SANISAND-MSf: a sand plasticity model with memory surface and semifluidised state
Bibliographic record
Abstract
A new constitutive model for sand is formulated by incorporating two new constitutive ingredients into the platform of a reference critical state compatible bounding surface plasticity model with kinematic hardening, in order to address primarily the undrained cyclic response. The first ingredient is a memory surface for more precisely controlling stiffness affecting the plastic deviatoric and volumetric strains and ensuing excess pore pressure development in the pre-liquefaction stage. The second ingredient is the concept of a semifluidised state and the related formulation of stiffness and dilatancy degradation, aiming at modelling large shear strain development in the post-liquefaction stage. In parallel, a modified flow rule aimed at providing a better description of non-proportional monotonic and cyclic loading is introduced. With a single set of constants, for which a detailed calibration procedure is provided, this new model successfully simulates undrained cyclic torsional and triaxial tests with different cyclic stress ratios, separately for the pre- and post-liquefaction stages, as well as liquefaction strength curves based on [Formula: see text] and shear strain criteria for initial liquefaction. The successful reproduction of the sand element response under undrained cyclic shearing contributes to future applications in realistic and thorough seismic site response analysis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".