Rheological modelling of asphalt materials properties at low temperatures: from time domain to frequency domain
Bibliographic record
Abstract
The rheological model, Huet model, which is composed of one spring and two power function elements, has two expressions: one in time domain, which is used for creep compliance, and one in the frequency domain, which is associated with complex modulus. Previous research efforts showed that the Huet model provides a very good fitting of experimental results obtained from creep and complex modulus tests on asphalt materials. However, the potential use of this model as an inter-conversion tool between the data obtained in the time domain and those in the frequency domain, and vice versa, was never previously evaluated. In this paper, the possibility of using the Huet model for predicting the complex modulus of asphalt binders and asphalt mixtures from the experimental data obtained from creep tests, performed with the bending beam rheometer at low temperatures, was investigated. The predictions obtained with the Huet model were experimentally verified by a set of complex modulus tests performed on 2 asphalt binders and 20 asphalt mixtures with the dynamic shear rheometer. The good agreement between the predicted and the measured complex modulus of asphalt binders suggested that the Huet model can be used for inter-converting data obtained in time and frequency domains. This was not verified in the case of the asphalt mixtures complex modulus data due to experimental limitations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".