VULNERABILITIES AND DESIGN CONSIDERATIONS FOR PAVEMENT INFRASTRUCTURE IN LIGHT OF CLIMATE CHANGE
Bibliographic record
Abstract
Although climate is directly linked to infrastructure deterioration, there is little research on potential impacts of climate change. Canadian estimates suggest increases in temperature of 2oC to 5oC and up to 10% precipitation over the next 45 years. Relatively little research has been completed to investigate the potential engineering impacts of climate change on infrastructure, and particularly roads and bridges. Potential impacts need to be addressed given the importance of road and bridge infrastructure on economic and social activity. More specifically, climate change can impact: thermal cracking, frost heave and thaw weakening, permafrost melting, permanent deformation associated with warm and cold temperatures, to name a few. Current and past engineering designs generally assume a static climate whose variability can be adequately determined from records of weather conditions, which normally span less than 30 years and often less than 10 years. The notion of anthropogenic climate change challenges this assumption and raises the possibility that the frequency, duration or severity of thermal cracking, rutting, frost heave and thaw weakening may be altered leading to premature deterioration. General methods for assessing the potential impacts of climate change on various aspects of society, economy and environment have been developed over the past two decades, largely based on approaches rooted in applied climatology or the hazards and risk assessment literature. The leading international source of guidance on climate change impact assessment is the Intergovernmental Panel on Climate Change (IPCC). It is an organization that is responsible for periodic reviews of the scientific literature on aspects of climate change science, impacts and adaptation assessment, and emissions mitigation. Generally, maintenance, rehabilitation or reconstruction will be required earlier. The paper provides a brief introduction and background on climate change in general and the related predicted impacts on road infrastructure and associated structures, with primary focus on bridges. A summary of findings provides some more specific details and has been prepared using available public agency documents that were located from public sources.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".