A numerical investigation on the effect of rotation on the cone penetration test
Bibliographic record
Abstract
The cone penetration test (CPT) is one of the most popular in situ soil characterization tools. However, the test is often difficult to conduct in soils with high penetration resistance. To resolve the problem, a rotary CPT device has recently been adopted in practice by rotating the rod to increase the penetrability, particularly in deep dense sand. This study investigates the underlying mechanism of the rotation effects from a micromechanical perspective using models based on the discrete element method. With rotation, the cone penetration resistance ( qc) decreases by up to 50%, while the cone torque resistance ( tc) increases gradually. These results are also used to successfully assess existing theoretical solutions. The mechanical work required during penetration is observed to keep rising as the rotational velocity increases. Microscopic variables including particle displacement and velocity field show that rotation reduces the volume of disturbed soil during penetration and drives particles to rotate horizontally, while contact force chain and contact fabric indicate that rotation increases the number of radial and tangential contacts and the corresponding contact forces, forming a lateral stable structure around the shaft, which can reduce the force transmitted to the particles below the cone, thus decreasing the vertical penetration resistance.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".