Probabilistic Seismic Performance Assessment of an RC Bridge Considering Corrosion‐Affected Bond‐Slip and Steel Bar Buckling
Bibliographic record
Abstract
ABSTRACT Reinforced concrete (RC) bridges are designed to remain safe and functional for their lifetime, during which the impacts of aging may result in performance degradation. Steel bar corrosion is one of the most common causes of structural performance degradation in RC structures subjected to earthquakes in seismic‐prone areas. Therefore, to ensure the adequate seismic performance of RC bridges over the course of their life, it is necessary to investigate the effect of corrosion on seismic performance prediction. To this end, this research work uses the recently developed tools for seismic performance assessment, including advanced finite element (FE) modeling strategies for corroded RC structures. The newly developed advanced FE modeling strategy can capture the corrosion impact on bonding between steel bars and surrounding concrete, as well as the vulnerability of steel bars to buckling, in addition to other effects on the steel bar cross‐sectional area, cover concrete spalling, and confinement level for core concrete. Using these newly developed strategies, the seismic performance of an RC bridge, impacted by corrosion over the course of its life, is examined in a probabilistic framework. In particular, it has been demonstrated that the conventional FE modeling approach, which neglects the corrosion‐affected bond‐slip and steel bar buckling, would lead to underestimated seismic risk for corroded RC bridges, specifically the seismic risk associated with the post‐peak behavior.
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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".