Bibliographic record
Abstract
This paper gives an overview of the difficulties associated with the design of hard rock pillars in South African mines. Recent examples of large scale pillar collapses in South Africa suggest that these were caused by weak partings which traversed the pillars. Currently two different methods are used to determine the strength of pillars, namely, empirical equations derived from back analyses of failed and stable cases and numerical modeling tools using appropriate failure criteria. It is illustrated in the paper that both techniques have their limitations and additional work is required to obtain a better understanding of pillar strength.Empirical methods based on observations of pillar behaviour in a given geotechnical setting are popular and easy to use, but care should be exercised that the results are not inappropriately extrapolated beyond the environment in which they are established. An example is the Hedley and Grant formula (derived for the Canadian uranium mines) that has been used for many years in the South African platinum and chrome mines (albeit with some adaptation of the K-value). Very few collapses have been reported in South Africa for layouts designed using this formula, suggesting that in some cases it might yield estimates of pillar strength that are too conservative.As an alternative, some engineers strongly advocate the use of numerical techniques to determine pillar strength. A close examination unfortunately reveals that these techniques also rely on many assumptions. An area where numerical modeling is invaluable, however, is to determine pillar stresses accurately and to study specific pillar failure mechanisms, such as the influence of weak partings on pillar strength.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".