A new approach for determining compressibility and permeability characteristics of dredged slurries with high water content
Bibliographic record
Abstract
In the vacuum treatment of dredged slurries via prefabricated vertical drains (PVDs), both filtration and consolidation may happen to dredged slurries when subjected to the vacuum gradient. Similar to the consolidation, the filtration may provide equally useful information on interpreting the constitutive behavior of the slurry. In this paper, a new approach utilizing the filtration process is devised to evaluate the constitutive behaviors of dredged slurries. The approach is composed of a vacuum filtration device and an iterative curve fitting algorithm. Two types of tests, namely step-vacuum filtration and constant-vacuum filtration followed by compression test, must be conducted on the slurry specimens, and only the time variations of the filtrate discharge should be recorded. On the basis of the recorded data, the curve fitting algorithm is adopted for determining the parameters of the constitutive equations. The effectiveness of the proposed approach is verified through comparing the obtained compressibility and permeability of the slurries with the data provided by the oedometer test and with the empirical relationships from different sources. The comparisons demonstrate that the constitutive relationships determined by the present approach can predict the compressibility and permeability characteristics of dredged slurries with an acceptable degree of accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".