Statistical assessment of soil-water characteristic curve models for geotechnical engineering
Bibliographic record
Abstract
A number of empirical equations have been proposed for the soil-water characteristic curve. A nonlinear, least squares method was used to determine best-fit parameters for several empirical equations that were best-fit to 230 water content versus soil suction data sets. In addition, two proposed correction methods to accommodate high soil suctions up to 1 000 000 kPa were applied to the various soil-water characteristic curve equations. The data sets of water content versus soil suction were arranged into one of the USDA soil classifications based on their relative amounts of sand, silt, and clay (only eight soil classifications had sufficient data for later analysis). The quality of fit for each model was compared using the Akaike Information Criterion. A series of conclusions were arrived at regarding (i) the relationship between two- and three-parameter equations, (ii) the relationship between exponential and sigmoidal type equations, and (iii) the value of correction factors for the high soil suction range.Key words: soil-water characteristic curve, unsaturated soil, soil suction, regression analysis, SWCC models, Akaike Information Criterion.
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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 | 0.068 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".