Towards the development of an empirical method to assist in the selection of ground support systems in rockburst-prone conditions
Bibliographic record
Abstract
Rockburst is one of the major risks in deep underground mines. It affects mining personnel safety and the operations and profitability of the mine. Although it is impossible to eliminate the probability of occurrence of a major seismic event, some measures need to be implemented to reduce the probability of seismically induced rockfalls (rockbursts). This is generally achieved with the installation of enhanced ground support systems, often referred to as dynamic ground support systems. The design of (dynamic) ground support systems in mines with rockburst-prone conditions is often based on the experience and knowledge acquired at each mine. This is used to create site-specific dynamic support designs. Stacey (2012) concluded that since the dynamic capacity of ground support systems and the demand from seismically induced dynamic loading cannot be reliably quantified, then ‘…a clear case of design indeterminacy’ results, making it ‘…impossible to determine the required support using the classical engineering design approach.’ This paper looks at the influence of combinations of ground motion factors (GMFs) and various geotechnical conditions on the reliability of numerous ground support strategies subjected to dynamic loading conditions. The performance of seven ground support systems strategies have been investigated for a range of GMFs expressed as a function of the seismic event magnitude and distance between the seismic source and damage. The performance criterion is the survivability of the ground support system (i.e. no fall of ground, although rehabilitation may be required). A reliability index was developed to classify the reliability of the performance. Results are shown as a preliminary version of a ‘survivability matrix’ which can provide insight into the selection of ground support systems in underground mines with rockburst-prone conditions.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".