Addressing Climate Change Resilience in Pavements: Major Vulnerability Issues and Adaptation Measures
Bibliographic record
Abstract
Climate change is the one of the greatest challenges of our time, and it poses a threat to the surrounding built and natural environments. This review paper addresses climate change resilience in pavements by considering major vulnerability issues and adaptation measures. First, a review on foundational information of climate change related to transportation infrastructure is provided to bring all transportation professionals and practitioners to the same knowledge base on climate change terminology. Such information includes sources of climate information, climate scenarios, downscaling climate data, and uncertainty in climate projection information. Relevant climate stressors to pavements are discussed in some depth, including the most significant ones, which are increases in temperature and precipitation intensity. Thus, the proposed different engineering-informed adaptation measures relevant to the climate stressors of interest were evidence-based with reference to published peer-reviewed articles and case studies. Such adaptation solutions are related to monitoring pavement key performance parameters and pavement adaptations in structural design, robust materials and mix design, along with adaptation in maintenance, regulation, and construction. Efforts to adapt pavement systems to climate change are ongoing. In addition to such research works, this study concludes that impacts of adaptation measures on pavement and environment should be incorporated in the decision-making process in planning and design. This makes it important to integrate practical adaptation strategies in design and construction standards and guides, and implement awareness and education of climate change adaptation among engineers and practitioners.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".