A model to predict the water retention curve from basic geotechnical properties
Bibliographic record
Abstract
The water retention curve (WRC) has become a key material function to define the unsaturated behavior of soils and other particulate media. In many instances, it can be useful to have an estimate of the WRC early in a project, when little or no test results are available. Predictive models, based on easy to obtain geotechnical properties, can also be employed to evaluate how changing parameters (e.g., porosity or grain size) affect the WRC. In this paper, the authors present a general set of equations developed for predicting the relationship between volumetric water content, θ, (or the corresponding degree of saturation, S r ) and suction, ψ. The proposed model assumes that water retention results from the combined effect of capillary and adhesion forces. The complete set of equations is given together with complementary relationships developed for specific applications on granular materials and on fine-grained soils. It is shown that the model provides a simple and practical means to estimate the water retention curve from basic geotechnical properties. A discussion follows on the capabilities and limitations of the model, and on additional tools developed to complement its use. Key words: water retention curve, unsaturated soils, prediction, porosity, grain size, liquid limit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".