Tunnel face instability in composite strata during shield tunneling: a case study
Bibliographic record
Abstract
Accurately predicting tunnel face stability during shield tunneling in composite strata remains a significant challenge due to the pronounced heterogeneity and contrasting mechanical behaviors of stratified geological formations. To address this issue, this study establishes a refined coupled FDM–DEM numerical model of a tunnel boring machine (TBM), capable of realistically capturing the excavation-induced disturbance process. A parametric study was conducted to evaluate the effects of the composite ratio and the chamber fill ratio on tunnel face behavior, including failure zone development, TBM thrust, and torque evolution. The results indicate that tunnel face instability evolves through a typical three-stage process: initial disturbance, failure development, and failure propagation with arch reformation. The extent of failure is positively correlated with the composite ratio and negatively correlated with the chamber fill ratio. A higher composite ratio amplifies disturbance effects due to increased exposure to weaker strata, while greater chamber fill enhances face support and suppresses failure propagation. These findings enhance the understanding of tunnel face failure mechanisms in composite strata and support the rational design of shield tunneling parameters under complex geological conditions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".