Simulation of unstable rock failure under unloading conditions
Bibliographic record
Abstract
Rockburst is an unstable rock failure and one of the most hazardous problems in deep hard-rock mines. Before excavation, rocks are loaded under a polyaxial condition. Upon excavation, the rocks at the excavation boundaries are loaded in the tangential direction and unloaded in the radial direction. Understanding rock behaviour under this excavation loading condition is critical for developing measures to control rock failure in underground construction. In this paper, numerical simulation results of unstable rock failure using an explicit finite element tool are presented. Firstly, uniaxial compression tests were simulated to confirm the suitability of the adopted numerical tool for simulating unstable rock failures. Transferred energy ratio (TER) and loading system reaction intensity (LSRI) were proposed as indicators to distinguish between stable and unstable failures. Secondly, unstable rock failures under polyaxial unloading conditions were simulated. The influences of loading system stiffness (LSS), specimen’s height to width ratio (H/W), and intermediate principal stress (σ 2 ) on rock failure were investigated. The simulation results showed that rock failure was more violent when the loading system was softer, the specimen was taller, and the confinement was lower. The modelling approach presented in this paper can be useful for predicting unstable rock failure and estimating released kinetic energy, which is important for designing rock support in deep tunnels to control rockburst damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".