Robust methods to estimate large-strain consolidation parameters from column experiments
Bibliographic record
Abstract
New approaches are proposed for robustly determining the compressibility and permeability functions for large-strain consolidation (LSC), using limited measurements from one-dimensional tests, such as column or centrifuge experiments. These new methods are developed from several new findings reported in this paper, including (i) new analytical solutions that relate parameters of compressibility functions to the final density profile, (ii) analytical proof of the independence of the shape of the settlement curve from the parameter M, where the permeability or hydraulic conductivity k is given by Me P , e is void ratio, and M and P are material-related constants; and (iii) that when the same k function is employed, for the case of one-dimensional settlement, the optimal values of M and P are located on a unique straight line in the P–lnM space. The third finding was determined from optimization analysis of thousands of LSC simulations. Using different combinations of these new findings, three new methods are developed to estimate the permeability function. The efficiency, robustness, and accuracy of the three methods are investigated by their application to four column tests from the literature. The simplest of the three methods requires only two LSC analyses plus several hand-calculations, demonstrating strong potential for practical use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".