Optimization of Hot-Mix Asphalt Surface Course Mix Design for Fatigue Resistance: High-Friction Aggregate and PG Plus
Bibliographic record
Abstract
The sensitivity of the fatigue resistance of surface hot-mix asphalt (HMA) mixes is evaluated for three primary design variables. Aggregate type, binder type, and binder content as well as their interaction have been quantified with respect to their effects on HMA fatigue life, rutting resistance, and stiffness. The objective was to optimize the design by extending fatigue performance while reducing the confounded negative effect on rutting resistance. Two aggregate types were used in the evaluation. Two binders of the performance grading (PG) 64-28 were also employed: a modified binder that meets national specifications and an unmodified binder at two binder levels (optimum and optimum plus 0.5%). Aggregate texture was compared visually using high-resolution scanning electron microscopy (SEM) images. The results showed that the value of modifying the binder to produce softer mixes can be compromised when a high-friction aggregate is used due to the irregular shape of the texture, which produces stiffer mixes. A slight adjustment to the amount of binder (+0.5%) can decrease this effect. Superior HMA fatigue performance was exhibited by the regular 12.5 aggregate and the modified binder at the optimum binder content plus the additional 0.5%. This conclusion was reached through the integration of the positive effects of the investigated variables, which revealed only an insignificant reversible impact on rutting resistance. The findings of this study can therefore be considered a guide for designing HMA with superior fatigue performance for use in pavement design.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".