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Probabilistic risk assessment (PRA) provides practical techniques for predicting and managing risks (i.e., frequencies and severities of adverse consequences) in many complex engineered systems. In this chapter, we survey methods for PRA and decision making in engineered systems, emphasizing progress in methods for dealing with uncertainties (e.g., via Bayesian belief networks, with dependencies among inputs expressed via copulas), communicating results effectively, and using the results to guide improved decision making by multiple parties (e.g., teams of stakeholders). For systems operating under threats from intelligent adversaries, novel methods (e.g., game-theoretic ideas) can help to identify effective risk-reduction strategies and resource allocations. The focus on methodology reflects the belief of the authors that in hard decision problems, where the risks and the best courses of action are unclear (often because of sparse, ambiguous, or conflicting data), state-of-the-art methodology may be critical to good risk management. This chapter discusses some of the most useful current methodologies, and suggests possible directions for extensions and improvements.
Overview of Risk Analysis for Engineered Systems
Probabilistic risk assessment (PRA) provides a body of practical techniques that can help engineers and risk managers to predict and manage risks (i.e., frequencies and severities of adverse consequences) in a variety of complex engineered systems. Examples of the types of systems to which PRA has been successfully applied include: nuclear power plants (beginning with the Reactor Safety Study (USNRC, 1975) and continuing to the present day); the space shuttle (to which risk analysis has been applied both before and especially after the Challenger disaster); dam and reservoir planning; highways and bridges; emergency planning; terminals and storage facilities for liquefied natural gas and other hazardous chemicals; and electric power generation and planning.
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