Evaluation of capillary water retention effects on the development of the suction stress characteristic curve
Bibliographic record
Abstract
The suction stress characteristic framework is a practical approach for relating the suction and the water-filled pore volume to the stress state of unsaturated soils. It predicts the effective stress by developing the suction stress characteristic curve from the soil-water retention curve. In this framework, the effective degree of saturation is usually calculated by the empirical water retention model of van Genuchten (published in 1980). In this paper, the use of a generalized soil-water retention model proposed by Lu in 2016, which differentiates the role of capillary and adsorption mechanisms, in the suction stress characteristic framework is studied. A redefinition of the effective degree of saturation is suggested, by choosing the retention state where capillarity approaches zero instead of the residual retention state. The validity of this assumption is examined using experimental data obtained by unsaturated shear strength and retention tests and datasets collected from the literature. The proposed definition is applicable for a variety of soils where capillarity is the dominant mechanism in producing suction stress within the range of suction 0–1500 kPa. In addition, it is observed that the generalized soil-water retention model presents a more realistic prediction of unsaturated shear strength compared with empirical water retention models.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".