Implementation of a Nonlinear State–Dilatancy Law in the NorSand Model
Bibliographic record
Abstract
The original NorSand (ONS) state-dilatancy law (ONS-SDL) presents challenges when trying to make changes in the formulation of the critical state line or stress–dilatancy rule and the associated yield surface. Additionally, using this law in its current form limits the model's applicability to a specific range of initial state parameters. Due to these limitations, using a higher state–dilatancy parameter for loose samples to improve the undrained response is often not possible. This paper provides a review of the ONS-SDL, and the requirements of a state–dilatancy law in Cambridge-type models in a more explicit way. A comprehensive analysis is then conducted to examine the effects of the chosen law on the model's formulation, its range of applicability, and its effectiveness in simulating undrained responses of loose samples. To address the limitations, a new nonlinear state–dilatancy law is introduced, offering improved responses and enhanced flexibility. This not only improves the model's performance but also allows for the utilization of different stress–dilatancy rules in future models, providing greater flexibility and adaptability. The improvements made by the new formulation have been highlighted by demonstrating the modified version's capabilities in simulating the mechanical behavior of two different sands.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".