Soil-water retention curve model for fine-grained soils accounting for void ratio–dependent capillarity
Bibliographic record
Abstract
This paper presents a soil-water retention curve (SWRC) model for fine-grained soils. Compared with existing studies, the proposed model accounts for the distinct roles of the volume change of soils on capillarity and adsorption mechanisms. The capillary water is described by a relation that includes the characteristics of the pore-size distributions as parameters, while the absorbed water is modeled by a novel proposition that both considers the phenomenon of capillary condensation and allows for the decoupling between the degree of capillary and adsorptive saturation. Based on this feature, the void ratio effects are considered in a way in which they only affect capillary water, i.e., consistent with how volume change influences soil microstructures. The relative contributions of void ratio effects and hydraulic hysteresis on the path- and history-dependence of a SWRC in Sr–s–e space, where Sr is degree of saturation, s is matric suction, e is void ratio, for deformable unsaturated soils are examined. The significance of discriminating the effects of volume change on capillary and adsorptive water is illustrated by applying the SWRC model to computing the shear strength of unsaturated soils with different void ratios. The model performance is assessed by comparing against test data reported for four types of fine-grained soils in the literature and those tested for natural loess in this work.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".