Preconditioning blasting for a deep blind sink shaft excavation
Bibliographic record
Abstract
In 2024, Glencore successfully completed an internal winze from 1,150–2,635 m below the surface at Craig Mine in Sudbury, Ontario, Canada. The shaft was sunk in brittle hard rock, which at the depths of construction resulted in seismicity, stress fracturing, pervasive spalling, and rockbursting conditions. The high-horizontal in situ stress meant adverse conditions manifested both in the shaft walls and the bench face. For comparison, a typical lateral development round throws muck away from the face, leaving it partially unconfined and this allows for stress redistribution to occur immediately after the blast. On the other hand, blasted muck from a shaft blast will fill the void created, which confines the bench and inhibits large-scale stress fracturing 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 limited working area associated with a shaft sinking operation, development is highly dependent on physical labour and handheld mining equipment. Compared with lateral mechanised development, fewer tactical controls can be used while shaft sinking to mitigate the risk of rockburst to operators. Preconditioning blasting became a critical control for managing high stress conditions in the shaft sink. There are limited guidelines in published literature for preconditioning blasting in shaft sinking operations and less evidence that preconditioning is providing a benefit. A customised preconditioning blasting strategy was developed based on visual inspections, seismic monitoring, and numerical modelling. The number of holes and location of the ‘de-stress’ charges were adjusted according to the rock mass conditions. It was also essential to institute controls on the shaft bottom mucking to prevent mucking beyond the planned break, so that the stress-fractured material that confined the highly-stressed rock ahead of the bench face was not removed. The experience learned from this project should be beneficial to other future shaft sinking projects at depth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".