Seismic fragility assessment of highway bridges: a state-of-the-art review
Bibliographic record
Abstract
Safety and serviceability of highway bridges, during and after an earthquake, is a prerequisite to ensure continuous transport facilities, emergency and evacuation routes. Recently, fragility curves have emerged as important decision support tools to identify the potential seismic risk and consequences during and after an earthquake. There has been a substantial increase in interest among researchers in the topic of seismic fragility assessment of highway bridges as evidenced by the growing number of published literature. Advanced computational techniques and available resources have led to the development of different methodologies for fragility assessment. This study presents a review of the different methodologies developed for seismic fragility assessment of highway bridges along with their features, limitations and applications. This study presents a review of available methodologies and identifies opportunities for future development. This study mainly focuses on the key features of different methods and applications rather than penetrating down to a critique of the associated analysis procedure or mathematical framework. It synthesises the existing information on fragility analysis, presents it in concise and useful tables, and explains different applications for different purposes, which would motivate decision-makers and stake holders to extend the application of fragility curves for more informed decision-making.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".