Case study: back-analysis of a historical open pit highwall failure at a coal mine in Canada
Bibliographic record
Abstract
An open pit highwall up to 400 m in height is being planned at a coal mine in southeastern British Columbia, Canada. A geotechnical drilling program completed in 2016 identified that a thick (20‒40 m), weak, soil-like coal seam will be exposed along the base of the proposed highwall slope. A historical highwall failure at an adjacent open pit occurred in 2011 where successful monitoring resulted in no personnel injury or equipment damage. Following the failure, a review identified that a thick seam of sheared coal at the base of the slope might have contributed significantly to the failure; however, no back-analysis was performed. The discovery of a similar thick, friable, soil-like coal seam which will be exposed at the toe of the proposed highwall emphasised the need for a back-analysis of the historical slope failure to assist with understanding the previous failure relative to the design of the future wall. A back-analysis was then completed using limit equilibrium and finite element numerical modelling methods and included a review of monitoring, climate, and observational data. The finite element method best captured the site observations and data associated with the failure. This paper presents the key findings from the back-analysis including the results of the finite element modelling, as well as conclusions which were relevant to the design of the proposed highwall slope.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".