Evaluation of effective parameters on pavement surface temperature
Bibliographic record
Abstract
The elevated temperatures adversely affect the durability and lifespan of pavement. Understanding the factors that influence asphalt pavement temperature offers valuable insights for creating climate-friendly cities with cooler pavement surfaces. In this study, three aggregates of varying types and colors, two types of bitumen (one without pigment and one with the addition of red pigment, Fe 2 O 3 ), and two levels of mean texture depth (MTD), high and low, were utilized to create asphalt samples using Marshall's method. A total of 38 thermocouple sensors were employed to simultaneously record temperatures in three areas within the samples, as well as the temperatures in shaded and sunlit conditions over a period of 17 days. Furthermore, a comprehensive evaluation was conducted to assess the impact of each factor on the solar reflectance index (SRI). Twelve general linear models (GLMs) were developed using a full factorial design of experiment, and five models with an R 2 greater than 95% were evaluated and analyzed. The analysis, based on the coefficients derived from the GLMs, indicates that the mean MTD is the most significant parameter affecting surface temperature. Pigment color emerged as the second most influential factor affecting both surface and bottom temperatures. Additionally, the findings revealed that MTD has the greatest impact on the SRI, followed by pigment color and aggregate color. It was also determined that the interaction between density, pigment color, and aggregate color plays a crucial role in determining the temperatures of both the surface and bottom of the specimens.
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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) | 0.000 | 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".