Three-dimensional tunnel face stability using a new heterogeneous dynamic filter cake
Bibliographic record
Abstract
In slurry-shield tunneling, a heterogeneous dynamic filter cake occurs due to the interaction between slurry infiltrating and tool cutting, whose influence on tunnel face stability is not fully plumbed. Thus, a numerically based-limit analysis framework is proposed to assess the tunnel face stability considering a heterogeneous dynamic filter cake. The numerical model with a heterogeneous dynamic filter cake, which has spatio-temporally variable permeability coefficients according to cutter layouts, is built for solving a transient seepage flow. Numerically obtained seepage flow results are further incorporated into the three-dimensional (3D) rotational failure mechanism, so as to give an upper-bound estimation on the tunnel face safety factor and the slurry pressure transfer efficiency. The proposed method is validated by comparing with previous studies regarding the slurry pressure drop, slurry pressure transfer efficiency, and tunnel face safety factors. Parametric analyses are performed to examine the excess pore pressure distributions at the filter cake–soil interface, and discuss the influences of cutterhead rotation and excess slurry pressures on the tunnel face stability. The results show that the defined 3D pressure transfer efficiency is between the one-dimensional and the two-dimensional solutions published previously; the tunnel face stability reduces as cutterhead rotating and finally reaches a dynamic equilibrium state.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".