Evaluating the effectiveness of high thermal resistance aggregates in asphalt mixtures for Urban Heat Island (UHI) mitigation
Bibliographic record
Abstract
This study investigates how different asphalt mixtures can mitigate the Urban Heat Island (UHI) effect by analysing their thermal properties, including conductivity, heat storage, and thermal inertia. Limestone asphalt mixtures (LM), characterised by high thermal inertia, were compared with glass (GM), ceramic (CM), and clay brick (CBM) mixtures, which have lower thermal inertia. Laboratory and field tests conducted during the summers of 2022 and 2023 evaluated how these materials respond to solar radiation and ambient conditions. LM exhibited more stable surface temperatures due to slower heating and cooling, whereas GM, CM, and CBM cooled more quickly at night, which helped reduce heat accumulation and UHI intensity. The research highlights the novel use of recycled materials—glass, ceramic, and brick—in asphalt mixtures and emphasises the importance of optimising air voids to enhance thermal performance. These findings support the development of sustainable asphalt designs that promote daytime heat dissipation and nighttime cooling in urban areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".