Micromechanical Insights Into Cone Penetration Testing in Granular Soils Using Discrete Element Modeling
Bibliographic record
Abstract
ABSTRACT The cone penetration test (CPT) is a widely used in situ technique for characterizing granular soils, yet interpretation remains challenging due to the complex interplay of soil microstructure, stress history, and probe characteristics. This study applies three‐dimensional discrete element method (DEM) simulations to investigate cone penetration resistance in clean sands under drained conditions, systematically varying key micromechanical parameters and probe properties. The parametric study examines the effects of particle size, void ratio, stress history (OCR), particle stiffness, interparticle friction, rolling resistance, particle shape, cone–soil interface friction, and cone tip angle on cone tip resistance ( q c ) and sleeve friction ( f s ). Results show that denser fabrics, stiffer or more angular grains, higher interparticle friction, and greater rolling resistance significantly increase q c , particularly in dense states where particle interlocking and dilatancy dominate. OCR strongly amplifies penetration resistance, with overburden pressure normalized q c and f s resistances increasing exponentially from OCR = 1 to 16. Probe‐related parameters, including blunter cone angles and rougher cone faces, further elevate q c , while sleeve wear can shift resistance from the sleeve to the cone tip. These findings clarify how micro‐mechanical and probe variables influence CPT measurements and highlight the need to account for them in interpretation frameworks, particularly when assessing soil density, fabric, or stress history from field CPT data.
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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 | 0.000 | 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".