Size Effects on the Void Ratio of Loosely Packed Binary Particle Mixtures
Bibliographic record
Abstract
Studies of binary particle mixtures provide useful insight into the effects of fine particles on the void ratio of natural, multisized geomaterials. The relative amount of fine particles in a mixture significantly changes the void structure and influences the behavior of such materials. This paper presents complimentary experimental evaluations and numerical simulations that show how void ratios change nonlinearly as additional fine particles are included in binary mixtures. Experimental results for a range of particle size ratios are presented. The paper also demonstrates that there are particular percentages of fine particles by weight at which the lowest values of void ratio are achieved. Loosely packed binary mixtures are simulated by a gravitational sphere packing method to further examine the effect of different weight percentages of fine particles on the void structure. The numerical studies are based on Monte Carlo simulations wherein spherical particles are randomly packed. The complex pore structure obtained by random packing prevents any predefined or repetitive packing arrangements, which can lead to the computation of nonrepresentative void ratio values. Results obtained from the numerical simulations are compared with experimental results and confirm the viability of the gravitational sphere packing method to efficiently reproduce realistic packed soil particle systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".