Microstructural self-organization in granular materials during failure
Bibliographic record
Abstract
The present paper is concerned with the analysis of microstructural instabilities in granular materials and with their relation to both macroscopic localized and diffuse failure modes. A discrete-element (DEM) computer simulation of deformations in an idealized two-dimensional frictional particle assembly subject to various biaxial loadings—notably drained compression and proportional strain paths—is proposed as a prototype model to investigate the underlying physics of material failure. Based on the transfer of the second-order work criterion to the microscopic level, we seek for contacts tagged as c − within the granular assembly that undergo instabilities during loading history. The DEM computations yield a description of failure as a microstructural self-organization process by which c − contacts aggregate into clusters which can either grow or breakdown as the network of contacts adjusts itself to externally applied loads during deformation history. It is proposed here that there is a close relation between the clustering of c − contacts and the resulting failure mode based on cluster size and spatial distribution. Localized deformations are found to correlate well with sustained growth of the above clusters, while diffuse failure has more to do with smaller clusters experiencing suppressed development. A comprehensive statistical analysis on the clusters lends support to this conclusion.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".