Numerical modeling of LEAP-2022 dynamic centrifuge tests adopting a multi-surface plasticity model
Bibliographic record
Abstract
This study presents the numerical results of a series of laboratory and dynamic centrifuge tests conducted by the team at Universidad del Norte, as part of the LEAP-2022 project. The soil's mechanical behavior was simulated using a pressure-dependent multi-surface plasticity constitutive model, which was carefully calibrated based on cyclic soil tests performed on Ottawa F-65 sand. These tests covered a wide range of initial densities, initial effective stresses, and cyclic stress ratios. The comparison between laboratory and numerical element tests revealed that the adopted constitutive model reasonably replicated most features of the material's undrained cyclic response, including liquefaction occurrence and the progressive development of double-amplitude permanent shear strains. The calibrated constitutive model was then used to blindly predict the dynamic behavior of centrifuge experiments composed of a sheet pile-soil system using the OpenSees finite elements software framework; these simulations are referred to as Type-B predictions. The numerical simulation showed that the model provided reasonable representation of soil responses in terms of accelerations and pore water pressure build-up; however, the simulations consistently overpredicted the displacements of the sheet piles. Therefore, based on the centrifuge experimental results, minor adjustments of the material parameters were performed, and the centrifuge tests were re-simulated; these simulations are referred to as Type-C predictions. The comprehensive evaluation exposed both the strengths and weaknesses of the modeling approach for the simulation of liquefiable deposits. Despite the discrepancies in sheet pile displacement, the study instills confidence in the model's applicability to liquefaction-related projects with similar conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".