Study on the efficiency of destress blasting in deep mine drift development
Bibliographic record
Abstract
Canadian hard rock mines continue to reach deeper deposits, which poses greater challenges to mine safety including rock burst control. Destress blasting techniques have been successfully employed in such underground mines with the aim of preconditioning highly stressed rock mass to mitigate the risk for rock burst occurrence in deep mines. In the present study, the efficiency of destress blasting is examined through a comparison between traditional and alternative numerical modelling approaches. The traditional modelling approach assumes a uniformly distributed blast-induced damage zone extending over the entire drift face, whilst the alternative modelling approach, presented herein, simulates the damage zone for each individual blast hole. In the first part of this paper, a three-dimensional numerical model of a single blast hole is constructed, whereby the extent of blast-induced damage zone is delineated. The latter part of this paper uses the single-hole model results to examine the efficiency of destress blasting as practiced in drift development in deep mines. It is demonstrated through comparison of FLAC3D numerical simulation results that the traditional modelling approach may lead to an overly optimistic indication of destress blasting efficiency when compared with the alternative modelling approach, in which a more precise simulation of the damage zones is applied.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".