Optimizing Hydraulic Conductivity Estimation from In Situ Measurements Using Artificial Data
Bibliographic record
Abstract
ABSTRACT Determination of the hydraulic conductivity-void ratio function (k-e) of sedimenting soft soils can be performed using in situ density and pore-pressure measurements in column tests, centrifuge, and in pilot or field deposits without resorting to back-calculation using numerical methods. The disadvantage of back-calculation is that it requires knowledge of the compressibility curve, which is known to change over time in soft soils. Direct determination of k-e has been employed in previous studies, essentially applying Darcy’s law to find k from fluxes and gradients determined by various interpolation strategies of the known density and pore-pressure profiles. This technique is sometimes called the instantaneous profile method (IPM). This paper examines this technique rigorously through both (1) generating artificial data from a large-strain consolidation model and applying the IPM to this artificial data to attempt to recover the consolidation properties inputted into the model, and (2) by comparison with known case studies in the literature. This exercise provides clear requirements and limits on data needs as well as data interpolation methods. It was found that a substantial reduction in the scatter of the estimated k-e values was achieved when a local dependency of k on void ratio was assumed only for the variation of k across an IPM element: k∼eb. This assumption of local variation did not inhibit IPM from regenerating the global hydraulic conductivity function, even if the power of this global function was very different from the assumed form of the local variance. Screening out fluxes generated by measurement noise also improved the results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".