Evaluating Minimum Support Pressure for Tunnel Face Stability: Analytical, Numerical, and Empirical Approaches
Bibliographic record
Abstract
Tunneling in loose soil and urban areas presents numerous challenges. One effective solution is the use of Earth Pressure Balance Shields (EPBSs). Maintaining the correct balance of pressure at the tunnel face is critical, as applying too little pressure can cause a collapse, while excessive pressure may result in a blow-out. Therefore, a key aspect of using EPBSs in urban environments is determining the optimal pressure required to stabilize the tunnel face, taking into account the existing soil in the excavation chamber and controlling the screw conveyor’s rotation rate. This study focuses on a section of the second line of the Tabriz subway to evaluate the minimum pressure needed for tunnel face stability using empirical, analytical, and numerical approaches. The analytical methods involve evaluating the limit equilibrium of forces and considering soil buckling due to overburden, while the numerical methods employ 3D finite element analysis. Additionally, a sensitivity analysis of the parameters affecting the required pressure was conducted and compared across the three approaches. The results revealed that the formation of a pressure arch mitigates the full impact of overburden pressure on the tunnel face. For soil cohesion values below 20 kPa, the numerical results aligned well with the empirical and analytical findings. For a tunnel depth of 22.5 m and a water table 2 m below the surface, the estimated minimum pressure ranged from 150 to 180 kPa. Moreover, the analytical methods were deemed more suitable for determining the required support pressure at the tunnel face. These methods considered wedge and semi-circular mechanisms as the most probable failure modes. Also, for cohesive ground, the pressure from the finite element analysis was found to be almost always equal to or greater than the values obtained with the analytical solutions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".