Thermorheological properties of asphalt binders
Bibliographic record
Abstract
Abstract The rheological properties of different types of asphalt binders were studied and compared considering their constituents and physical characteristics. The saturate, aromatic, resin, and asphaltene (SARA) analysis and differential scanning calorimetry (DSC) have shown their individual constituents and two distinct glass transition temperatures, indicating the phase changes of the two main components of the asphalt binders, namely asphaltenes and maltenes. Rheological characterization was performed over a wide range of temperatures (−10°C‐60°C) showing that these materials may exhibit viscoelastic solid to viscous liquid behaviour. Master curves of complex viscosity, storage modulus, and loss modulus were constructed by applying the time‐temperature superposition principle, which was found applicable over the temperature range considered. Stress relaxation and steady‐shear test were applied to the samples in order to determine their rheological behaviour in the nonlinear viscoelastic regime (viscosity and nonlinear relaxation modulus). The rheological results were modelled and revealed that the Kaye‐Bernstein‐Kearsley‐Zappas (K‐BKZ) constitutive equation is suitable in representing the rheological behaviour of asphalts. The SARA analysis and rheological measurements were found to be compatible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.003 | 0.001 |
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".