Dynamic Viscosity Prediction of Blends of Paving Grade Bitumen with Reclaimed Bitumen
Bibliographic record
Abstract
The recycling of reclaimed asphalt pavement is of significant economic and environmental benefits. In this case, however, the satisfactory performance of the final product needs scientific planning of the mixture and thorough quality control. Before its use, a number of tests must be performed to verify that the binder meets the relevant requirements and can be used in asphalt mixtures. The binder characteristics in the reclaimed asphalt pavement and the expected properties of the binder blend in the new asphalt mixture must be known. For its prediction and calculation, a European standard offers the calculation of the penetration or softening point of the binder blend in the mixture with reclaimed asphalt content. However, in some countries, the determination of paving grade bitumen types (categories) is based not on dynamic viscosity measured with DSR instrument, so other validated test and calculation methods are in force. A viscosity-based method has not yet been validated for paving grade bitumens standardized on a penetration basis, although this method is more advantageous in many aspects when monitoring daily production processes; it is much shorter and requires less material than measuring softening point or penetration. The article deals with the measurement of the dynamic viscosity of bitumen blends of asphalt mixtures made using reclaimed asphalts, determined with a dynamic shear rheometer at different frequencies (0.1–10.0 Hz sweep). Furthermore, the relationships between the different composition ratios of national paving grade bitumens classified on the bases of penetration level and bitumens from reclaimed asphalt pavement are examined.
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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 | 0.001 |
| 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) | 0.000 | 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".