Improved Predictions of Liquefaction-Induced Lateral Spreading with SANISAND-MSf: Incorporating Effects of Static Shear Stress
Bibliographic record
Abstract
This study presents an improved numerical model for simulating cyclic liquefaction-induced lateral spreading. The enhancement is achieved by incorporating the effects of static shear stresses on the undrained cyclic shearing of sands in the SANISAND-MSf constitutive framework. This improvement allows the model to capture the undrained response of sands for different densities and stress states. The model is implemented in the finite element-based platform OpenSees for application to dynamic problems. The performance of the model is assessed by simulating cyclic direct simple shear tests and centrifuge experiments of mildly inclined liquefiable sand deposits, specifically designed to study lateral spreading. The results of the simulations demonstrate the effectiveness of the SANISAND-MSf model in replicating the liquefaction response observed in the centrifuge experiments, including the triggering of cyclic liquefaction and the subsequent development of large shear strains, which are key drivers of lateral spreading. The improved predictive capabilities of this constitutive model, particularly through the incorporation of the effect of static shear stresses, can help to better assess and mitigate the risks associated with liquefaction-induced lateral spreading during earthquakes.
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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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".