Mechanism of Strain Burst by Laboratory and Numerical Analysis
Bibliographic record
Abstract
Strain burst is often considered to be a type of failure related to brittle rock material; therefore, many studies on strain burst focus on the brittleness of rock. However, the laboratory experiments show that strain burst can not only occur in hard brittle rock‐like granite but also in the relatively ductile rock‐like argillaceous sandstone. This result proves that behavior of rock material is not the only factor influencing the occurrence of strain burst. What must also be considered is the relative stiffness between the excavation wall/ore body and the surrounding rock mass. In order to further studying the mechanism of strain burst considering the whole system, the engineering geomechanial model and numerical model of strain burst due to excavation are built, respectively. In a series of numerical tests, the rock mass involving the excavation wall as well as roof and floor is biaxially loaded to the in situ stress state before one side of the excavation wall is unloaded abruptly to simulate the excavation in the field. With various system stiffness determined by the microproperties including the contact moduli of particles and parallel bond moduli in the models of roof and floor, the different failure characteristics are obtained. Based on the failure phenomenon, deformation, and released energy from the roof and floor, this study proves that the system stiffness is a key factor determining the violence of the failure, and strain burst is prone to happen when the system is soft. Two critical Young’s moduli ratios and stiffness ratios are identified to assess the violence of failure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".