The design of stable pillars in the Bushveld Complex mines: a problem solved?
Bibliographic record
Abstract
This paper gives an overview of the difficulties associated with \ndetermining the strength of hard-rock pillars. Although a number \nof pillar design tools are available, pillar collapses still occur. Recent \nexamples of large-scale pillar collapses in South Africa suggest that \nthese were caused by weak partings that traversed the pillars. \nCurrently two different methods are used to determine the strength \nof pillars, namely, empirical equations derived from back analyses \nof failed and stable cases, and numerical modelling tools using \nappropriate failure criteria. The paper illustrates that both \ntechniques have their limitations and additional work is required to \nobtain a better understanding of pillar strength. \nEmpirical methods based on observations of pillar behaviour in \na given geotechnical setting are popular and easy to use, but care \nshould be exercised that the results are not inappropriately extrapolated \nbeyond the environment in which they are established. An \nexample is the Hedley and Grant formula (derived for the Canadian \nuranium mines), which has been used for many years in the South \nAfrican platinum and chrome mines (albeit with some adaptation of \nthe K-value). Very few collapses have been reported in South Africa \nfor layouts designed using this formula, suggesting that in some \ncases it might yield estimates of pillar strength that are too conservative. \nAs an alternative, some engineers strongly advocate the use of \nnumerical techniques to determine pillar strength. A close \nexamination unfortunately reveals that these techniques also rely \non many assumptions. An area where numerical modelling is \ninvaluable, however, is in determining pillar stresses accurately and \nfor studying specific pillar failure mechanisms, such as the influence \nof weak partings on pillar strength. \nIn conclusion, it appears that neither empirical techniques nor \nnumerical modelling can be used solely to provide a solid basis for \nconducting pillar design. It is therefore recommended that both \nthese techniques should be utilized to obtain the best possible \ninsight into a given design problem. Owing to the uncertainties \nregarding pillar strength and loading stiffness, monitoring in trial \nmining sections and in established mining areas is also an essential \ntool to test the stability of pillar layouts in particular geotechnical \nareas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".