Seismic Resilience Assessment of a Regional Bridge Network
Bibliographic record
Abstract
ABSTRACT This study evaluates the seismic resilience of a regional bridge network, focusing on the interconnectedness and interdependence among individual bridges and emergency facilities. A framework for resilience assessment is developed and applied to a bridge network in Vancouver, British Columbia, consisting of 11 bridges across seven main routes. The methodology integrates fragility‐based models to calculate network resilience, considering various bridge configurations, including monolithic and seismically isolated bridges. The analysis highlights that bridges with seismic isolation exhibit superior resilience and functionality compared to monolithic bridges, especially under higher seismic intensities. Network damage, reliability, and resilience indices are used to quantify the impact of individual bridge failures on overall network performance. The results demonstrate the importance of bridge type and network topology on resilience, emphasizing that increasing alternative paths between critical nodes enhances network reliability and reduces vulnerability. These findings offer valuable insights for disaster mitigation strategies and infrastructure resilience planning in seismic‐prone regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".