Induced mechanism of tunnel rockbursts based on dynamic buckling of rock plates
Bibliographic record
Abstract
Rockburst, characterized by a sudden and violent rock failure resulting in the expulsion of rock from its surroundings, poses a significant threat to the safety of tunnel excavation operations, often causing property damage and injuries to workers. Buckling has been identified as a critical mechanism leading to rockbursts. Seismic events or blasting can induce rockbursts when stress waves reach the free surface of underground openings. This paper aims to investigate the induced mechanism of tunnel rockbursts based on the dynamic buckling of rectangular rock plates. As a rock stress wave approaches a tunnel sidewall, it decomposes into perpendicular and parallel component loads relative to the free surface. The perpendicular stress reflects off the free surface, forming a rectangular thin plate of rock. The parallel stress triggers parametric resonance in the plate, resulting in a tunnel rockburst. An illustrative example of tunnel sidewall rockbursts in Jinping II hydropower project, China, is provided to study the effects of stress wave amplitude and frequency, static and dynamic components, rock damping, multiple frequencies, and vibration modes. Based on this mechanism analysis, recommendations are proposed to mitigate the risk of tunnel rockbursts. The research offers a plausible explanation for the heightened frequency and severity of rockbursts in Tunnel Boring Machine tunnels compared to New Austrian Tunneling Method tunnels at the Jinping II project for the first time.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".