Effect of water pressure on time-dependent permeability characteristics of sand conditioned with foam and bentonite slurry
Bibliographic record
Abstract
During earth pressure balance shield tunnelling in water-rich sandy ground, both foam and other conditioning agents, such as bentonite slurry, are injected to prevent water spewing. Permeability tests were conducted to investigate how water pressure affects the permeability of sand conditioned with foam and bentonite slurry. Experimental results demonstrate that increasing water pressure at the top and bottom of the specimen extends the initial stable period of the permeability coefficient, significantly slowing down its growth rate during the fast growth period. Soil grain migration was observed in specimens exposed to sufficiently high water pressure. During the slow growth period, the permeability coefficient decreased as water pressure increased, and this decrease rate correspondingly decreased. Under a consistent hydraulic gradient, increased water pressure led to enhanced stability of foam bubbles and extended the time-dependent curves for the permeability coefficient. Furthermore, the relationship between chamber pressure dissipation and foam stability was discussed during the standstill period of shield machines. To prevent water spewing, it is recommended to use the permeability coefficient of the muck at the outlet of the screw conveyor with the lowest water pressure as the evaluation index during permeability testing.
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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 | 0.000 |
| 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".