Failure of rock mass induced by unloading near excavation face: a true triaxial test
Bibliographic record
Abstract
The sudden release of stress near the excavation face during tunnelling in hard rock under high geostress conditions can trigger severe failures such as collapse and rockburst. However, conventional uniaxial and triaxial tests are inadequate in replicating the complex unloading conditions encountered in situ. This study employs a true triaxial unloading system to simulate various stress paths and investigate the deformation behaviour and failure mechanisms of granite under unloading-dominated conditions. The results show that specimens primarily fail due to strong dilatancy along the unloading surface, with combined shear, tension, and splitting failure modes. Higher axial stress leads to brittle splitting failure concentrated in the specimen’s central zone, whereas lower axial stress induces tensile and slabbing failure near the unloading boundary. Mechanical parameter evolution and energy dissipation patterns reveal that increased confinement enhances energy storage capacity while intensifying brittle behaviour upon unloading. Furthermore, three classical rock failure criteria are evaluated. The Mogi–Coulomb criterion demonstrates the best predictive capability for unloading-induced strength under true triaxial stress paths. These findings provide theoretical and practical insights for failure prediction and support design in deep underground excavation projects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".