Estimation of rockburst wall-rock velocity invoked by slab flexure sources in deep tunnels
Bibliographic record
Abstract
For rock support in burst-prone ground, the wall-rock velocity adjacent to the surface of underground openings is a vital support design parameter, and depends on the seismic source mechanism inducing rockburst damage. In this study, to estimate the wall-rock velocity evoked only by rock slab buckling (an important rockburst source mechanism), a comprehensive velocity assessment method is proposed, using an excellent slab column buckling model with a small eccentricity, which relies on a novel compressive or tensile buckling failure criterion of rock slab. The true-triaxial loading–unloading tests and rockburst case analyses reveal that rock mass slabbing induced by high rock stress has major impacts on the evolution and formation of buckling rockburst in deep tunnels. Using a method based on the energy balance principle, the slabbing thickness of intact rock mass is also calculated by an analytical method, which indicates that the slabbing thickness parameter has a nonlinear relation to the following six parameters: uniaxial tensile strength (UTS), uniaxial compressive strength (UCS), normal stress (σn), length of joint (L), friction angle ([Formula: see text]), and joint roughness coefficient (JRC). These proposed models and methods have been quite successfully applied to rockburst and slabbing cases occurring in deep tunnels. These applications show that slab flexure is an important source mechanism invoking high wall-rock velocities and leading to severe rockburst damages in the area surrounding deep tunnels.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".