Assessment and Modeling of Aging Effect on Asphalt Material Dynamic-Mechanical Properties
Bibliographic record
Abstract
Abstract In this study, the effects of progressive aging on the dynamic-mechanical characteristics of asphalt and asphalt concrete were assessed and evaluated. Field-collected asphalt-aggregate mixture was used to prepare asphalt concrete test specimens. These specimens were then subjected to five aging levels and tested for dynamic-mechanical properties in the laboratory. Results showed that the dynamic-mechanical characteristics of asphalt concrete are significantly affected by progressive aging. In addition, aged asphalt binders that corresponded to the asphalt concrete samples aged at various aging levels were tested for complex shear modulus using a dynamic shear rheometer. A significant effect of progressive aging on the complex shear modulus and viscosity of the binder was observed. A detailed assessment showed that even though the mechanical behavior of asphalt binder significantly changes because of aging, the corresponding asphalt concrete sample does not necessarily show similar change in its behaviors. Finally, this study developed an approach to estimate the dynamic-mechanical characteristics of the aged asphaltic materials. Using this approach, it may be possible to predict the mechanical behaviors of aged asphalt concrete and binder. It is hoped that the developed approach can be useful for advanced modeling of asphalt pavements.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".