Discrete element model (DEM) simulations of cone penetration test (CPT) measurements and soil classification
Bibliographic record
Abstract
This paper presents a study on the simulation of cone penetration tests (CPTs) using the discrete element model (DEM) method. This study’s main objective is to investigate the effect of different modeling parameters and simulation configurations on the ability of three-dimensional DEM simulations to replicate realistic CPT tip resistance (q c ) and friction sleeve shear stress (f s ) measurements. The CPT tests were simulated in virtual calibration chambers (VCCs) containing particles calibrated to model the behavior of sand. The parameters investigated included the granular assembly properties, interparticle contact parameters, particle–probe interface characteristics, and simulation configuration. Results indicate that the interparticle contact parameters, boundary conditions, and void ratio have an important role in the tip resistance and friction sleeve measurements obtained from the simulations. Particle-level interactions such as particle displacements and rotations and interparticle contact forces were analyzed throughout to provide insight into the differences in measured CPT response. Interpretation of the q c and f s measurements using soil behavior type (SBT) charts for soil classification indicates that the simulated CPT response is representative of the response of coarse-grained soils measured during field soundings. Analysis of results within the SBT framework can provide insight into the influence of soil particle properties on CPT-based soil classification.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".