Pore Water Response of Seabed Soils during Multi-Hazards: Model Validation
Bibliographic record
Abstract
Coastal structures are prone to multi-hazard loading; for instance, earthquake ground shaking, and subsequent tsunami loading. The instability of seabed sand caused by residual and momentary liquefaction, as well as large bed shear stresses, are primary concerns for engineers. The foregoing soil response is related to the soil’s pore water pressure response during and after earthquake shaking, i.e., before the tsunami attack. Our objective is to develop and validate a one-dimensional numerical model in OpenSees to simulate the excess pore water generation and dissipation during and immediately after earthquake loading. The model consists of four node elements and the pressure dependent multi-yield material model, and we validate the model through comparisons with LEAP-GWU-2015 centrifuge test data. In addition, we perform a convergence study to examine sensitivity to mesh size, time step, and convergence criteria. The results show that the numerical model captures the magnitude of the excess pore water pressure and its long-term asymptotic dissipation response well, and the permeability has a large impact on the excess pore water pressure response, as expected. Our work gives reference parameters for the Ottawa-F65 sand used in the LEAP-GWU-2015 centrifuge tests. The results indicate that our model can be adopted for future research on the liquefaction potential for coastal structures during complex, multi-hazard loading scenarios.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".