Theory and application of possibility and evidence in reliability analysis and design optimization

Abstract

Numerous design optimization methodologies and reliability analysis techniques have been developed to address aleatory and epistemic uncertainties in engineering system design. Aleatory uncertainty is modeled by statistical distributions, while epistemic uncertainty becomes an alternative in cases where data is sparse and cannot be fully captured statistically. Possibility and evidence theories are computationally efficient and robust for quantifying epistemic uncertainty in reliability analysis and design optimization. This paper provides a comprehensive analysis of existing methodologies, challenges, and opportunities in managing uncertainty in engineering systems. Additionally, the concepts and practical applications of possibility and evidence theories are reviewed. Potential future research directions are outlined ultimately. This paper provides the sector with a clear understanding of possibility theory and evidence theory and their developments.