Optimizing rejuvenator dosage for high RAP content asphalt binders under multiple recycling cycles: A study based on an alternative rheological approach
Bibliographic record
Abstract
The widespread use of rejuvenators has enabled the incorporation of 60 % reclaimed asphalt pavement (RAP) into new asphalt mixtures . However, it is uncertain whether rejuvenators can persist in restoring the kernel properties of asphalt binders after multiple recycling processes. This study conducted three cycles of aging and rejuvenation using two bio-based rejuvenators (R1 and R2) and one wax-based rejuvenator (R3), with subsequent aging performed in the laboratory. After determining the rejuvenator dosages based on the Recommendation proposed by RILEM TC-264 RAP TG3, the study assessed dynamic rheological behavior across a broad temperature range, rutting performance, fatigue life , and chemical property changes in the rejuvenated binders. The results indicated that the optimal dosage for R1 and R2 showed a decreasing trend, and the rejuvenated binders still exhibited good high-temperature rutting resistance after three cycles of rejuvenation. The low PG was restored, and due to the cumulative softening effect of the rejuvenators, the fatigue life increased by 146.57 % and 137.83 % under 2.5 % strain, and by 102.17 % and 88.99 % under 5 % strain, respectively. Additionally, the relative content of asphaltenes remained stable, and the chemical aging index (CAI) decreased. In contrast, for R3, despite an increasing optimal dosage, the low PG could not be restored. After three cycles, the relative content of asphaltenes increased by 4.38 % compared to the aged binder , and the CAI also increased. Although bio-based rejuvenators are more suitable for multiple recycling, attention must be paid to their gradually increasing sensitivity to low-temperature cracking and the potential for excessive softening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".