Prédiction des propriétés viscoélastiques des enrobés bitumineux Approches micromécaniques et expérimentales
Bibliographic record
Abstract
The asphalt mixtures are composed of aggregates and asphalt used in the construction of the majority of roads. In order to ensure the sustainability of the infrastructures, the evaluation of the quality and the performances of these materials are essential. In this context, several researches have been focused on the development of predictive models, often empirical ones, in order to deduce the viscoelastic properties of an asphalt mixture based on the properties of its constituents (binder and/or aggregate). In this context, we suggest a homogenization model based on the generalized self-consistent scheme (GSC) to predict the complex module of the asphalt concrete from the properties of its components. In the aim of the approach validation, different types of mixtures (hot and warm) made in the laboratory were tested. The results showed that one can predict the complex modulus of the different types of asphalt concretes for temperatures less than 20 °C. However, beyond this temperature, the precision of the model decreases. Besides, the comparison of the micromechanical model with the rheologic models in literature showed that the suggested model can be also relevant in terms of predictions as the considered models.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".