Variability Investigation of Reclaimed Asphalt Pavement Materials
Bibliographic record
Abstract
The maximum permissible content of reclaimed asphalt pavement (RAP) is restricted due to its negative effect on the stability of hot mix asphalt with RAP (HMA-RAP) performance. To address this problem, characteristics of materials, including aggregate gradation, aged asphalt content, and aged asphalt properties, were quantified by testing RAP obtained from different sources. Additionally, the changing law and variability of the indexes are also analyzed. In accordance with the quality requirements of hot mixture asphalt stipulated by the Chinese standard, a control model of the maximum RAP content embraced in recycled asphalt mixture for hot central plant recycling is established. Furthermore, the distribution characteristic of asphalt content with respect to particle size is analyzed. Eventually, a fluctuation range model of blended asphalt penetration is established. The results indicate that (1) the variabilities of aggregate gradation, asphalt content, and aged asphalt properties of RAP are nonnegligible; (2) a control model regarding maximum permissible RAP content in HMA-RAP is proposed based on aggregate gradation and asphalt content; (3) the asphalt content and particle size of RAP are exponentially distributed, and the particle size elevates with the decrease of asphalt content; and (4) the fluctuation range of blended asphalt penetration is related to the asphalt content and penetration of aged asphalt, and the fluctuation range extends with the increasing of RAP content. This paper suggests that, to characterize the variability of RAP with its aggregate gradation and asphalt content, for RAP without pretreatment, the maximum permissible RAP content in HMA-RAP is recommended to be controlled under 30%.
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 | 0.001 |
| 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) | 0.000 | 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".