Second-generation component and system-level seismic fragility models for reinforced concrete bridges in California
Bibliographic record
Abstract
California is a seismically active region that contains approximately 26,000 bridges. Historical earthquakes have caused severe damage and collapse of bridges in California, resulting in casualties, economic losses, and disruptions to transportation networks. Seismic fragility models of bridges estimate the probability of exceeding damage states at varying ground motion intensity levels. These models can be utilized for bridge vulnerability assessment, risk and resilience quantification, and to support earthquake preparedness and response planning. Recent studies have developed a new generation of seismic fragility models for bridges, demonstrating several advancements when compared with the widely used first-generation HAZUS models. These models, termed second-generation fragility functions, are derived through detailed dynamic response analyses and are differentiated at the component level, with more rational, performance-based criteria for bridge grouping and archetype sampling. This study compiles and adapts a comprehensive set of second-generation fragility models from the literature. As part of this compilation, we filtered out fragility functions with outdated capacity models and unrealistic bridge configurations, establishing uniform bridge grouping criteria, and harmonized different cross-model ground motion intensity measures and bridge component definitions. The database comprises approximately 2300 component- and 500 system-level fragility models categorized into 26 bridge groups. It can serve as a valuable resource for researchers and practitioners conducting various related analyses to enhance the seismic resilience of bridge infrastructure in California.
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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".