The influence of constitutive model selection on predicted stresses and yield in deep mine pillars – A case study at the Creighton mine, Sudbury, Canada
Bibliographic record
Abstract
Abstract Based on recent advances in modelling the post‐yield behaviour of brittle rock, the authors have developed a calibrated inelastic model of the 7,910 level (2.4 km depth) at the Creighton Mine in Sudbury, Ontario, Canada using data collected from the monitoring of pillar dilatancy. While this calibrated model represents a state‐of‐the‐art continuum approach for capturing the progressive development of yield and stresses in mine pillars, alternative state‐of‐practice approaches (elastic and perfectly plastic material models, for example) represent potentially acceptable options for practical application. The purpose of this study is to examine the influence of constitutive model choice on stress paths throughout the pillar system at the mining level of interest. The strengths and limitations of various material models are compared and contrasted. Elastic models are shown to adequately represent the larger scale pillar system behaviour from a stress transfer perspective, whereas the state‐of‐art brittle modelling approach is shown to be ideal for understanding specific pillar‐scale stresses and yield.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".