Full-scale field study on cyclic vacuum preloading for dredged slurry-reclaimed ground
Bibliographic record
Abstract
Vacuum preloading with prefabricated vertical drains (PVDs) is a widely employed method for the treatment of slurry ground. Conventionally, a constant vacuum pressure of 85 kPa is maintained throughout the treatment, leading to significant energy consumption due to continuous vacuum pump operation. Moreover, this approach exacerbates clogging issues inherent in PVD treatment of clayey slurry when compared to a lower loading pressure. To address these challenges, this study adopted a cyclic vacuum loading mode, which not only reduced energy use but also mitigated the clogging problem. By periodically switching the vacuum pump on and off, the under-membrane vacuum pressure varied cyclically within a predefined range. Initially, the effectiveness and energy-saving potential of this cyclic vacuum loading mode were validated through laboratory tests on PVD treatment of slurry. Subsequently, a full-scale field trial on cyclic vacuum loading was conducted on dredged slurry-reclaimed ground. The cyclic vacuum pressure in the trial ( P max = 80 kPa, P min = 50 kPa) was implemented using 15 min pump-on and 45 min pump-off cycle. Key treatment effects including the pore water pressure dissipation, ground settlement, water discharge, and power consumption were monitored. In situ vane shear and plate load tests were performed post-treatment to assess ground improvement. The results demonstrated that cyclic vacuum preloading reduced energy consumption by 74.4% while achieving 88.4% of the effects of traditional full vacuum loading.
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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) | 0.001 | 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 | 0.001 |
| 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".