Image-based investigation of deformation and clogging behavior of dredged soil under varying vacuum pressure gradients
Bibliographic record
Abstract
Step vacuum preloading, an enhanced vacuum preloading method, has been developed to mitigate clogging-induced poor improvement of dredged soil. However, the deformation and clogging behaviors under varying vacuum pressure gradients remain insufficiently understood. This study investigates the deformation and clogging behavior of dredged soils under varying vacuum pressure gradients (10, 20, 40, and 80 kPa) through four model tests. Particle image velocimetry technology was employed to capture soil displacement fields and analyze strain evolution. The test results indicate that during the first vacuum stage, all tests exhibited horizontal compression near the prefabricated vertical drain and vertical compression in the far field. At a gradient of 10 kPa, deformation shifted to vertical compression with horizontal extension in subsequent stages, whereas higher gradients largely preserved the initial pattern. The clogging zone primarily developed during the first vacuum preloading stage, with its thickness correlated with the initial vacuum pressure. Lower pressure gradients effectively reduced horizontal soil particle migration and compression, thereby mitigating clogging. In addition, empirical equations were proposed to describe the development of the clogging zone under different vacuum pressure gradients. These findings provide direct visual evidence of the spatiotemporal evolution of clogging and offer references for optimizing theoretical consolidation analyses.
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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 | 0.001 | 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".