Fuzzy Probabilistic Assessment of the Impact of Corrosion on Fatigue of Aircraft Structures
Bibliographic record
Abstract
A strategy for fuzzy-probabilistic assessment of the impact of corrosion on fatigue of aging aircraft structures is developed. Depending on the level of subjectivity and degree of belief with which they are known, corrosion-assisted fatigue crack growth parameters may be represented as either purely random variables or fuzzy random variables. The membership functions of the probabilistic characteristics of fuzzy random variables, namely mean values and coefficients of variation are developed. Probabilistic models for corrosion-assisted fatigue crack growth are also presented. First order reliability method (FORM) is employed to compute the discrete values of reliability indices and probabilities of failure for components subjected to corrosion-assisted fatigue cracking. A fuzzy modeling strategy is then used to compute the possibility distributions of the probabilistic response quantities, namely reliability index and probability of failure. Rather than providing a crisp value for the reliability of aging aircraft, as is conventionally done, the merit of the present formulation lies in its ability to combine experimental data with expert knowledge to provide confidence bounds on aging aircraft structural integrity. The predicted bounds are dependent on the level of knowledge regarding the input parameters, with a high degree of knowledge resulting in narrow bounds. An example problem is used to demonstrate the proposed methodology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".