Poisson’s ratio of granular materials for Mohr-Coulomb elastoplastic model
Bibliographic record
Abstract
The Mohr-Coulomb elastoplastic model is extensively employed in geotechnical engineering. Despite the well-known nonlinear behaviour of granular cohesionless materials under a wide range of normal stress or confining pressure, the Mohr-Coulomb elastoplastic model continues to be largely used in geotechnical engineering, due to its simplicity and large availability in numerous commercialised software. Its application requires the input of a key parameter, named Poisson’s ratio. It is however a big challenge as its value changes with axial strain and confining pressure. In this study, the optimal Poisson’s ratio of granular materials for the Mohr-Coulomb elastoplastic model is determined by reproducing the experimental results of stress-strain relationships through numerical modelling with the Mohr-Coulomb elastoplastic model. The results show that the Poisson’s ratio μ0.02, corresponding to the slope of the volumetric strain against axial strain curve at 2% of the peak deviatoric stress can be used in numerical modelling with the Mohr-Coulomb elastoplastic model as long as the granular material does not exhibit dilation behaviour. When the material exhibits dilation behaviour, the Poisson’s ratio μ0.36, corresponding to the slope of the volumetric strain against axial strain curve at 36% of the peak deviatoric stress, seems to be appropriate in numerical modelling with the Mohr-Coulomb elastoplastic model. In addition, the study also shows that the Mohr-Coulomb elastoplastic model can be used to simulate mechanical behaviour of granular material under small strain, but not appropriate under large strain conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".