Multiscale Model for Asphalt Mixtures Subjected to Cracking and Viscoelastic Deformation
Bibliographic record
Abstract
The study reported in this paper presented a multiscale computational model, along with its validation and calibration, to predict the damage-dependent behavior of asphalt mixtures subjected to viscoelastic deformation and cracking. Asphalt mixture is a classic example of a multiphase composite that represents different lengths of scales. The understanding of the mechanical behavior of asphaltic materials has been a challenge to the pavement mechanics community because of the multiple complexities involved: heterogeneity, anisotropy, nonlinear inelasticity, and damage growth in multiple forms. To account for this issue in an accurate and efficient way, the study reported here presented a two-way linked multiscale computational modeling approach. The two-way linked multiscale model had its basis in continuum thermomechanics and was implemented with a finite element formulation. With the unique multiscale linking between scales and the use of the finite element technique, this model could take into account the effects of material heterogeneity, viscoelasticity, and anisotropic damage growth in small-scale mixtures on the overall performance of larger-scale structures. Along with the brief theoretical model formulation, the multiscale model was validated and calibrated through the comparison of the numerical, analytical, and experimental results of three-point bending beam tests of asphalt mixture samples that involved viscoelasticity, mixture heterogeneity, and cohesive zone fracture.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".