A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds

Roberto Rocchetta (Corresponding author), Luis G. Crespo (Corresponding author)

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4 Citations (Scopus)
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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.
Original languageEnglish
Article number107900
Number of pages15
JournalReliability Engineering and System Safety
Volume216
Early online dateJul 2021
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Reliability-based design optimization
  • Scenario theory
  • convex programming
  • Reliability Bounds
  • lack of data uncertainty
  • conditional value-at-risk (CVaR)
  • Constraints Relaxation
  • Conditional value-at-risk
  • Constraints relaxation
  • Lack of data uncertainty
  • Convex programs
  • Reliability bounds

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