Consolidation of clay slurry under very low stresses: from novel oedometer apparatus invention to nonlinear consolidation characteristics and finite strain modelling
Bibliographic record
Abstract
This study first invents a novel oedometer apparatus for clay slurry, featuring a lightweight acrylic loading cap, a noncontact laser displacement sensor, and a 1:1 dead-weight loading system to improve traditional consolidation devices. The novel apparatus is then used to examine two clays: Hong Kong Marine Deposit and Kaolin clay. The loading with a minimum stress of 0.025 kPa is applied on samples with a maximum initial water content exceeding 9 times the liquid limit. Results demonstrate the “S” shape compression curves influenced by initial water contents, and the power-type relationships between permeability coefficient and void ratio. Empirical equations are obtained to determine the yield stress point based on initial water content and liquid limit. Higher initial water contents increase compression parameters (e.g., recompression index, Cr; compression index, Cc; and creep index, Cα), though Cr/Cc and Cα/Cc are almost in the normal range. The Cc of Kaolin clay with initial water contents above 3.5 times the liquid limit is significantly relevant to effective stress. Finally, a nonlinear creep model is enhanced and integrated into the finite strain consolidation equations, effectively simulating the oedometer tests and a self-weight consolidation test of clay slurry with nonlinear consolidation characteristics.
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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 | 0.001 |
| 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".