Bounding surface SANICLAY plasticity model for cyclic clay behavior
Bibliographic record
Abstract
SUMMARY Natural clays are anisotropic in their in situ state and have an undisturbed shear strength in excess of the remolded strength. In addition, most of the structures founded on clay deposits must be designed to withstand cyclic loads such as earthquakes or ocean waves. When subjected to cyclic loadings, clay exhibits complex response. A realistic modeling of clay response under irregular loading requires an appropriate description of the stress‐strain relationship. This paper extends the formulation of a Simple ANIsotropic CLAY plasticity (SANICLAY) model by incorporation of a bounding surface formulation for simulation of clay response under cyclic loading. The most important elements of the proposed formulation are incorporation of bounding surface plasticity concept with proper repositioning of the projection center and adoption of a new damage parameter. These have led to significantly improved simulation of clay response in cyclic loading. The model is developed with the aim of maintaining the simplicity and yet including an adequate level of sophistication for successful modeling of the key features of clay response. The formulation is presented in detail followed by validation of the model demonstrating its capabilities in capturing a number of important characteristic features of clay response in cyclic loading. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".