Evaluating the aggregate structure in hot-mix asphalt using three-dimensional computer modeling and particle packing simulations
Bibliographic record
Abstract
In a hot-mix asphalt (HMA) pavement, the aggregate structure serves as a backbone and is primarily responsible for resisting pavement distresses. A sound aggregate structure implies optimal packing of aggregates providing both particle–particle contact and sufficient void space to fill in asphalt. In this paper, three-dimensional particle packing concepts are applied to the study of aggregate structure in HMA. A sequential deposition packing algorithm was used for packing typical aggregate gradations. The packing fraction and the distribution of particle–particle contacts in the simulated compact were studied. The packing simulation gave satisfactory results when aggregates above a certain minimum size were considered. Regression models were established to estimate the coordination number of any size aggregate in the compact. Such studies, in conjunction with the recent advances in X-ray computed tomography imaging techniques and discrete element modeling (DEM) simulations, have tremendous potential to help develop a deeper understanding of the HMA aggregate structure, develop and optimize the various parameters that describe the aggregate structure, and relate these parameters to the performance of pavements in a scientific way.Key words: packing, aggregate structure, computer simulation, aggregate–aggregate contact, pavement performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".