Numerical Modelling Challenges in Rock Engineering with Special Consideration of Open Pit to Underground Mine Interaction
Bibliographic record
Abstract
This paper raises important questions about the way we approach numerical analysis in rock engineering design. The application of advanced numerical models is essential to adequately analyze and design different geotechnical aspects of pit-to-cave transitions. We present a critical review of numerical methods centered around the hypothesis that a model is not, and cannot be, a perfect imitation of reality; therefore, numerical modelling of large-scale mining projects requires the real problem to be idealized and simplified. The discussion highlights the dichotomy of continuum vs. discontinuum modelling and the important question of whether continuum models can effectively capture dynamic continuum-to-discontinuum processes typical of cave mining. The discussion is complemented by examples of hybrid continuum-discontinuum models to analyze the important problem of transitioning from surface (open pit) mining to underground mass mining (caving). The results demonstrate the hypothesis that forward modelling should be performed in the context of a risk-based approach, with numerical models becoming investigative tools to assess risk and evaluate the impact of different unknowns, thus classifying modelling outputs in terms of expected consequences.
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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 | 0.004 | 0.008 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".