Enhancing Pavement Resilience in Canada Through Adaptive Asphalt Binder Selection
Bibliographic record
Abstract
Abstract Climate change is increasingly posing risks to flexible pavements due to rising temperatures and more frequent and intense extreme heat events. As climate patterns shift from historical norms, the vulnerabilities of these pavements become more evident, threatening transportation infrastructure and economic stability. This paper presents a climate change impact assessment on the SUPERPAVE ® asphalt binder Performance Grade (PG) selection for mix design in Canada. Therefore, a tool was firstly developed named as Performance-Graded Asphalt Cement under a Changing Climate (PGAC3) considering projected temperature conditions. Secondly, the relative impact of climate change on design temperatures was studied under different Representative Concentration Pathway (RCP) scenarios. The findings indicate that projected temperature changes are geographically variable and affect critical design factors differently. Notably, case study simulations for a representative city suggested that adapting PG selection to future climate conditions could potentially extend the pavement service life. These insights underscore the critical need for the Canadian transportation sector to prioritize and implement robust climate adaptation strategies and policies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".