Analysis of fractal characteristic of fragments from rock burst tests under different loading rates
Bibliographic record
Abstract
Original scientific paperRock burst is a common serious geological hazard in underground engineering, which seriously affects the progress of projects.The mechanism of rock burst can be explained by the distribution law of rock fragments and its fractal characteristics.A simulation experiment of rock burst was conducted with the granite samples under a biaxial loading machine system to analyze the fractal characteristics of the fragments from rock burst tests.The granite fragments were collected and divided into coarse, medium, fine, and micro grains by a screening method.The number and mass distribution of the fragments in different size ranges were also analyzed.The fractal dimensions of the rock fragments were calculated by the mass-granularity distribution method.Results show that the loading rate is proportional to the damage degree of rock burst; the mass of rock burst debris increases with the increase in loading rate, which indicates that a high loading rate leads to considerable rock damage.A high loading rate also results in small proportions of fine and medium grains and a large proportion of coarse grain, with no significant change in the micro grain.Under the high-loading-rate condition, the fractal dimensions of rock fragments are small, but the released energy of rock burst is large.The conclusions obtained in this study confirm the feasibility of reducing the risk of rock burst by adjusting the excavation rate in engineering practice and provide the basis for further study on the mechanism of rock burst.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".