Study on the failure mechanism of a tunnel reinforced by a heterogeneous grouting layer based on the upper bound method
Bibliographic record
Abstract
During shield tunnel construction, the proper application of backfill grout can effectively prevent tunnel instability and excessive ground settlement. Studying the failure mechanisms of grouting-reinforced tunnels can ensure engineering safety. However, few scholars have investigated the stability of tunnels reinforced by grouting layers. In this work, the upper bound finite element method (UBFEM) is employed to investigate tunnel stability by considering homogeneous grouting and eccentric grouting layers, and the impacts of different parameters on the grouting layer properties are evaluated. To reduce the difficulty of using the UBFEM, a simplified rigid block upper bound method is proposed. This method can effectively reflect the instability mechanism of a tunnel reinforced by grouting, and it is convenient for practitioners to use. The results indicate that, compared with homogeneous grouting layers, tunnels reinforced with eccentric grouting experience a 30.8% smaller plastic failure area, a 16.8% greater ultimate support force coefficient, and an approximately 9.0% greater maximum vertical settlement coefficient due to tunnel instability caused by eccentricity. The proposed method provides a quantitative evaluation index for tunnel stability with eccentric grouting layers. The findings are synthesized into nondimensional charts and tables for practical application by industry professionals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".