Influence of particle size and gradation on shear strength–dilation relation of granular materials
Bibliographic record
Abstract
Upon close scrutiny of data reported in the literature, taking into account particle-scale characteristics to optimize the precision of the well-known empirical Bolton’s equations and imposing particle-size limits on them is recommended. The present paper examines the potential influence of particle size and grading on the shear strength–dilation relation of granular materials from the results of 276 symmetrical direct shear tests. The applicability of physical symmetrical direct shear tests to interpret the plane strain frictional shearing resistance of granular materials has been widely discussed using the discrete element method (DEM) computer code SiGran. Sixteen different grain-size distribution curves of three different materials were tested at different normal pressures and initial relative densities. It is demonstrated that while the contribution of dilatancy to shear strength is not influenced by the variation in the coefficient of uniformity, C u , in the investigated range, it significantly decreases with increasing mean particle size, D 50 . The coefficients of Bolton’s equations have been, therefore, adjusted to account for D 50 . A comparison of the predictions by the proposed empirical formulas with plane strain friction angle, [Formula: see text], and dilation angle, ψ, data from the literature shows that accounting for the grain size yields more accurate results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".