Conventional probabilistic risk assessment (PRA) methodologies (USNRC, 1983; IAEA, 1992; EPRI, 1994; Ellingwood, 2001) conduct risk assessment for different external hazards by considering each hazard separately and independent of each other. The risk metric for a specific hazard is evaluated by a convolution of the fragility and the hazard curves. The fragility curve for basic event is obtained by using empirical, experimental, and/or numerical simulation data for a particular hazard. Treating each hazard as an independent mutually exclusive event can be inappropriate in some cases as certain hazards are statistically correlated or dependent. Examples of such correlated events include but are not limited to flooding induced fire, seismically induced internal or external flooding, or even seismically induced fire. In the current practice, system level risk and consequence sequences are typically calculated using a Fault Tree Analysis (FTA) that uses logic gates to express the causative relationship between events. Furthermore, the basic events in an FTA are considered as independent. Therefore, conducting a multi-hazard PRA using a Fault Tree is quite impractical. In some cases using an FTA to conduct a multi-hazard PRA can even be inaccurate because an FTA cannot account for uncertainties in events and the use of logic gates limits the consideration of statistical correlations or dependencies between the events. An additional limitation of an FTA based PRA is embedded in its inability to easily accommodate newly observed data and calculation of updated risk or accident scenarios under the newly available information. Finally, FTA is not best suited for addressing beyond design basis vulnerabilities. Therefore, in this paper, we present the results from a study on multi-hazard risk assessment that is conducted using a Bayesian network (BN) with Bayesian inference. The framework can consider general relationships among risks from multiple hazards, allows updating by considering the newly available data/information at any level, and evaluate scenarios for vulnerabilities due to beyond design bases events.
Probabilistic risk assessment using fuzzy fault tree analysis based on two types of failure possibility distributions in process industries
