Numerical modelling and scientific visualisation – integration of geomechanics into modern mine designs
Bibliographic record
Abstract
As mines progress to depths for which the induced stress levels exceed the intact strength of the host rock, significant challenges related to rock mass instability must be met. However, given complexity and the scale of orebodies in deep mines, it is increasingly more challenging to predict/pinpoint where and when stress levels will become problematic. Prediction of where and when large scale instabilities will occur continues to be the ‘holy grail’ of rock mechanics in deep mining. There is no perfect solution; however, there have been a number of technological advancements that greatly helped to develop our understanding of rock mass behaviour and the risks pertaining to deep hard rock mines. It is recognised that at the mine scale, geology and material properties are not fully known, however, using past experience and sound engineering judgment, it is possible to use innovative tools and methodologies to arrive at a reasonable approximation of how a rock mass will behave at depth. The main goal of this paper is to provide an overview of how some of these tools and methodologies have evolved and are actively being applied to the planning of deep mines. Vale Canada Ltd.'s Creighton Mine will be used as a case study to demonstrate how these new techniques have contributed to a better understanding, and hence a better mine planning approach for hard rock mines at depth.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".