Analysis of rockburst in tunnels subjected to static and dynamic loads
Bibliographic record
Abstract
The presence of geological structures such as faults, joints, and dykes has been observed near excavation boundaries in many rockburst case histories. In this paper, the role of discontinuities around tunnels in rockburst occurrence was studied. For this purpose, the Abaqus explicit code was used to simulate dynamic rock failure in deep tunnels. Material heterogeneity was considered using Python scripting in Abaqus. Rockbursts near fault regions in deep tunnels under static and dynamic loads were studied. Several tunnel models with and without faults were built and static and dynamic loads were used to simulate rock failure. The velocity and the released kinetic energy of failed rocks, the failure zone around the tunnel, and the deformed mesh were studied to identify stable and unstable rock failures. Compared with models without discontinuities, the results showed that the velocity and the released kinetic energy of failed rocks were higher, the failure zone around the tunnel was larger, and the mesh was more deformed in the models with discontinuities, indicating that rock failure in the models with discontinuities was more violent. The modeling results confirm that the presence of geological structures in the vicinity of deep excavations could be one of the major influence factors for the occurrence of rockburst. It can explain localized rockburst occurrence in civil tunnels and mining drifts. The presented methodology in this paper for rockburst analysis can be useful for rockburst anticipation and control during mining and tunneling in highly stressed ground.
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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 | 0.001 | 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".