Three-Dimensional Discrete Element Simulation of Asphalt Concrete Subjected to Haversine Loading
Bibliographic record
Abstract
Limited by the current computing power, it is impossible to simulate a full scale asphalt pavement with a three-dimensional (3D) microstructure-based discrete element (DE) model, when the time-dependent behaviors of the materials are considered. Researchers currently focus on modeling the microstructure of asphalt mixture materials and have not attempted pavement modeling. In addition, existing studies are limited to modeling asphalt mixtures without considering the time-dependent properties. In this paper, 3D DE models of asphalt mixtures under dynamic loads were developed with the assistance of X-ray Computed Tomography (X-ray CT). The time-dependent model was considered in the simulations. To shorten the computation, a frequency-temperature superposition technique was employed. X-ray CT images were used to reconstruct the microstructure of asphalt concrete, while a time-dependent model was built by taking into consideration micro-scale interactions within the models. Through this study, it was observed that the dynamic modulus and phase angles of asphalt mixtures could be well-predicted from the component properties of those mixtures. It is anticipated that the techniques developed in this study can be used to simulate full scale pavements when the computation power is available.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".