Strength and dilatancy of crushable soils with different gradings
Bibliographic record
Abstract
Peak strength and dilatancy of granular materials generally decrease with increasing mean effective stress, and such a decrease will be enhanced due to the occurrence of particle breakage. This paper presents a simple empirical approach to modify Bolton’s original strength and dilatancy equation for crushable soils with different crushability. The proposed approach is based on data of a series of drained triaxial tests on carbonate soils with five different particle size distributions and three initial relative densities. It is also validated against other published experimental data on various crushable soils, including carbonate soils, limestones, coarse aggregates, and silica sands. The modified relation retains a similar form to Bolton’s equation with only one additional parameter introduced. As a result, the crushing strength-related parameter in the original relation is modified to incorporate the impacts of particle shape, gradings, and mineralogy on particle breakage. This modified parameter tends to increase as soil crushability decreases, which keeps a similar physical meaning to Bolton’s crushing strength-related parameter, and is suitable for a wider range of crushable soils with different gradings. The proposed strength and dilatancy equation for crushable soils yields to Bolton’s equation for strong soil particles where particle breakage is negligible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".