Rockburst risk management in deep hard rock mines: A multi-tiered approach with dynamic ground support as the last line of defense
Bibliographic record
Abstract
Rockbursting around underground excavations continues to be a core risk for many deep mining operations. Numerous advancements in mine design, preconditioning, monitoring, mining equipment, exclusion zones, and ground support have significantly lowered the risk to mine workers. As mining continues in deeper, more challenging environments, a multi-tiered approach to rockburst risk mitigation, starting with mine design, is required. Both the overall mine sequence, for example, avoiding converging mining fronts, and local design "stick handling" can play an important role in risk reduction. Rockburst case studies dating back to the late 1990s up until 2024 are used to highlight risk factors and risk mitigation (tactical and strategic). Examples from several deep hard rock mines are used, generally in high horizontal stress fields (k ratios 1.5 to 2), and strong brittle rock (> 200 MPa unconfined compressive strength). Mining induced stresses as well as high in situ stress due to depth are discussed. The context is Canadian mining, which typically has highly mechanised operations, large openings and equipment. Examples from a narrow vein mine and shaft sinking are also given because neither environment is simple to mechanise, highlighting the need for other risk mitigations. A "sieve" analysis based on mine incident reports is used to show how a multi-tiered risk management plan can lower exposure to violent ground failures. A few incidents still make their way through the "sieve," indicating a need for further improvements. Thoughts on future/ current developments, and their strategic importance are given. Ground support is the last line of defence. The proliferation of multiple styles of yielding tendons and support systems has made many choices available to rock engineering professionals. Despite the increased availability of dynamic ground support components, there are still difficulties getting clean load transfer to bolts, determining support demand, monitoring loss of capacity over time/mining, and evaluating true system capacity. Virgin development in deep high stress brittle rock can result in high strainburst exposure prior to any stoping operations. The mining process must reduce worker exposure to unsupported rockmasses. Successful preconditioning in a shaft sinking operation, the use of high early strength shotcrete, and mechanised equipment are also 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".