Model for the Prediction of the Strain-Softening Stress–Strain Relationship for Unsaturated Soils Associated with Large Shear Deformation
Bibliographic record
Abstract
Local shear zones typically develop in a progressive manner in geostructures constructed with or within unsaturated soils due to the influence of wetting and drying cycles. The unsaturated shear strength of soils will reduce associated with large shear deformation in these shear zones over a long period of time (i.e., strain-softening behavior) and can significantly influence the long-term performance of the geostructures. In this study, a disturbed state concept (DSC) model is proposed for predicting the strain-softening stress–strain relationship of unsaturated soils associated with large shear deformation. In the DSC-based model, the apparent stress–strain relationship was expressed as a weighted average of a hyperbolic stress–strain relationship at the relative intact state and a horizontal stress–strain relationship at the fully adjusted state (i.e., the shear strength is constant at the residual value at the fully adjusted state) with a disturbance function as the weight. In addition, a formulation approach of the disturbance function was introduced. The proposed DSC-based model includes several mechanical parameters that are predicted by extending existing models in literature based on two stress state variables. The step-by-step procedures for determining the DSC model parameters are succinctly described. In addition, two sets of suction-controlled ring shear tests were conducted based on axis-translation technique on two different soils including a coarse- and a fine-grained unsaturated soil, respectively. The comparisons between the measured stress–strain curves and the predicted results using the proposed model suggested that the proposed DSC-based model provides a reasonable prediction for the strain-softening stress–strain relationship for different soils associated with 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".