Discrete Element Modeling of Cone Penetration Tests Incorporating Particle Shape and Crushing
Bibliographic record
Abstract
The effect of particle shape and particle crushing on the results of cone penetration testing (CPT) of granular materials in a calibration chamber has been studied using three-dimensional discrete element modeling. Simulating the whole chamber with a realistic particle size requires a large number of particles, which leads to a large computational time. Both 90° and 30° segments of a calibration chamber were used in this study to reduce computational time. The effect of particle shape was simulated by prohibiting particle rotation or using simple two-ball clumps. Prohibiting particle rotation was found to increase tip resistance significantly compared with free particle rotation, and replacing a single sphere with different shapes of simple two-ball clumps was also found to have an important effect on the tip resistance. Particle crushing was simulated during CPTs by replacing a broken particle with two new equal-sized smaller particles. The results showed that there was a considerable reduction in the tip resistance for the crushing model compared with the noncrushing model, and this reduction increased as the confining stress increased.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".