Dynamic failure characteristics of surrounding rocks under different lateral pressure coefficients in deep tunnel transient excavation
Bibliographic record
Abstract
Abstract A novel transient unloading testing system was adopted to simulate the transient excavation of tunnels under different lateral pressure coefficients (k0). The results show that the transient excavation of a tunnel induces significant stress redistributions and concentrations, particle displacements and vibrations to the surrounding rocks. The decrease of k0 enhances the dynamic disturbance of transient tunnel excavation, and especially when k0 = 0.4 and 0.2, the tensile stress can be observed on the top of the tunnel. The peak particle velocity (PPV) of the measuring points on the top of the tunnel decreases with the increasing distance between the tunnel boundary and measuring point. The transient unloading wave is generally concentrated on lower frequencies in the amplitude-frequency spectrum under the same unloading conditions, especially for lower k0 values. In addition, the dynamic Mohr–Coulomb criterion was used to reveal the failure mechanism of a transient excavated tunnel by involving the loading rate effect. It is found that the excavation damaged zone (EDZ) of the tunnel is dominated by the shear failure, and the number of the shear failure zones increases with the decrease of k0. The EDZ of tunnels after transient excavations varies from ring-shape to egg-shape and X-type shear with the decrease of k0. The evolution of the EDZ induced by the transient unloading is associated with k0, i.e., the shear failure of surrounding rocks mainly occurs in the stress redistribution stage under high k0 (1.0–0.7), while the dramatic destruction of surrounding rocks is more prone to occur after the transient unloading process when k0 ≤ 0.6.
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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 | 0.000 |
| 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".