Effects of Preheating on the Rheological Properties of Rejuvenated Asphalt Binder
Bibliographic record
Abstract
Hot in-place recycling (HIR) is a process that reuses reclaimed asphalt materials to benefit the environment and conserve natural resources. HIR has not yet been widely applied in China because of technical drawbacks such as reclaimed materials’ fatigue properties and poor cracking resistance. Preheating is the first procedure in HIR and significantly affects the diffusion of rejuvenators within the aged asphalt and the remixing of virgin and aged mixtures. This study focuses on the effects of preheating on the rheological properties of rejuvenated asphalt binder using dynamic shear rheometer (DSR) testing. Three types of rejuvenators (Reju-A, Reju-B and Reju-C), three types of base asphalt binder (#50, #70, and #90), recovered asphalt from recycled asphalt pavement, and laboratory-aged asphalt were investigated to analyze the rheological properties under different heating conditions. The results show that (1) rejuvenators contain little in the way of viscous characteristics and have a viscosity-reducing effect on aged asphalt; (2) field-aged asphalt loses cohesion and gains rutting resistance during aging; and (3) preheating has a significant effect on diffusion of rejuvenators within aged asphalt as well as on rheological properties. In short, a good controlled preheating operation is critical to the quality of the asphalt rejuvenation process.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".