Fatigue Performance of Re-Refined Engine Oil Bottom–Modified Asphalt: Laboratory Study
Bibliographic record
Abstract
Re-refined engine oil bottom (REOB), one of several products obtained from refining recovered engine oil, has been used in the asphalt industry since the 1980s. Generally, REOBs are used to help soften the base asphalt binder and are commonly used from 3% to 10% by weight to achieve desired low temperature asphalt binder properties. Recently, blame for poor cracking performance in a number of Canadian and northern U.S. pavement sections has been laid on the use of REOBs. This issue has prompted state agencies in the northeast United States to ban its use, without necessarily understanding how REOB affects asphalt binder and mixture performance. A research effort was conducted to evaluate the laboratory performance of asphalt binders and mixtures modified with REOB. Performance grading, master stiffness curves, the double-edge notch tension test (DENT), 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 in accordance with AASHTO R29, the addition of REOB accelerated the age-hardening effects in 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 were not affected by REOB, the relaxation properties were highly affected. The black space analysis, using the Glover–Rowe approach, and the DENT test showed promise in identifying the age-hardening effects and correlated well with mixture fatigue cracking.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".