Review of advances in micromechanical modeling of aggregate–aggregate interactions in asphalt mixtures
Bibliographic record
Abstract
This paper presents a comprehensive review of the work done by a number of researchers on the modeling of asphalt mixture. Included are some of the earliest models such as those with non-interacting particles (models with and without geometry specified), models with particle interaction, and some new models developed in recent years. The paper focuses on the description and comparison of the most recently developed finite element network model (FENM), a clustered discrete element model (DEM), and a micromechanical finite element model (FEM) used in micromechanical modeling of asphalt mixture. These models consider the complex mixture microstructure and aggregate–aggregate interaction. These models are demonstrated and applications of the advances are provided, where virtual laboratory simulation and laboratory tests were employed. The feasibility of nanotechnology application in asphalt mixture is also briefly discussed.Key words: micromechanical modeling, micromechanics, aggregate–aggregate interaction, finite elements, discrete elements, asphalt mixture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".