Impact of component damage correlations on seismic fragility and risk assessment of multi-component bridge systems
Why this work is in the frame
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Bibliographic record
Abstract
Seismic fragility modeling of bridges has evolved from simplified system-level assessments to high-fidelity, component-based methodologies. However, a key challenge remains in accurately incorporating damage dependency among bridge components, which might influence high-resolution seismic risk estimates that rely upon damage simulations of each bridge component. While previous studies have explored demand and capacity correlations in various structures, a comprehensive framework integrating these dependencies within the component-based bridge fragility modeling approach remains absent. This study addresses this gap by introducing a refined methodology for modeling seismic damage correlations across bridge components and damage states. A correlation-based fragility modeling framework is proposed, leveraging joint probabilistic seismic demand models and a hierarchical capacity correlation structure. The framework is systematically compared against other correlation models, including fully independent, fully correlated, and partially correlated approaches. Using a four-span, multi-column reinforced concrete bridge as a benchmark, the influence of correlation modeling on key seismic risk metrics, such as bridge collapse fragility, repair costs, and recovery durations, is assessed. Results demonstrate that neglecting damage correlation, or treating it perfectly correlated, sometimes would lead to significant biases in risk estimations. The proposed framework provides a practical extension of the existing component-level seismic fragility modeling approach for seamlessly integrating correlation effects, improving its effectiveness and applicability for downstream risk and resilience assessment of bridge systems.
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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) | 0.001 | 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 it