Uncertainties inherent in any engineering problem have been traditionally modeled and processed in the context of the classical probability theory. In recent decades, alternative methods based on nonprobabilistic concepts have been introduced as effective tools for handling uncertainties in the presence of fragmentary or incomplete experimental data. These methods are gaining increasing importance, especially in early design stages, when available data are insufficient to perform a probabilistic analysis.

This Special Issue of the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems: Part B contains 9 papers addressing representative topics in the area of nonprobabilistic treatment of uncertainty. Engineering problems involving hybrid random/interval (Cicirello and Langley, Hu and Du, Alibrandi and Kho), super ellipsoidal (Elishakoff et al.), interval (Muhanna et al., Tangaramvong et al.), and fuzzy-sets based (Behera et al., Walz et al.) uncertainties are analyzed. Novel methods for processing uncertainties are introduced, and the...

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