Insights into hydraulic gradients on slurry infiltration characteristics in saturated sands
Bibliographic record
Abstract
In slurry shield tunneling, pressurized slurry infiltrates into the ground ahead of a tunnel face to generate a dynamic heterogeneous filter cake, over which the hydraulic gradients fluctuate due to cutting disturbances. This study aims to experimentally investigate the effects of hydraulic gradients on slurry infiltration characteristics by conducting modified infiltration column tests on four sandy soils with different particle-size distributions, subjected to four hydraulic gradients under fixed slurry pressure conditions. The results showed that the slurry infiltration process could be divided into three stages based on the Peclet number: the mud spurt, the deep-bed filtration, and the filter cake formation. Across the tested hydraulic gradients, both the filter cake formation time and the mud spurt duration increased linearly with the soil particle size d15, exhibiting greater sensitivity to lower hydraulic gradients. It was found that the final slurry infiltration distance initially increased and then decreased as the hydraulic gradient raised. Additionally, the critical hydraulic gradient corresponding to the maximum final slurry infiltration distance decreased with d15. The slurry infiltration distances and the deposition of bentonite particles were further validated through visualized infiltration tests and the measured porosity profiles. Furthermore, the filter cake exhibited a hydraulic conductivity ranging between 1 × 10–9 and 8 × 10–9 m/s, inversely proportional to the hydraulic gradient.
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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".