Preconditioning blasting for rockburst control in a deep shaft sink
Bibliographic record
Abstract
This paper details the preconditioning blasting strategy that was developed and used while sinking the third deepest shaft/winze in Canada in a brittle rock mass. The high-stress conditions presented at the construction site resulted in seismic activity, uncontrolled spalling, and rockbursting. For comparison, muck is thrown away from the face in a lateral development round, leaving the round partially unconfined, which allows for immediate stress redistribution when operators are not present. A vertical blast will fill the created void with broken muck, which confines the bench and inhibits stress redistribution from occurring. As confinement is reduced from mucking out the round, there is an increase in strainburst risk when operators are required to mark bootlegs and prepare for drilling/loading the next advance. Due to the nature of shaft sinking, which relies heavily on physical labor and handheld mining equipment, there is increased operator exposure to rockburst risk compared with mechanized mining. Therefore, preconditioning blasting becomes a critical control for managing high-stress conditions. There are limited guidelines in published literature for preconditioning blasting in shaft sinking operations and less evidence that preconditioning blasting is providing a benefit. Therefore, the preconditioning blasting strategy that was used for the shaft sink was entirely original and was optimized based on visual inspections and seismic monitoring. This method should be beneficial for managing rockburst risks in deep shaft sinking in future operations.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".