Effect of spacing of grid PHD on performance of combined PHD–PVD vacuum preloading method for treatment of clayey slurry
Bibliographic record
Abstract
A novel ground improvement method that combines grid prefabricated horizontal drains (PHDs) with prefabricated vertical drain (PVD) assisted by vacuum preloading is proposed for the beneficial reuse of dredged clayey slurry for reclamation purpose. To assess the feasibility of this innovative method, physical model tests are designed and conducted using high-water content Hong Kong marine deposits as the clayey slurry material. Furthermore, the impact of the spacing configuration of the grid PHD on the effectiveness of the proposed method is investigated through a series of model tests. A test without the installation of PVD was set, and in this case, two phases of vacuum preloading are applied sequentially through the PHD layer installed in stage. The other three tests involve three phases, with the addition of a vacuum preloading stage through PVD and variations in arrangement pattern of grid PHD layer. Results show that this proposed approach yields a final average undrained shear strength of soil of approximately 30 kPa, meanwhile reducing the average water content to around 50%. Furthermore, it is observed that decreasing the vertical spacing of grid PHDs results in growing final settlement. Reducing the horizontal spacing has less impact on the final settlement.
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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".