Simulations of triaxial compression test for sandy loam soil using PFC3D
Bibliographic record
Abstract
Abstract. A thorough knowledge and research of the soil mechanical properties is essential while studying the dynamics of soil-tool interaction in soil tillage operations. A discrete element model is developed using Particle Flow Code in three dimensions (PFC3D). Four mechanical properties of soil such as Shear strength, Young’s modulus, angle of internal friction and soil cohesion were studied along with the stress and displacements using triaxial compression tests. The results from the experiments performed in University of Manitoba are used to determine and calibrate the model parameter (soil particle stiffness). Unconsolidated undrained triaxial compression tests were performed to study the effect of moisture content and confining pressures on sandy loam soil. The soil moisture content levels: high (27-29% d.b), medium (19-21% d.b) and low (9-11%d.b) were used and the constant dry bulk density as 1325 kg/m3. The confining pressures for the triaxial tests were 50, 100 and 150 kPa at the strain rate of 1% min-1.The model particle stiffness Kn was calibrated for high moisture content level for 50 kPa and 150 kPa. The values of the measured and simulated shear strength were found to be within 5% of relative error for the calibrated values of particle stiffness. The particle stiffness was found to be 4 x 104 N/m and 6 x 103 N/m for 50 kPa and 150 kPa for high moisture level respectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".