Life‐cycle seismic fragility of a cable‐stayed bridge considering chloride‐induced corrosion
Bibliographic record
Abstract
Abstract The coastal or sea‐crossing bridges located in seismic regions are experiencing long‐term chloride‐induced corrosion in their life cycles and have a high risk of suffering from strong earthquakes. Thus, the fragilities of these bridges must be properly evaluated under the multiple hazards of corrosion and earthquake. Conventional fragility assessment requires expensive computational efforts despite extensive applications in previous research. To this end, this paper proposes a life‐cycle fragility analysis framework based on the endurance time method to investigate the deterioration impact on the seismic fragility of bridges. An example sea‐crossing cable‐stayed bridge is utilized as a case study and modeled by OpenSees considering different service years. The life‐cycle fragility curves of the example bridge are generated using the artificial endurance time series. Fragility analysis results show that the proposed method is capable of assessing the life‐cycle seismic fragility of the bridge with high efficiency. The bearing shows a higher damage probability than the pylon and pier. As the service time increases, the structural deterioration has a marginal impact on the system fragility of the bridge, while the component fragility varies across different components. The structural deterioration results in beneficial effects on the pylon and the bearing at the pylon but exerts adverse effects on the pier and the bearing at the pier.
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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 | 0.001 |
| 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 | 0.001 | 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".