Research Article Open Access

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Hong-Zhong Huang1, Liping He1, Yu Liu1, Ning-Cong Xiao1, Yan-Feng Li1 and Zhonglai Wang1
  • 1 University of Electronic Science and Technology of China, China
American Journal of Engineering and Applied Sciences
Volume 6 No. 1, 2013, 95-136

DOI: https://doi.org/10.3844/ajeassp.2013.95.136

Submitted On: 14 May 2012 Published On: 26 March 2013

How to Cite: Huang, H., He, L., Liu, Y., Xiao, N., Li, Y. & Wang, Z. (2013). Possibility and Evidence-Based Reliability Analysis and Design Optimization. American Journal of Engineering and Applied Sciences, 6(1), 95-136. https://doi.org/10.3844/ajeassp.2013.95.136

Abstract

Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

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Keywords

  • Possibility Theory
  • Evidence Theory
  • Design Optimization
  • Per Formability Improvement
  • Various Uncertainties
  • Theoretical Research
  • Computational Development