Fractional-Order Derivative, Bounding Surface Plasticity–Based Anisotropic NorSand: Formulation and Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The NorSand model is used for the constitutive modeling of granular materials, particularly loose tailings, as it incorporates the material’s state in its formulation. However, there are some drawbacks such as not considering the loading and the material fabric anisotropy, inaccurate modeling of the stress–dilatancy behavior of the medium-dense to dense granular materials under undrained cyclic shear conditions, not addressing the actual directions of principal stresses during cyclic torsional shear tests dealing with pure rotation, and not modeling the inelastic nature of unloading and subsequent reloading phases during undrained cyclic shearing. This paper introduces a new stress–dilatancy parameter based on the fractional plasticity and modifies NorSand to account for the anisotropy in the main framework of the bounding surface plasticity and employs the single stress point mapping rule to update unloading and reloading stresses based on kinematic hardening parameters. It also benefits from the nonassociative nature of the applied fractional derivative to the dilatancy parameter to develop the plastic flow in the direction, which is not mandatorily perpendicular to the yielding surface. The developed constitutive model is then validated against experimental data for the monotonic triaxial cases at both loose and dense states; drained triaxial cyclic, undrained cyclic simple shear, and cyclic torsional shear test cases and good prediction accuracies are observed. The sensitivities of the modified NorSand model to the hardening parameter and the original NorSand model to the softening parameter are compared to assess the efficiency and suitability of these parameters in predicting the material response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it