Characterization of Three-Dimensional Strong Force Chain Properties of Mineral Aggregate Mixtures Based on the Discrete Element Method
Bibliographic record
Abstract
The skeleton structure composed of mineral aggregates is the main body to bear and transfer external loading in asphalt mixtures. To investigate the loading transfer mechanism of the mineral aggregate skeleton, the uniaxial penetration test and Discrete Element Method (DEM) were conducted for the Mineral Aggregate Mixture (MAM) to analyze its mechanical behavior. The three-dimensional strong force chain (SFC) was identified and evaluated based on the proposed recognition criterion and evaluation indices. The results indicate that 4.75 mm should be the boundary to distinguish the coarse and fine aggregates. The skeleton composed of aggregates located on SFCs has better bearing and transferring loading capacity due to its SFC number, average length, and total length decreasing with an increase in the aggregate size. Compared to SMA-16 and OGFC-16, AC-16 exhibits a higher number and total length of its SFC, a smaller average length of its SFC, and a lower average strength of its SFC. Consequently, AC-16 has a lower bearing and transferring loading capacity than that of SMA-16 and OGFC-16. In addition, approximately 90% of SFCs can only transfer external loading downward through 3–5 aggregates. The average direction angle of the SFC formed by fine aggregates is significantly higher than those formed by coarse aggregates. This indicates that the load transfer range of MAM composed of fine aggregates is noticeably larger, leading to lower loading transfer efficiency.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".