Mechanisms and Modeling Methods of Strain-Softening Behavior of Unsaturated Soils
Bibliographic record
Abstract
Studies about the strain-softening behavior of unsaturated soils published in the literature during the past three decades are summarized under three categories; namely: (i) mechanical characteristics and micromechanisms, (ii) prediction models for shear strength, and (iii) numerical methods for modeling strain-softening behavior of unsaturated soils. In addition, the influence of the soil–water characteristic curve and time effects on the strain-softening behavior of unsaturated soils are discussed. Various experimental studies related to the strain-softening behavior of unsaturated soils are summarized to interpret the mechanical behavior characteristics and micromechanisms of the strain-softening under large shear deformation. The widely used empirical/semi-empirical prediction models from the literature for interpreting the peak, critical, and residual shear strength of unsaturated soils are comprehensively summarized considering the influence of soil fabric and water phase on the shear strength. Several numerical methods (i.e., conventional plasticity, bounding surface plasticity, disturbed state concept, and elasto-viscoplasticity) of modeling the strain-softening behavior of unsaturated soils are discussed, highlighting their strengths and limitations. The comprehensive details summarized in this paper related to the strain-softening behavior is valuable for the rational analysis and design of geostructures in unsaturated soils that undergo large shear deformation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".