Performance of Hot-Mix Asphalt with Fractionated Reclaimed Asphalt Pavement Content
Bibliographic record
Abstract
Usage of reclaimed asphalt pavement (RAP) material can have economic and environmental benefits. However, the variability in RAP sources and the uncertainty in long-term performance tend to limit the use of RAP content in asphalt mixtures to 20% of the mixture or less. RAP fractionation is one of numerous methods that have been proposed to increase RAP usage. RAP fractionation into fine and coarse stockpiles aims to improve the consistency of RAP particle sizes as well as binder content and properties to maintain an acceptable mix design and performance. The objective of this study is to characterize the performance of mixtures containing fractionated RAP content for local materials in Manitoba. Cracking and rutting performance of hot-mix asphalt mixes were assessed using Illinois Flexibility Index Test and Hamburg wheel-tracking test, respectively. Results showed that resistance to rutting increased with the increase of RAP content. Cracking resistance decreased with the incorporation of RAP. Additionally, fractionated RAP samples showed better resistance to cracking and rutting than unfractionated RAP samples. The final findings of this research will help transportation agencies in Manitoba to optimize the design of asphalt mixtures containing RAP.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".