Abstract
Engineering design under uncertainty has gained considerable attention in recent years. A variety of reliability analysis strategies and methodologies are taken into account and applied increasingly to accommodate uncertainties. There exist two different types of uncertainties in practical engineering application: aleatory uncertainty, which is classified as objective and irreducible uncertainty with sufficient information on input uncertainty data and epistemic uncertainty, which is a subjective and reducible uncertainty that stems from a lack of knowledge on input uncertainty data. The nature of uncertainty depends on the mathematical theory within which problem situations are formalized. When sufficient data is available, probability theory is very effective to quantify uncertainty. However, when data is scarce or there is lack of information, the probabilistic methodology may not be appropriate. Among several alternative tools, possibility theory and evidence theory have proved to be computationally efficient and stable tools for reliability analysis under aleatory and/or epistemic uncertainty involved in engineering systems. Thus this chapter first attempts to give a better understanding of uncertainty in engineering design with a holistic view of its classifications, theories and design consideration, and then discusses general topics of foundations and applications of possibility theory and evidence theory. The overview includes theoretical research, computational development and performability improvement about possibilistic and evidential methodologies in the area of reliability during recent years, and it especially reveals the capability and characteristics of quantifying uncertainty from different angles. Finally, perspectives on future research directions are stated.
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Huang, HZ., He, L. (2008). New Approaches to System Analysis and Design: A Review. In: Misra, K.B. (eds) Handbook of Performability Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-131-2_31
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