Impact of analysis method on the fragility curves of regular and irregular box-girder highway bridges
Bibliographic record
Abstract
Fragility curves constitute a popular tool for the seismic vulnerability assessment of structures. Nevertheless, obtaining the fragility curves based on conventional non-linear time-history analysis (NTHA) methodologies is time consuming. Therefore, more computationally efficient methods, such as the fragility through capacity spectrum assessment (FRACAS) technique, have been proposed. In this study, a detailed seismic fragility analysis of regular and irregular multi-span bridges was performed, utilizing NTHA and FRACAS methods. For this purpose, 3D models of unbalanced, skewed, and curved irregular bridges were considered, taking into account uncertainties related to geometry, material as well as seismic excitations. In the presented methodology, the FRACAS method, recently developed for buildings, was developed to be suitable for the 3D analysis of the examined bridges, namely 3D FRACAS, which was further improved to reduce the demand error compared to NTHA approach. The fragility curves obtained from the improved 3D FRACAS were compared with those obtained via NTHA and 3DFRACAS methods. Results illustrate that the improved 3D FRACAS approach can be effectively used for developing the fragility curves for regular and irregular bridges with adequate accuracy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".