Fuzzy Probabilistic Assessment of Aging Aircraft Structures Subjected to Multiple Site Fatigue Damage
Bibliographic record
Abstract
Abstract A strategy is developed for fuzzy probabilistic assessment of the fatigue resistance of aging aircraft structures due to multiple site fatigue damage (MSD). The residual strength of an aircraft structure may be significantly reduced by the existence of fatigue damage at multiple locations. Depending on the level of subjectivity and degree of knowledge, MSD-related parameters may be represented as either purely random variables or fuzzy random variables. The membership functions of probabilistic characteristics of fuzzy random variables, namely mean values and standard deviations, are developed. Mechanistic and probabilistic models used to evaluate multi-site fatigue damage are also presented. A probabilistic solution strategy, employing the first order reliability method (FORM), is combined with the response surface-based fuzzy modeling approach to develop possibility distributions of the probabilistic response quantities (namely reliability indices and failure probabilities) for components subjected to multiple site fatigue damage. Instead of providing the traditional single valued, purely probabilistic measure for reliability, the present formulation proves its merit in its ability to combine experimental data with expert knowledge to provide confidence bounds on the structural integrity of aging aircraft. Moreover, the predicted bounds are dependent on the level of knowledge regarding the fuzzy input parameters, with a higher degree of knowledge resulting in more narrow bounds. An example problem is used to demonstrate the advantages of the proposed methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".