Citation

ASCE ASME J. Risk Uncertain. Eng. Syst. A Civ. Eng. 2018; 4(1): 02017003.

Copyright

(Copyright © 2018, American Society of Civil Engineers)

DOI

10.1061/AJRUA6.0000958

PMID

unavailable

Abstract

Engineering models are typically developed based on underlying physics and expressed mathematically under stated assumptions. These models can be used for deterministic or probabilistic analysis or both. In a deterministic analysis, all the variables of the model are treated without any uncertainty; whereas in a probabilistic analysis, most of the variables have uncertainty associated with them that are expressed as random variables. Hence, the outcome of a model using probabilistic analysis is associated with probability values. For the performance of a physical system such as a bridge, the objective might be to calculate the probability of excessive deflection. To put it in a different way, the objective might be to determine the bridge reliability for deflection. Similarly, reliability of a component for other limit states can be calculated, such as stress or buckling. All these component reliabilities can then be combined for various modes or limit states using mathematical relationships to obtain a system reliability. Obviously, this reliability analysis at the system level is quite involved because of the uncertainties in various components. Similarly, risk analysis entails considering the probabilities of failure, or nonperformance, and the consequences associated with particular events of interest or event scenarios for a system. Additionally, optimization offers tools to search for appropriate solution or design configurations for a system. It may involve maximization of cost, weight, or any other parameter of a physical system subject to certain geometric and physical constraints. Few of these constraints could be reliability constraints. Combining reliability analysis, risk analysis and optimization is of great interest to researchers. These concepts have been applied to engineering systems as well as nonengineering systems, such as in social science (e.g., social risk), political science (e.g., game theory for predicting the outcome of presidential elections), finance (e.g., financial risk and insurance), and medicine (e.g., risk factors associated with the occurrence of subdural hematoma, heart attack, or diabetes). Hence the topic of interdisciplinary applications of reliability analysis, risk analysis and optimization to various disciplines is of great interest to researchers and the community as a whole. This special collection focuses on high-end research papers (co)authored by active researchers in the pertinent field worldwide.

Language: en