Operational open pit ground support design at Rainy River Mine
Bibliographic record
Abstract
Open pit design utilises core-logging and mapped discontinuity data to kinematically determine the optimal geotechnical slope designs. However, with the demand for steepening of open pit slopes to optimise strip ratio, some shallower discontinuities may create unfavourable kinematic conditions that require additional risk mitigation to support steeper slope angles. This paper presents a case study from Rainy River Mine in Northern Ontario, where kinematic controls on wedge and planar stability failure mechanisms were assessed, and stabilising rockbolt patterns were designed. A practical approach is presented for collecting high-quality slope scan data using drone photogrammetry and light detection and ranging (LiDAR) techniques, generating detailed representations of potential slope risks. Utilising RocScience’s RocSlope3 numerical modelling code (software), accurate 3D models of the slope profiles were developed, incorporating discrete discontinuities, orientations, and persistence model inputs. The Factor of Safety resulting from RocSlope3 models are compared to simplified slope profiles modelling techniques in RocScience SWedge and RocPlane. Fit-for-purpose stabilising rockbolting patterns are prepared to meet design acceptance criteria. This case study emphasises the importance of integrating widely available scanning techniques to develop representative 3D models, which are critical to develop effective stabilisation solutions of complex sliding failure geometries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".