Probabilistic analysis for mine design, using coal pillar design to illustrate its potential usefulness
Bibliographic record
Abstract
© 2015 by the Canadian Institute of Mining, Metallurgy & Petroleum and ISRM. Analytical engineering design is based upon a trial-and-error iterative and deterministic process. Within this process, the engineer obtains some estimated values, plugs them into de facto closed-form equations, and receives output, which is expected to be a single number that serves as the basis for the design. This number is typically a factor of safety, or some other equivalent strength-stress ratio. This provides the engineer with a quick and relatively quantitative design methodology. This can create problems however, because in mining applications, the actual in situ system is highly variable, complex and often chaotic, which can lead to potentially incorrect conclusions that result in unsafe designs. A more appropriate and reliable approach is a probabilistic analysis for engineering design. This process is used widely in civil and other engineering disciplines, but is often overlooked for applications in coal mine design. Additionally, it seems the amount of past studies using this approach, especially in coal mine design, are rather limited. Considering how the probabilistic approach can account for uncertainty in parametric values and how unpredictable a mining design can be, the authors believe that this approach has potential in mining. This paper will focus on a summary of past studies related to probabilistic analysis in coal mine pillar design and provide recommendations for future work that could improve design reliability.
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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 | 0.001 |
| 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".