A novel micromechanical–analogical model for low temperature creep properties of asphalt binder and mixture
Bibliographic record
Abstract
The ENTPE (École Nationale des Travaux Publics de l’État) transformation is commonly used to predict the low temperature properties of asphalt binders from the corresponding mixtures experimental data and vice-versa. Nevertheless, the transformation parameter, α, associated to the ENTPE equation, cannot be directly obtained without relying on both binder and mixture testing. This paper presents a comprehensive investigation to link the ENTPE transformation to the mixture microstructure. This is accomplished by three-point bending tests on asphalt binders and mixtures, digital image processing and statistical evaluation of mixture microstructure, together with a newly proposed micromechanical–analogical model, called MCF (Moon – Cannone Falchetto), used for deriving an explicit expression of α. The values of α obtained from asphalt binder and mixture laboratory measurement are compared to the values predicted by the new formulation. The results indicate that reasonable predictions of low temperature creep stiffness of asphalt binder can be obtained when the new expression of α is used in the ENTPE transformation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".