Asphalt Pavement Materials Management to Reduce Urban Heat Island Effetcs: A Review
Bibliographic record
Abstract
Urban pavements, typically constructed with asphaltic materials, significantly contribute to forming urban heat islands. Consequently, there is an urgent need to explore bituminous material solutions that mitigate thermal discomfort and address issues related to the mechanical properties of pavements, thereby enhancing their durability and service life. This study aims to analyze asphalt mixtures employed to reduce the temperature of flexible pavements through a comprehensive literature review. Reviewed 241 articles from two international databases and included 49 in the final analysis after applying selection criteria. The findings highlight the effectiveness of various materials and methods in providing thermal insulation and storage, refracting solar radiation, and achieving energy transmission through incorporation into asphalt mixtures. Among these, fiberglass, polyethylene glycol, and hollow microspheres yielded the most promising results. Despite the demonstrated efficacy of recent studies in temperature mitigation, the review underscores the critical need for durability control methods to ensure large-scale applicability. Additionally, comprehensive laboratory analyses are necessary, considering not only high temperatures but also the physicochemical interactions that may accelerate the degradation of the asphalt binder. Identifying and implementing innovative practices and materials to optimize pavement management and mitigate thermal effects is essential. Applying these findings can guide the development of future pavement designs and interventions, helping to reduce the effects of urban heat islands and ensure more durable and sustainable structures better suited to meet urban populations’ needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".