Formation mechanism of clogging of dredge slurry under vacuum preloading visualized using digital image technology
Bibliographic record
Abstract
Vacuum preloading combined with prefabricated vertical drains (PVDs) system has been widely used to improve the soft clay with high water content. Clogging is usually formed around the PVDs during the vacuum preloading, impeding the propagation of the vacuum pressure and slowing down the consolidation process. To understand the forming mechanism of the clogging, particle image velocimetry (PIV) technique and particle tracking velocimetry (PTV) technique were adopted in the model test of vacuum preloading test. Through this study, three stages can be identified from the results of water volume discharge rate and maximum displacements versus time. In the first stage, the soil around the PVD is horizontally consolidated, which leads to the rapid formation of clogging. In the second stage, the formation of clogging slows down due to the loss of vacuum pressure, which further reduces the drainage. In the third stage, the clogging tends to be stable, and the drainage consolidation rate is significantly reduced. PTV results show that there is a difference in the displacement of large and small particles during improvement. Two methods were proposed to estimate the thickness of clogging zone, reflecting a growing layer of clogging zone compressed around the PVD. This study provides new insights to investigate the formation mechanism of clogging during vacuum preloading test.
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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".