Effect of particle size and particle size distribution on critical state loci of granular soils
Bibliographic record
Abstract
The critical state soil mechanics captures a wide range of stress–strain behaviour in an understandable context. It provides a conceptual framework for predicting soil behaviour and that is why the critical state is a central part of most advanced constitutive models. This study aims at quantifying the effects of both particle size, and particle size distribution on the critical state loci. Two soils, a natural soil and a tailings, were selected and CSLs were identified for twelve uniform and well graded particle size distributions. Mineralogy and particle shapes were rigorously quantified to ensure other factors are not influencing the results. Particle size has a small influence on the CSL in the sand to gravel range, but silts can have a significantly different CSL. In both natural soil and tailings, particle size distribution appears to have a significant influence on the CSL in e − log p′space and little influence in q − p′ space. Well graded soils have lower CSLs compared to uniform ones, that are generally parallel to the CSLs of their dominant constituent, with the exception of convex distributions where progressively finer particles in larger proportions can form structures noticeably less compressible than any of their constituents.
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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 | 0.002 |
| 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 | 0.000 | 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".