A novel algorithm for model uncertainty reduction in trapezoidal fuzzy fault tree risk assessment.

ABSTRACT: Intelligent risk assessment in complex systems increasingly relies on methods like trapezoidal fuzzy fault trees. However, conventional techniques often struggle with accurately calculating top-event probabilities and handling model uncertainty, which undermines the reliability of risk evaluations. To address this, we propose a novel algorithm that effectively reduces model uncertainty in trapezoidal fuzzy fault tree analysis. By leveraging the cut-set theorem and operating directly on trapezoidal fuzzy numbers without defuzzification, our approach preserves the completeness of fuzzy information throughout the calculation. The algorithm accommodates three common fault tree logics: OR-only, AND-only, and mixed OR/AND gates. In a case study on liquid ammonia leakage, the method achieved a 45

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12704870 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature