Bridge infrastructure resilience assessment against seismic hazard using Bayesian best worst method
Bibliographic record
Abstract
The transportation agencies are experiencing numerous challenges in endorsing resilient bridge infrastructure against natural disasters such as earthquakes, floods, and hurricanes. Therefore, assessing bridge infrastructure resiliency against a seismic hazard is critical to improve the bridge's endurance and effective recovery. In this study, assessment of bridge infrastructure resilience against the seismic hazard is performed using Bayesian best–worst method (BWM). Based on a literature review, 15 resilience parameters were identified for bridge infrastructure resiliency against seismic hazard under two primary aspects, namely reliability and recovery. The Bayesian BWM was used to calculate the weight of the seismic resilience variables based on the experts’ judgment. Finally, a bridge resiliency index was developed to determine the weights of the parameters. To demonstrate the applicability of the proposed model, the infrastructure resiliency against the seismic hazard of a bridge located in Vancouver Island, British Columbia, Canada is assessed using two sets of data from two different sources. It is mentionable that the resiliency index was close for both of the data sets. The outcome of this study is that the model for determining bridge resiliency index will assist the bridge engineer and policymakers make effective decisions in the face of future seismic danger.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".