Integrating unmanned aerial vehicle photogrammetry in design compliance audits and structural modelling of pit walls
Bibliographic record
Abstract
Existing field data collection methods for pit slope assessment are manual, time consuming, and can expose technical manpower in hazardous conditions. Advances in unmanned aerial vehicles (UAVs) technology allows collecting photogrammetry data of pit slopes. This aerial approach is fast, on demand and can improve the spatial and temporal resolution of the collected data. The collected data can be used to generate digital elevation models (DEMs) and point clouds to assess the bench face angle and catch benches. Furthermore, virtual mapping can be used to collect detailed structural data. This study presents the application of UAV technology to collect data at a pit wall, in Nevada, USA. A DEM is generated to conduct a design compliance audit of the pit slope. The aerial photogrammetry data is used to generate a point cloud of the slope for virtual structural mapping. The structural mapping data is integrated with the surveyed pit slope geometry to generate a conditioned discrete fracture network (DFN) model. The discontinuities mapped on the slope surface are replicated in the DFN model, while behind the wall, a constrained stochastic model is used to describe the structural complexity of the rock mass. This combined deterministic-stochastic DFN model is used to conduct a kinematic stability analysis of the pit slope. The results are compared to the field observations of slope failure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".