Decision-theoretic reliability sensitivity. (May 2022) - Straub, Daniel; Ehre, Max; Papaioannou, Iason
Abstract: We propose and discuss sensitivity metrics for reliability analysis, which are based on the value of information. These metrics are easier to interpret than other existing sensitivity metrics in the context of a specific decision and they are applicable to any type of reliability assessment, including those with dependent inputs. We develop computational strategies that enable efficient evaluation of these metrics, in some scenarios without additional runs of the deterministic model. The metrics are investigated by application to numerical examples. Highlights: Expected value of partial perfect information (EVPPI) is a generally applicable and easy to interpret sensitivity metric. We present the application of EVPPI for reliability assessments in different decision contexts. We develop efficient computational strategies for the evaluation of the EVPPI. In some cases, the EVPPI is obtained by a mere post-processing of the reliability analysis.