Influence of end effect under loading in the intermediate principal stress direction on strainburst behaviors of Beishan granite
Bibliographic record
Abstract
Abstract In this study, strainburst tests on Beishan granite under different intermediate principal stress (σ 2) loadings were performed using a true-triaxial rockburst system. Rectangular prism specimens were prepared and divided into two groups. For the first group, the specimens were in direct contact with platens. For the second group, the specimen surfaces loaded by σ 2 were daubed with a layer of lubricant to reduce the end friction of the platens. A loading mode, which kept one specimen surface free and applied loads on the other five surfaces of the specimen, was used to simulate the stress state of a rock element on the excavation boundary. A high-speed video camera was then used to capture the failure process of the specimens. The experimental results indicated that as σ 2 increased, the degree of violence of the specimens during failure increases. However, the σ 2-dependent strength and energy release characteristics of the lubricated specimens were significantly different from those of the non-lubricated specimens. The strength of the lubricated specimens was lower than that of the non-lubricated specimens under a given σ 2, and the strength difference between them increased as σ 2 increased. Moreover, the kinetic energy difference of the rock fragments between the non-lubricated and lubricated specimens increased with increasing σ 2.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".