Rheological evaluation of polymer-modified asphalt binders
Bibliographic record
Abstract
Currently, the most used polymer for asphalt binder modification is the thermoplastic elastomer styrene butadiene styrene (SBS) and aromatic oil is commonly added to the mixtures to improve their compatibly. This paper proposes the use of oil shale from sedimentary rock as a compatibilizer agent for polymer-modified asphalt binder (PMB). PMBs were produced by mixing a bitumen with a linear SBS copolymer (3.5% (w.w-1)) using two oil shale contents (2 and 4%) and petroleum aromatic oil to evaluate comparatively the effect of the compatibilizer agent on the SBS PMB properties. The rheological characteristics of the SBS PMBs were analyzed in a dynamic shear rheometer (DSR) and the morphology accessed by fluorescence optical microscopy. The viscoelastic behavior of the samples corroborated the results for the classical properties and varied according to the sample morphology and composition. The results indicate that the aromatic and shale oils have similar effects on the microstructure, storage stability and viscoelastic behavior of the PMBs. Thus, shale oil could be successfully used as a compatibilizer agent without loss of properties or could even replace the aromatic oil. Following the Superpave methodology it was observed that the linear- and radial-SBS PMBs and linear-SBS PMB with 2% of shale oil can be used up to 70 °C, and the linear-SBS PMBs with 4% of shale oil or 2% of aromatic oil can be used only up to 64 °C.
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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 | 0.001 | 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".