Vulnerability and Adaptation of Transportation Infrastructure to Climate Change
Bibliographic record
Abstract
It is clear that climate change represents a significant risk to the performance of engineered systems and to public safety in Canada. As such, engineers, asset managers and decision-makers must address climate change adaptation as part of their primary mandate – the protection of the public interest, which includes life, health, property, economic interests and the environment. Vulnerability and risk assessment form the bridge to ensure climate change is considered in engineering design, operations and maintenance of civil infrastructure. Identifying the components of the infrastructure that are highly vulnerable to climate change impacts allows cost-effective engineering, operations and policy solutions to be developed. This paper puts future climate risks in the context of the current condition of Canada’s infrastructure and the impacts of climate change. It presents a high level overview of some of the tools available to decision-makers and infrastructure practitioners to consider climate change impacts, from planning to operations and maintenance. The article focuses on processes and methodologies that have been used by public agencies and municipalities in Canada to identify and quantify risks, as well as develop climate change adaptation solutions. Engineers Canada’s PIEVC Protocol, a methodology used in more than 40 projects across Canada to evaluate the vulnerability of infrastructure is described in more detail. The Protocol has been applied to a wide spectrum of infrastructure: roads, highways, bridges and associated structures; potable water, wastewater and storm water systems; electrical transmission infrastructure and dams; buildings; airports; and coastal infrastructure. The applications cover all regions of Canada. An example of the risk assessment for a section of highway in British Columbia is presented to illustrate the application of the Protocol to a highway transportation system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".