Performance of asphalt binders modified with Re-refined Engine Oil Bottoms (REOB)
Bibliographic record
Abstract
Re-refined Engine Oil Bottoms (REOB) are one of several products obtained in the refining of recovered engine oil and have been used since the 1980’s in the asphalt industry. Generally, REOB is used to help soften the base asphalt binder and is commonly used from three to ten percent by weight in order to achieve desired low temperature asphalt binder properties. Recently, poor cracking performance in a number of Canadian and northern United States pavement sections have been blamed on the use of REOB to modify the asphalt binder. This has prompted many state agencies in the northeast United States to ban its use. This paper summarizes the laboratory performance of asphalt binders modified with REOB. Two different sources of REOB were blended with different base grades at varying dosage rates in the study. Performance grading, master stiffness curves, double edged notch tension test, and Black Space analysis were conducted on the asphalt binders at different levels of laboratory aging. The research study showed that while being able to achieve softer asphalt binder grades, the addition of REOB accelerates the aging of the asphalt binder with higher levels of age hardening occurring at higher REOB dosage rates. The study also indicated that while the stiffness properties at low temperatures are not impacted by the REOB, the relaxation properties, as measured using m-slope of the Bending Beam Rheometer (BBR), are highly affected. Both the Black Space analysis, using the Glover-Rowe approach, and the DENT test show promise at identifying the age hardening affects.
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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".