Multi-criteria optimization of aggregate gradation based on thermal, surface, and mechanical properties of the asphalt solar collector
Bibliographic record
Abstract
The aggregate gradation of asphalt concrete for a road pavement is a critical element since it influences its strength and mechanical properties, and also has a major role on the thermal properties. An experimental study was set up to develop a multi-criteria optimization of AC12 dense-graded asphalt mixtures designed for asphalt solar collector layers according to the Bailey method (fine, middle, or coarse gradation). Samples were tested in terms of thermal potential, surface properties, and mechanical performance. It was found that a coarser gradation increased the heat concentration within the asphalt (more efficient mixture for asphalt solar collector), thanks to lower specific surface area, thicker asphalt films, and fewer interconnected internal voids. The surface and mechanical performance of all asphalt systems matched the common technical prescription limits for surface road layers. Overall, the multi-criteria evaluation was effective in optimizing the characteristics of the solar collector asphalt concrete.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".