Prediction of hydraulic conductivity parameters of slurries from centrifuge consolidation settlements
Bibliographic record
Abstract
Slurry materials such as mine tailings and dredged clays undergo large, self-weight consolidation settlements. Compressibility and hydraulic conductivity characteristics of such slurries are essential for the estimation of their settlements and settlement rates for the safe maintenance of the storage facilities. These two constitutive functions of slurry materials are frequently estimated from laborious large-strain consolidation tests. Centrifuge consolidations tests are receiving great attention, therefore, to circumvent the time-consuming consolidation tests in the 1 g environment. However, the inverse analysis of settlement–time data for the estimation of compressibility and hydraulic conductivity functions is computationally expensive due to the requirement of a large number of forward analyses and thus is not available yet for the centrifuge environment. In this work, for the first time, two existing back analysis procedures for 1 g column tests were explored for predicting the hydraulic conductivity parameters of several slurry materials from the centrifuge test data. These procedures require well-defined and time-invariant compressibility functions of the slurry materials. These methods need only time–settlement data from a single centrifuge test, and do not depend on the excess pore pressure profiles. A finite difference solution of the analytical model for finite strain consolidation behavior of samples in centrifuge environment was utilized for the forward analysis. Four simulated synthetic temporal settlement curves and four centrifuge consolidation test data from the literature were utilized for validation of the studied methods. The methods were simple, computationally inexpensive due to the requirement of only a smaller number of forward analyses, and effective in accurately predicting the hydraulic conductivity constitutive parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".