Numerical modelling of rock preconditioning by destress blasting
Bibliographic record
Abstract
This paper describes a three-dimensional numerical modelling technique for the simulation of rock preconditioning by destress blasting in hard rock mining operations at depth. Rock preconditioning is commonly practised in high-stress environments as a means for the alleviation of rockburst events during production drilling and blasting. The numerical model for preconditioning employs two parameters: α, a rock fragmentation factor and β, a stress dissipation factor, in the preconditioned zone. The need for preconditioning to alleviate rockburst occurrence is evaluated from a theory previously established by the authors, which makes use of strain energy parameters to calculate the so-called Burst Potential Index (BPI) in the rockmass surrounding underground excavations. When the BPI approaches or exceeds 100%, the method suggests that rockburst is imminent and rock preconditioning is required. The modelling technique is based on incremental analysis to permit the simulation of successive mining and destressing cycles. This paper focuses on typical face destress blasting practice found in Canadian mines drift development at depth. A drift preconditioning case study is presented using three different destress blasting patterns. Cet article décrit une technique de modélisation numérique tridimensionnelle pour la simulation de pre-conditionnement du roc par les tirs de relaxations (ou decompression) dans les mines profondes de roche dure. La pre-conditionnement du roc est souvent pratiquée dans les endroits de contraintes élevées pour aider à réduire le coups de terrain durant le forage et dynamitage de production. Le modèle numérique de pre-conditionnement emploie deux paramètres: α, un facteur de fragmentation de roche, et β, un facteur de dissipation de contrainte, dans la zone pre-conditionnée. Le besoin de pre-conditionnement pour contrôler le coups de terrain est évalué par une théorie déjà établie par les auteurs, qui utilise les paramètres de l'énergie de déformation pour calculer l'index de potentiel d'un coups (BPI). Quand l'index BPI dépasse 100%, la méthode suggère que le coups de terrain est imminent et que la pre-conditionnement du roc est du. La technique de modélisation est basée sur l'analyse incrémentale afin de permettre la simulation des cycles successifs de minage et décompression. Cet article se concentre sur les tirs de relaxation de face typique, qui se trouve dans les galeries de développement des mines Canadiennes. Un cas d'étude de pre-conditionnement est présenté en utilisant trois patrons différents de tirs de relaxation.
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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".