A new elastoplastic constitutive model for coarse granular aggregates incorporating particle breakage
Bibliographic record
Abstract
A new elastoplastic stressstrain constitutive model is developed for granular coarse aggregates incorporating the degradation of particles during triaxial shearing. Coarse granular aggregates are subjected to breakage during excessive stress changes. Most of the available constitutive models do not consider the degradation of particles during shearing. In the current model, a plastic flow rule has been developed incorporating the energy consumption due to particle breakage during shear deformation. A non-associated flow and a kinematic type yield locus have been adopted in the model. A general formulation for the rate of particle breakage during shearing has been developed and incorporated in the plastic flow rule. The effects of particle breakage on the plastic distortional and volumetric deformations are incorporated in the current model. The stressstrain formulations are developed within the general critical state framework. The model can accurately predict the stressstrain and volume change behaviour of coarse granular aggregates. The plastic dilation and contraction features of coarse aggregates at various confining pressures are well captured, and the strain-hardening and post-peak strain-softening behaviour of coarse granular media is adequately represented. A particular feature of the model is its capability to predict the degree of particle breakage at any stage of shear deformation.Key words: constitutive modelling, coarse granular aggregates, particle breakage, dilatancy, non-associated flow, plasticity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".