Novel anisotropic elastoplastic modeling of <i>K</i> <sub>0</sub> -consolidated sands
Bibliographic record
Abstract
Accurately defining the yield surface is a cornerstone of constitutive modeling for sands, as it governs the onset and evolution of plastic deformation under various stress paths. This study proposes and compares some new anisotropic yield functions for K 0 -consolidated sands by introducing two shape parameters ( n and χ) and one anisotropic state variable ( α). For the proposed yield surfaces, the elastoplastic constitutive model is developed within a bounding surface plasticity framework, using a simplified form of the yield function as the plastic potential. Both inherent and induced anisotropy are captured through kinematic hardening and rotational evolution mechanisms. The performance of the proposed yield surfaces is evaluated through two complementary studies. First, the effects of degradation in n, χ, and α on model performance are systematically investigated. Second, a parameter sensitivity analysis is conducted to assess the influence of key model parameters. Results show that with increasing anisotropic consolidation, the proposed model, characterized by flexible shape control and anisotropic state representation, achieves significantly improved predictive accuracy compared to existing models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".