Rigid Body Spring Network Model for Plasticity and Fracture
Bibliographic record
Abstract
In this work the concept of the Rigid Body Spring Network model (RBSN) is extended to account for hysteresis and brittle fracture of structural members.RBSN was proposed by Kawai [1] who employed a simple discrete numerical model that expresses the phenomenological properties of the material based on rigid masses and springs that undergo all the structural deformation.Material is discretized based on Voronoi [2] tessellation into convex polygons that form the discrete rigid bodies of the model.These are interconnected with three zero length springs in the middle of their common facets.The behavior of springs follows the smooth hysteretic Bouc-Wen model [7].Crack initiation and propagation is constrained at the rigid body facets.Random mesh generation, using only a minimum distance criterion is used and effectively minimizes the mesh bias towards crack propagation due to monotonic loading.Crack formation obeys simple cut-off and Mohr-Coulomb type of criteria [3].Based on this formulation, the different states are closely followed and the plastic as well as fracture behavior is manifested for highly stressed regions.Numerical results are presented for 2D plane stress models that validate the proposed method and verify its computational efficiency as compared to standard elastoplastic finite element methods.
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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 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".