Considerations for vulnerability and risk assessment of bridges and culverts including extreme weather events
Bibliographic record
Abstract
As a result of increased extreme weather events in many parts of the world, there have been growing concerns related to the risks to transportation infrastructure and infrastructure resiliency. Recently, a national survey was completed which included, amongst other topics, how provincial and municipal bridge departments across Canada are responding to these challenges. In conjunction with several transportation agencies, Stantec has been developing appraisal indices to include the effects of extreme events on flooding, scour, wildfires and other vulnerabilities such as fatigue, seismic, and load carrying capacity. The appraisal ratings are being implemented in a Bridge Management System to provide input into risk analysis and resiliency assessment and assist agencies in decision making regarding these challenges. This paper will highlight how agencies can consider in a practical way the effects of potential extreme weather related events, such as flooding, scour, and wildfires. The results of this research as well as the findings of the recent national survey will be shared. Parameters that can be used as input into each appraisal are discussed e.g. for flooding, factors such as flood frequency, risk of overtopping, freeboard, traffic delay consequences can be considered. The paper will be of interest to other jurisdictions who manage bridges and culverts who may be developing responses to these challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".