Modification of SANISAND-MSf Model for Simulation of Undrained Cyclic Shearing under Nonzero Mean Shear Stress
Bibliographic record
Abstract
Adequate simulation of undrained cyclic shearing of sands subjected to nonzero mean shear stresses is crucial for understanding complex behaviors associated with seismic liquefaction. This study presents the modification of a constitutive model, SANISAND-MSf, specifically designed to capture the effects of nonzero mean shear stresses on cyclic shearing responses. The novel model introduces a new constitutive ingredient, the shear stiffness enhancement, which progressively adjusts plastic shear stiffness and dilatancy, allowing for the simulation of residual deformation accumulation in scenarios in which initial liquefaction is hindered. Additional modifications are applied to the existing memory surface and semifluidized state constitutive components to accommodate nonzero mean shear stresses and asymmetric loading conditions. The new version of the SANISAND-MSf model was validated against various laboratory experiments involving symmetric and asymmetric cyclic mobility and residual deformation accumulation responses. The generic nature of the new and modified ingredients allows their incorporation into any bounding surface plasticity model. This study contributes to the advancement of constitutive models for seismic liquefaction-induced displacements, enhancing predictive accuracy for geotechnical risk assessment and mitigation strategies.
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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 | 0.001 | 0.000 |
| 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".