Risk Assessment Protocol for Existing Bridge Infrastructure Considering Climate Change
Bibliographic record
Abstract
The escalating impact of climate change on global weather patterns threatens the functionality and resilience of infrastructure systems. This paper presents a rigorous risk assessment protocol tailored to existing bridge infrastructure, integrating climate change projections, structural integrity, and socioeconomic factors. The protocol’s application involves five sequential steps: selecting a bridge, disassembling the structure into components, calculating utilization factors for design and projected temperatures, evaluating severity factors encompassing structural and socioeconomic aspects, and ultimately determining an overall risk rating. To demonstrate the protocol’s effectiveness, a case study was conducted on the Westminster Drive Underpass in London, Ontario. This study shows how the protocol systematically evaluates the vulnerability of each bridge component to projected temperatures under the Representative Concentration Pathway 6.0 model. The protocol provides a holistic risk assessment by incorporating both the structural response and socioeconomic implications of failure. The results rank the bridge’s risk level and highlight the urgency of intervention. The protocol emerges as a robust tool for decision-makers, practitioners, and engineers, offering a comprehensive approach to strengthen bridge infrastructure against the challenges of climate change.
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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 | 0.060 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".