Large-scale destress blasting for seismicity control in hard rock mines: A case study
Bibliographic record
Abstract
Destress blasting is a rockburst control technique where highly stressed rock is blasted to reduce the local stress and stiffness of the rock, thereby reducing its burst proneness. The technique is commonly practiced in deep hard rock mines in burst prone developments, as well as in sill or crown pillars which become burst-prone as the orebody is extracted. Large-scale destressing is a variant of destress blasting where panels are created parallel to the orebody strike with a longhole, fanning blast pattern from cross cut drifts situated in the host rock. The aim of panel destressing is to reduce the stress concentration in the ore blocks or pillars to be mined. This paper focuses on the large-scale destress blasting program conducted at Vale’s Copper Cliff Mine (CCM) in Ontario, Canada. The merits of panel destressing are examined through field measurements of mining induced stress changes in the pillar. The destressing mechanism is simulated with a rock fragmentation factor (α) and stress reduction/dissipation factor (β). A 3D model is built and validated with measured induced stress changes. It is shown that the best correlation between the numerical model and field measurements is obtained when the combination of α and β indicates that the blast causes high fragmentation (α = 0.05) and high stress release (β = 0.95) in the destress panel. It is demonstrated that the burst proneness of the ore blocks in the panel stress shadow is reduced in terms of the brittle shear ratio (BSR) and the burst potential index (BPI).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".