Implementing Waste Oils with Reclaimed Asphalt Pavement
Bibliographic record
Abstract
The present asphalt pavement industry faces two major issues. These two major issues are the increasing demand for ecofriendly asphalt mixtures and the costs of raw materials. The use of reclaimed asphalt pavement will be an essential attempt to reduce the costs of aggregates and bitumen in the final mixture. On the other hand, the main challenge for implementing RAP (Reclaimed Asphalt Pavement) is to overcome quality issues. RAP doesn't perform like a fresh pavement since it is an aged material and needs to be improved. This puts forward the requirement for extra practices like using of rejuvenating agents. Since bitumen loses its oily constituents when it ages, the use of oil-containing additives can be effective. In this study, two kinds of waste oils were used to rejuvenate aged asphalt mixture. Optimum contents of Waste Engine Oil (WEO) and Waste Vegetable Oil (WVO) additives were determined in order to implement various RAP contents. The effects of these two oily based rejuvenators on utilization of RAP in bituminous mixtures were investigated. The results represented that the use of WEO and WVO as rejuvenators for mixtures containing RAP enhances the amount of RAP used in bituminous mixtures.
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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 | 0.001 |
| 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".