Experimental and Numerical Investigation of Using Waste Glass Aggregates in Asphalt Pavement to Mitigate Urban Heat Islands
Bibliographic record
Abstract
Abstract In this study, the experimental and numerical effects of using waste glass as aggregates of asphalt pavement are evaluated. The main reason for using this waste material as aggregates of hot mix asphalt (HMA) was to alleviate an environmental problem associated with asphalt pavements called urban heat islands (UHI). This phenomenon can increase the temperature in urban areas compared to their suburbs. Regarding the experimental part, two different HMA mixtures containing 100% limestone aggregates (HMAL) and 100% glass aggregates (HMAG) were made. An experimental setup was used to simulate the solar radiations on top of HMA specimens. As a result, thermal parameters, including thermal conductivity, thermal diffusivity, and specific heat capacity, were measured and calculated using the heat transfer equations and the heat transfer test. These results were then used to develop finite element models for two different pavement structures with different asphalt concrete layers (one of them with HMAL and the other with HMAG). Furthermore, the air temperature data, extracted by TRNSYS software for Bechar city in Algeria, was used for modeling. The surface temperature, first, second, and third interface temperatures in the asphalt pavement were obtained. The results revealed that using this waste aggregate increased the surface temperature during the day, which can make it susceptible to rutting. However, it reduced the surface temperature at night. More importantly, the HMAL absorbed 34 % and released 47% more heat than HMAG during days and nights. Hence, the HMAG performance can mitigate the UHI effects. Moreover, using this waste material as aggregates in HMA can introduce a recycling method with low costs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".