A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds. (December 2021)

• Record Type: Journal Article
• Authors: Rocchetta, Roberto; Crespo, Luis G.
• Is Part Of: Reliability engineering & system safety. Volume 216(2021)
• Publication Date: 2021-12
• Subjects: Reliability-based design optimization -- Scenario theory -- Reliability bounds -- Conditional value-at-risk -- Constraints relaxation -- Lack of data uncertainty -- Convex programs; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107900
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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. Highlights: Scenario optimization programs for risk-based and reliability-based design. Convex data-driven RBDO with theoretical guarantees on the designs' reliability. Feasibility of the optimization is enforced by softening the samples constraints. Distribution-free and non-asymptotic bounds quantify the lack of data uncertainty. Trade-off between the reliability of individual requirements and the design's cost.