A structural modelling framework for prediction of damage development and failure of composite laminates
Bibliographic record
Abstract
This article presents the latest developments of a constitutive modelling framework, CODAM (COmposite DAmage Model), for predicting the non-linear in-plane response of composite laminates using continuum damage mechanics. The methodology is best suited for non-linear structural analysis of large-scale laminated composites whose boundaries do not interfere/interact with the damage zone that develops and grows within the structure. The new development presented here, CODAM2, addresses the deficiencies in both the numerical and material objectivity of the original version of CODAM. While the previous CODAM formulation was essentially a local smeared crack model that was augmented with crack band scaling to overcome one aspect of the numerical objectivity, namely the mesh-sensitivity, CODAM2 introduces a non-local regularisation scheme to alleviate both the spurious mesh dependency and mesh orientation problems that plague all local strain-softening models. Two of the 13 test cases, provided in the third-world wide failure exercise, which were related to the in-plane tensile and compressive loading of open hole specimens, were used in order to demonstrate the effectiveness of CODAM2 in predicting the damage development and the corresponding overall response in such structural loading configurations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".