Comparison Between NorSand Cambridge Type and SaniSand Type Models in Simulating Sand Behavior under Monotonic Loading
Bibliographic record
Abstract
NorSand Cambridge type models employ a single yield surface plasticity with an associated flow rule, whereas SaniSand type models are formulated using bounding surface plasticity with a non-associated flow rule.Despite their distinct formulations, their approaches to simulating the monotonic behavior of sand rely on similar concepts.Both models measure the distance from the current stress condition to a specific moving target toward critical state in the stress space to calculate their hardening behavior.This paper presents how these entirely different frameworks share this similar concept in simulating the monotonic behavior of sand and shows a comparison of their modeling abilities.RÉSUMÉ Les modèles NorSand de type Cambridge utilisent une unique surface de plasticité avec une règle d'écoulement associée, tandis que les modèles de type SaniSand sont formulés en utilisant une surface limite et une règle d'écoulement nonassociée.Bien que leurs formulations diffèrent, leurs approches pour modéliser le comportement monotonique des sables se reposent sur des concepts similaires.Les deux types de modèles mesurent une distance entre les conditions actuelles de contraintes, et un état de contrainte cible qui si déplace vers l'état critique dans l'espace des contraintes, afin de calculer le comportement d'écrouissage.Cet article présente comment ces cadres conceptuels partagent ce concept similaire pour la simulation du comportement monotonique des sables, et montre une comparaison des capacités de modélisations des modèles.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".