TY - JOUR
T1 - A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds
AU - Rocchetta, Roberto
AU - Crespo, Luis G.
PY - 2021/12
Y1 - 2021/12
N2 - Reliability-based design approaches via scenario optimization are driven by data thereby eliminating the need for creating a probabilistic model of the uncertain parameters. A scenario approach not only yields a reliability-based design that is optimal for the existing data, but also a probabilistic certificate of its correctness against future data drawn from the same source. In this article, we seek designs that minimize not only the failure probability but also the risk measured by the expected severity of requirement violations. The resulting risk-based solution is equipped with a probabilistic certificate of correctness that depends on both the amount of data available and the complexity of the design architecture. This certificate is comprised of an upper and lower bound on the probability of exceeding a value-at-risk (quantile) level. A reliability interval can be easily derived by selecting a specific quantile value and it is mathematically guaranteed for any reliability constraints having a convex dependency on the decision variable, and an arbitrary dependency on the uncertain parameters. Furthermore, the proposed approach enables the analyst to mitigate the effect of outliers in the data set and to trade-off the reliability of competing requirements.
AB - Reliability-based design approaches via scenario optimization are driven by data thereby eliminating the need for creating a probabilistic model of the uncertain parameters. A scenario approach not only yields a reliability-based design that is optimal for the existing data, but also a probabilistic certificate of its correctness against future data drawn from the same source. In this article, we seek designs that minimize not only the failure probability but also the risk measured by the expected severity of requirement violations. The resulting risk-based solution is equipped with a probabilistic certificate of correctness that depends on both the amount of data available and the complexity of the design architecture. This certificate is comprised of an upper and lower bound on the probability of exceeding a value-at-risk (quantile) level. A reliability interval can be easily derived by selecting a specific quantile value and it is mathematically guaranteed for any reliability constraints having a convex dependency on the decision variable, and an arbitrary dependency on the uncertain parameters. Furthermore, the proposed approach enables the analyst to mitigate the effect of outliers in the data set and to trade-off the reliability of competing requirements.
KW - Reliability-based design optimization
KW - Scenario theory
KW - convex programming
KW - Reliability Bounds
KW - lack of data uncertainty
KW - conditional value-at-risk (CVaR)
KW - Constraints Relaxation
KW - Conditional value-at-risk
KW - Constraints relaxation
KW - Lack of data uncertainty
KW - Convex programs
KW - Reliability bounds
UR - http://www.scopus.com/inward/record.url?scp=85110625649&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2021.107900
DO - 10.1016/j.ress.2021.107900
M3 - Article
SN - 0951-8320
VL - 216
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 107900
ER -