Enhancing Bitumen Rheological Properties with Recycled Waste Lubricating Oil
Bibliographic record
Abstract
Asphalt mixtures, widely used in road paving due to their resistance to corrosion under various conditions and flexibility, allow for property modifications to improve the road's quality and extend its service life.Utilizing waste reduces environmental risks, contributes to sustainable development, and enhances the properties of bitumen and asphalt mixtures.Recycling allows us to dispose of these materials in various forms.This study investigates the possibility of recycling lubricating oil waste to improve the quality of asphalt and convert it into asphalt suitable for use.The study used four percentages of lubricating oil waste by weight of Shirqat bitumen, with a penetration grade of 20-30.The four percentages used to lubricate oil waste are (0, 3, 6, 9)% by weight of bitumen.The results demonstrated that adding used lubricants can improve the properties of bitumen with a penetration grade of 20-30, making it suitable for use in Iraq's southern and central regions, which typically use bitumen with a penetration grade of 40-50.Studies also showed an increase in asphalt's susceptibility temperature, indicating the possibility of using it in the cold region.However, the results were not satisfactory for high temperatures.This study is considered good from an economic and environmental perspective in terms of recycling waste and using it to improve the performance of asphalt in cold areas, including Iraq's northern regions.The best percentage of lubricating oils is 6 per cent as an additive to asphalt, which gives it specifications suitable for the climate of most regions of Iraq, and the results are consistent with Iraqi materials and construction standards.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".