Effect of bentonite slurry on the function of foam for changing the permeability characteristics of sand under high hydraulic gradients
Bibliographic record
Abstract
During earth pressure balance (EPB) shield tunnelling in sandy ground, not only foam but also other conditioning agents need to be injected to reduce the permeability of muck and avoid water spewing out of the screw conveyor. Permeability tests were carried out to study the permeability characteristics of conditioned sand under high hydraulic gradients. A low bentonite slurry injection ratio (BIR) enhanced the workability of foam-conditioned sand. As the hydraulic gradient increased, the initial permeability coefficient of conditioned sand increased, and the initial stable period became shorter or disappeared. The BIR had a more significant effect on the permeability of conditioned sand than the foam injection ratio (FIR), and this effect gradually weakened as the hydraulic gradient increased. The initial permeability coefficient of the foam–bentonite slurry-conditioned sand decreased by approximately an order of magnitude compared with that of the foam-conditioned sand. With the addition of bentonite slurry, suitable sand conditioning can accept a higher water content (w) and lower FIR, resulting in suitable ranges of w and FIR that are more flexible. Finally, the mechanism of stabilizing foam under the action of bentonite slurry was discussed by considering the interaction between foam bubbles and fine particles.
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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 | 0.000 |
| 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".