An extended finite element method with polygonal enrichment shape functions for crack propagation and stiff interfaceproblems
Bibliographic record
Abstract
Abstract The extended/generalized finite element method has proven significant efficiency for handling crack propagation and internal boundaries. In certain conditions, however, one of the major drawbacks relates to the representation of unrealistic traction oscillations, particularly in stiff interfaces. To the authors' best knowledge, the few remedies found in the literature depend on the type of underlying finite element, which in some aspects limits general applications. Since one of the major sources of oscillations is created by couplings within standard shape functions for certain crack arrangements, it is herein proposed a novel approach based on enrichment Laplace shape functions directly adapted to the underlying geometry of split subdomains. By doing so, all sources of oscillations are effectively removed, while enriched degrees of freedom are defined exclusively on one side of the domain. The performance is studied using both element and structural examples with highly stiff cracks. More importantly, further assessment in more complex crack propagation problems, including mixed‐mode fracture of concrete beams and a peel test, shows excellent agreement with experimental/numerical data in terms of load‐displacement curves and traction profiles. Results are shown to be objective with respect to the mesh for stiffness values virtually representing infinitely stiff interfaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".