Model for predicting the variation of shear stress in unsaturated soils during strain-softening
Bibliographic record
Abstract
Several of the geotechnical structures constructed with unsaturated soils undergo a large deformation prior to reaching failure conditions (e.g., progressive failure of a soil slope). During this process, the shear stress in soils typically increases initially and then reduces with an increase in the shear strain. The prediction of the stress–strain relationship is critical for reasonable interpretation of the mechanical behavior of those geo-structures that undergo large deformation. This paper introduces a model based on the disturbed state concept (DSC) to predict the variation of shear stress in unsaturated soils during the strain-softening process under consolidated drained triaxial compression condition. In this model, the apparent stress–strain relationship is formulated as a weighted average of a hyperbolic hardening response extending the pre-peak state stress–strain curve and a linear response extending the critical state stress–strain curve with an assumed disturbance function as the weight. The prediction procedure is described in detail and the proposed model is validated using several sets of published data on unsaturated soils varying from coarse- to fine-grained soils. Finally, a comprehensive error analysis is undertaken based on an index of agreement approach.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".