Effect of water pressure on permeability of foam-conditioned sands for EPB shield tunneling
Bibliographic record
Abstract
Foam conditioning is a widely adopted technique in earth pressure balance (EPB) shield tunneling for the purpose of reducing sand permeability and preventing water spewing. The permeability of foam-conditioned sands differs from that of natural sands due to the presence of foam bubbles. This study investigated the effect of water pressure on the permeability of foam-conditioned sands using novel laboratory permeability tests. The water pressure, for the first time, is decoupled with the hydraulic gradient, owing to a newly developed permeameter with the controllable downstream hydraulic pressure in the laboratory. The results show that the permeability is significantly affected by the water pressure, and the effect is also predominantly dependent upon the foam injection ratio. The initial hydraulic conductivity increases with the increasing water pressure, while the initial stable period duration decreases. The water-plugging structure formed by foam bubbles and sand particles is prone to be damaged under high water pressure due to the shrinkage of foam bubbles. This means that the existing permeability tests with low water pressure underestimate the permeability of foam-conditioned sands. The underlying mechanism of water pressure in modifying the permeability of foam-conditioned sands is also examined from a particle-scale perspective.
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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 | 0.001 |
| Open science | 0.000 | 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".