Reliability assessment of buckling strength for imperfect stiffened panels under axial compression
Bibliographic record
Abstract
Designing stiffened panels requires evaluating reliability of these structures with regards to the collapse limit state as it could be affected by the presence of general localized defects. Considering the case of a small square depression located on the surface panel, the buckling strength under in-plane uniform axial compression was evaluated through nonlinear finite element modelling. Artificial neural networks were introduced for representing the collapse load as function of the key intervening design variables. A full factorial design of experiment table constructed on these variables provided samples for the training phase, and complementary samples were used to test and validate the obtained models. Separating the various sources contributing to variability of the buckling strength, Monte Carlo method was used to evaluate the probability of failure as function of the applied compression load acting on the stiffened panel system. It was found that localized defects have a drastic effect on the reliability probability. For the considered geometric parameters and boundary conditions, the localized defect present on the central segment of the stiffened panel was recognized to be the most severe one.
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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 | 0.002 |
| 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".