Management of rockburst risks in deep underground engineering through controlled contour blasting
Bibliographic record
Abstract
During the development of deep underground engineering projects, the surrounding rock is susceptible to significant deformation, rockburst, and other engineering disasters. The Jinping II Hydropower Station in China experienced several highly intense rockburst during the excavation of auxiliary tunnels and the drainage tunnel. To better understand the rockburst failure process and investigate possible mitigation solutions, the combined finite-discrete element method (FDEM) is adopted to model the development and evolution of the excavation damaged zone (EDZ) also associated to controlled contour blasting. To assess the mechanical effectiveness of this method, numerical simulation analyses employing the failure approaching index, energy release rate, and excess shear stress indices are conducted. Results suggest that blasting-induced damage significantly influences the energy accumulation patterns in the surrounding rock. Changes in blasting design schemes lead to distinct evolution processes of surrounding rock damage, consequently affecting the energy release processes and the rockburst susceptibility. Recommendations for optimizing contour blasting parameters are also proposed to help reduce the risk of rockburst.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".