Multi-scale analysis of cone penetration test (CPT) in a virtual calibration chamber
Bibliographic record
Abstract
A virtual calibration chamber was developed using a three-dimensional (3D) discrete element method (DEM) to perform cone penetration tests (CPTs) on a discrete analogue of Ticino sand. The macroscale response of the DEM model was previously shown to be in good quantitative agreement with that of analogous physical models. In the current study the performance of the model at meso and microscale levels of resolution is examined. The microscale response is examined using particle displacements and contact force distributions. The mesoscale behaviour is examined using stress and strain fields obtained through appropriate averaging and interpolating procedures. Four CPTs are examined at the steady-state penetration stage. The effects of radial boundary conditions, initial stress state, initial average density, and particle rotational inertia are examined. The ability of the micro and mesoscale data to identify and explain the relevant mechanisms underlying the significant differences in the macroscale response of the models is discussed. Comparisons with similar phenomena observed in physical tests are also highlighted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".