Evaluation of the adjusted rockburst damage potential method for dynamic ground support selection in extreme rockburst conditions
Bibliographic record
Abstract
As modern underground mining progresses deeper, the elevated stress condition often translates to a greater seismic and rockburst hazard. The sudden and often violent failure of rock associated with rockbursts poses a significant threat to the safety and profitability of the operation. There is a wide range of practices and strategies for managing seismic and rockburst risks which are currently accepted and implemented in the mining industry. The rockburst damage risk, however, is currently managed, amongst other techniques, with the implementation of dynamically resistant support systems. The current engineering design of dynamic support systems for rockburst prone excavation is plagued with uncertainties and unknowns (Potvin & Wesseloo 2013b). Because of the complex nature of rockburst damage, an empirical approach of dynamic support selection is often preferred over a theoretical approach. The adjusted rockburst damage potential (RDP) method, re‐interpreted by Duan (2015) and based on the original RDP developed by Heal et al. (2006) and Heal (2010), is an empirical approach of dynamic support selection that utilises five rockburst damage contributing factors. In this paper, the adjusted empirical method was applied to two historic rockburst cases to assess its performance under extreme rockburst conditions. The study’s results, key findings and recommendations are presented in this paper.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".