Comparing Rheological Indexes to Optimize Rejuvenator Dosage for Asphalt Binders Containing High Ratios of Recycled Asphalt
Bibliographic record
Abstract
ABSTRACT Determining the optimum dosage of a rejuvenator is essential to fulfilling its restoration mission on aged bitumen. Inadequate rejuvenator dosage will lead to insufficient rheological property restoration, whereas excessive rejuvenator content will oversoften the binder blend, leading to impaired rutting resistance. Two different methods, namely, blending chart and response surface modeling, are used in this study to optimize the rejuvenator dosage of binder blends with a high reclaimed asphalt pavement (RAP) binder ratio. Recycled binder blends were prepared using 25 %, 50 %, and 75 % recovered RAP binder and the corresponding virgin binder proportions, with added rejuvenator contents ranging from 0 to 10 % by weight of the binder blend. Rheological tests were performed under a wide temperature range to obtain different indexes as optimization criteria. Indexes include rotational viscosity at binder mixing and compaction temperatures, critical performance grading (PG) temperatures, nonrecoverable compliance, and crossover temperature. The results indicate that indexes obtained at a higher-temperature range required more rejuvenator content to restore the properties of the recycled binder blend to reach a target value. Some indexes only reflected the decrease in stiffness without revealing the changes in the viscous behavior and relaxation capacity. In addition, the selection of optimization criteria should consider the dominant distress type for the specific region. The difference in rejuvenator dosage determined by the blending chart and response surface modeling methods was found to be marginal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".