Development of a Fuzzy Probabilistic Methodology for Multiple-Site Fatigue Damage
Bibliographic record
Abstract
A strategy for fuzzy-probabilistic assessment of the impact of multiple-site fatigue damage (MSD) on the fatigue resistance of aging structures is developed. The residual strength of a structure may be significantly reduced by the existence of fatigue damage at multiple locations. Depending on the level of knowledge with which they are known, MSD-related 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 standard deviations are developed. Mechanistic and probabilistic models used to evaluate multi-site fatigue damage are also presented. A probabilistic solution strategy, employing a first-order reliability method, is combined with a response-surface-based fuzzy modeling approach to construct the possibility distributions of the probabilistic safety indicators (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 structures. Moreover, the predicted bounds are dependent on the level of knowledge regarding the fuzzy input parameters, with a greater knowledge producing more narrow bounds. An example problem is used to demonstrate the advantages of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".