Effect of thixotropy and segregation on centrifuge modelling
Bibliographic record
Abstract
In this paper, segregation related to the application of high centrifugal acceleration and the effect of thixotropy in modelling the settling behaviour of slurry/soft soils using a centrifuge are examined. Settling column and centrifuge tests were conducted on slurry/soft soils at various sand fines ratios (SFR) to define a segregation boundary using the ternary diagram. A segregation boundary based on centrifuge and settling column tests are established and the results indicate that the application of high gravity in centrifuge tests prompts and enhances segregation. It is also found that the segregation boundary of high gravity tests is a function of applied acceleration level, grain size of sand, the percentage fines and clay contents. The application of formulas that estimate the maximum size of sand that remain in suspension are evaluated and found conservative at high gravity tests. The shear strength of slurry/soft soils at various SFR and age were evaluated to assess the effect of non-gravity, time-dependent behaviour on strength and settling behaviour of slurry/soft soils. The results of the shear strength tests at various ages and composition indicate that strength gain is mainly due to thixotropy. The gain in shear strength due to thixotropy may not be properly modelled in centrifuge testing and may create difficulty in extrapolating centrifuge test results to prototype.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".