Prediction of coupled hydromechanical behavior of unsaturated soils based on seasonal variations in hydrologic conditions
Bibliographic record
Abstract
Seasonal variations in hydrologic conditions greatly influence the hydromechanical properties of unsaturated soils. There are several models available to estimate shear strength of unsaturated soil under various hydrologic conditions. However, many of these existing models provide little to no data regarding the deformations associated with wetting and drying of unsaturated soils. The incremental hydromechanical behavior for an unsaturated soil is generally described by a constitutive framework. In this study, a modified Sheng, Fredlund, and Gens (SFG) soil constitutive model was utilized with in situ hydrologic data to simulate fully coupled mechanical behavior for an unsaturated slope over different hydrologic events. This paper also presents a hydrological prediction approach to estimate hydrologic characteristics of unsaturated soils over several wetting and drying events using only the soil-water characteristics parameters of the main drying curve. The proposed approach provides a possibility of describing long-term hydrologic behavior of unsaturated soils by means of a limited amount of in situ hydrologic data. The outcome of this study provides geotechnical engineers with the capability of estimating deformational behavior of unsaturated soils under various real-time rainfall–evapotranspiration conditions and implementing more effective emergency planning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".