous areas. These techniques can identify the orientation of discontinuities in the rock mass, which is vital in evaluating slope stability. Moreover, new semi-automatic extraction techniques for discontinuities in rock masses allow aerophotogrammetric models to be utilized in geomechanical characterization in a more accessible and efficient way. With the use of 3D point cloud analysis software, the primary sets of discontinuities in a rock mass outcrop can be identified, measured, and utilized in various applications. This study presents a methodology for constructing three-dimensional models of a mine slope and utilizing them to determine the primary orientations of discontinuity sets using images captured through UAV survey. The method comprises four primary steps: (i) UAV flight and image capture, (ii) 3D point cloud construction using SfM technique, (iii) detection and measurement of discontinuities, and (iv) statistical data analysis. INTRODUCTION AND OBJECTIVES The collection of structural data is a critical task in characterizing rock masses and evaluating slope stability in mining. Traditionally, this task is carried out manually using a geologist's compass, exposing the technical team to various risks. Additionally, many areas are inaccessible to the team, resulting in data gaps. With the advancement of aerial surveys using unmanned aerial vehicles (UAV) and the structure from motion (SfM) image processing technique, 3D point clouds (3DPC) can be generated from RGB images taken by UAVs, as described by Francioni et al. (2019). This 3DPC can be used in various applications, such as terrain analysis, slope stability, and hazard monitoring, which can reduce risk exposure and provide crucial information to the technical team, as discussed by Papathanassiou et al. (2020).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".