Abutment loading in deep cave mines: towards understanding susceptibility to strainbursts
Bibliographic record
Abstract
Deep cave mining is an inevitable requirement to meet the growing global demand for valuable minerals such as copper and gold. The experiences from historical and current deep mining suggest that the rock masses encountered at depth are more likely to be stronger, more brittle, and less jointed. While this may be a favourable condition for some mining and civil projects, under the high abutment loading associated with cave mining, the rock surrounding the mine drifts is susceptible to strainbursting, a sudden and high-energy failure mode. Loading conditions in cave mines can evolve rapidly over the course of mining, which can cause shifts in the susceptibility and triggers for strainbursting. Historical perspective on the strainbursting hazard in deep mining is presented in this paper, as well as a recent case study of the DMLZ mine where data-driven assessments have been applied to better understand the strainbursting hazard under the abutment loading condition. The strainbursting phenomenon can be difficult to manage in a complex cave mining environment. Therefore, tools and strategies for analysis of the occurrence of strainbursting that can be used to better constrain and manage the problem are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".