Empirical approaches for rock burst prediction: A comprehensive review and application to the new level of El Teniente Mine, Chile
Bibliographic record
Abstract
Rockburst phenomena pose significant challenges in the mining industry, particularly with increased underground activities at greater depths. These sudden failures not only jeopardize personnel safety but also impact mining investments. Consequently, it becomes crucial to assess the reliability and effectiveness of empirical methods employed for predicting rock burst occurrences and their severity, an ongoing subject of debate within the scientific community. This research presents a comprehensive review of empirical approaches for rock burst prediction. Subsequently, these approaches are applied to predict rock burst occurrences and its intensity within sections of a tunnel at the new level of El Teniente mine in Chile. Most of these methods rely on single-factor criteria to predict the likelihood and severity of rock bursts. However, inconsistencies are observed in the results obtained from these approaches in numerous cases. This discrepancy highlights the influence of various input parameters on rock burst estimations and emphasizes that single-index criteria may not encompass all the pertinent factors that contribute to this phenomenon. Consequently, such criteria may inadequately estimate or reflect the probability of rock burst occurrences. Given the multifaceted nature of rock burst phenomena, which depend on multiple factors, it becomes imperative to explore new approaches that consider a broader range of influencing factors, thereby yielding more realistic results. Hence, continued research is essential to develop new methods that address this issue comprehensively and ensure the safety of the mining industry.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".