Seismic performance and seismic risk assessment of sheet-pile retaining wall subjected to liquefaction
Bibliographic record
Abstract
Earthquakes may trigger liquefaction in saturated sand, causing significant lateral deformations and posing a risk to retaining walls, especially in waterfront areas. To systematically investigate the impact of sand liquefaction on the seismic performance and risk of sheet-pile retaining walls, this study employs a novel multi-yield surface elasto-plastic constitutive model to simulate the liquefaction characteristics of saturated sandy soils during earthquakes. The dynamic coupling effect between pore water and soil particles is systematically considered, and a finite element model for a sheet-pile retaining wall test established is based on a centrifuge test configuration. The accuracy and effectiveness of the soil constitutive model and the finite element model are validated by matching the liquefaction strength curve of the Ottawa sand and centrifuge test results. A total of 100 ground motion records are selected as the base input to develop seismic fragility curves and seismic risk curves of the calibrated sheet-pile retaining wall model subjected to liquefaction-induced lateral spreading. In addition, the Cumulative Absolute Velocity (CAV) is identified as the optimal seismic intensity parameter based on the effectiveness, correlation, practicality, and proficiency. Ultimately, the influence of soil permeability on the seismic performance and seismic risk of the sheet-pile retaining wall is analyzed. Overall, the research outcome provides meaningful insights into the seismic design and mitigation measures of equivalent sheet-pile retaining structures in liquefiable sites.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".