An assessment of progressive damage in mechanical joint of GLASS/EPOXY composite under quasi-static loading
Bibliographic record
Abstract
The prediction of crack initiation and propagation of damage initiation and propagation in composite structures has gained significant attention due to the increasing use of these materials in the aerospace industry. In this context, estimating progressive damage in composite ply is crucial, as it refers to the gradual failure and deformation of the structure, which can lead to a reduction in the useful life and safety of the structure. By examining these damages, it is possible to identify the causes and factors contributing to their occurrence and to propose suitable solutions for preventing and repairing the damages. In the present study, an effort is made to develop numerical, analytical, and experimental approaches for modeling and estimating progressive damage at mechanical joints in composite aircraft structures, considering quasi-static loading, including tensile loading. The study incorporates an investigation of damage mechanisms such as fiber breakage, matrix cracking, and delamination that commonly occur in composite laminates under mechanical stress. Combining modeling and experimental results allows for a comprehensive understanding of damage evolution, enabling the formulation of strategies aimed at improving the durability and safety of composite structures in aerospace applications. Ultimately, based on the results of modeling and experiments, strategies will be proposed to enhance the lifespan of the structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".