Design processes involve several sources of uncertainties in loads and/or parameters that may seriously affect the estimates of performance and reliability of engineering systems. The selection of an appropriate mathematical representation of uncertainty that is based on available information is crucial to obtain realistic results. It is widely recognized that the credibility of traditional probabilistic methods is unquestionable when large data sets are available; when only limited data are available to define the probabilistic distribution of nondeterministic quantities, one may want to supplement probabilistic analysis by some other techniques. This consideration has aroused the ever-growing interest of researchers toward alternative uncertainty models based on nonprobabilistic concepts.

This special section of the ASCE–ASME Journal of Risk and Uncertainty in Engineering Systems: Part B contains six papers addressing various engineering problems in the context of the nonprobabilistic treatment of uncertainty. A wide spectrum of approaches including imprecise...

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