Enhancing micro-surfacing asphalt with granite waste: a study on durability and performance
Bibliographic record
Abstract
Micro-surfacing asphalt is a preventive maintenance method to delay asphalt pavement aging and deterioration. This study examines the use of granite powder from industrial granite waste as a filler to significantly improve micro-surfacing durability. The base filler was replaced by granite filler at 0%, 25%, 50%, 75%, and 100% by mass, with a residual bitumen content of 8.5%. Mixtures were evaluated according to International Slurry Surfacing Association–ISSA A143 guidelines. Performance tests included wet cohesion, bleeding, vertical displacement, deformation, abrasion under wet conditions, and moisture sensitivity. Results showed that 100% granite replacement increased adhesion by 6.25% at 30 min and 2.30% at 60 min, reduced displacement and deformation by 6.19%, enhanced moisture resistance by 27%, and decreased bleeding by 1.17%. Improvements are attributed to the high SiO 2 and Al 2 O 3 contents, angular particle morphology, and the large specific surface area of granite.
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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".