Modification of the rock burst criteria based on the strain rate function under large-scale strain rates
Bibliographic record
Abstract
The strain rate is a crucial parameter for distinguishing dynamic and static loading. However, further research is needed to fully understand coal's mechanical behavior across large-scale strain rates. This paper primarily utilizes laboratory experiments to examine the strain rate effect of coal under large-scale strain rates, focusing on the strength of coal. The theoretical criterion for rock bursts under large-scale strain rates was refined using the critical stress criterion for rock bursts. The main findings are as follows. The variation in peak stress can be characterized by two distinct stages: a linear slow growth stage followed by an exponential rapid growth stage, observed under low, medium, and high strain rates (10−5 to 104 s−1). Additionally, the burst tendency index decreases as the strain rate rises, with lower strain rates promoting greater accumulation of strain energy. When the critical stress level exceeds the peak stress of coal, the likelihood of rock bursts rises considerably. Consequently, significant changes in the critical stress value, induced by external disturbances, can serve as a precursor indicator for rock bursts. The updated rock burst criteria, considering various loading disturbances, establish a theoretical basis for preventing rock bursts under different strain rate 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".