Pressure infiltration behavior and fluid loss of bentonite slurry: a comparative study of two bentonite slurries
Bibliographic record
Abstract
Bentonite slurry is frequently used to temporarily stabilize the excavation for slurry tunnel boring machines (TBMs) driving in permeable soils, such as sand and gravel. In this study, two types of bentonite slurries (BS1 and BS2) were subjected to a series of infiltration column tests and modified fluid-loss tests under various pressure levels. Monitoring of water discharge and pore pressures at different depths of the sand bed enabled the identification of two effective sealing patterns during infiltration: the formation of a filter cake and rheological blocking. BS1 exhibited a tendency to form a filter cake, which played a vital role in effectively transferring the applied pressure to the underlying soil skeleton. The application of higher pressure facilitated the rapid formation of a filter cake, resulting in a shorter time span for slurry invasion and minimizing fluid loss. On the other hand, rheological blocking was dominant when using BS2, and the maximum infiltration distance was found to linearly increase with the applied pressure. A comparison between the measurement and a simple prediction model derived from Darcy's law revealed an overestimation of the infiltration distance during slurry invasion. Furthermore, based on the modified fluid-loss test, higher pressure was found to densify the filter cake and result in lower hydraulic conductivity.
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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) | 0.000 | 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".