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A scenario optimization approach to reliability-based design

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  • Rocchetta, Roberto
  • Crespo, Luis G.
  • Kenny, Sean P.

Abstract

This article introduces a scenario optimization framework for reliability-based design given a set of observations of uncertain parameters. In contrast to traditional methods, scenario optimization makes direct use of the available data thereby eliminating the need for creating a probabilistic model of the uncertainty in the parameters. This feature makes the resulting design exempt from the subjectivity caused by prescribing an uncertainty model from insufficient data. Furthermore, scenario theory enables rigorously bounding the probability of the resulting design satisfying the reliability requirements imposed upon it with respect to additional, unseen observations drawn from the same data-generating-mechanism. This bound, which is non-asymptotic and distribution-free, requires calculating the set of support constraints corresponding to the optimal design. In this paper we propose a framework for seeking such a design and a computationally tractable algorithm for calculating such a set. This information allows determining the degree of stringency that each individual requirement imposes on the optimal design. Furthermore, we propose a chance-constrained optimization technique to eliminate the effect of outliers in the resulting optimal design. The ideas proposed are illustrated by a set of easily reproducible case studies having algebraic limit state functions.

Suggested Citation

  • Rocchetta, Roberto & Crespo, Luis G. & Kenny, Sean P., 2020. "A scenario optimization approach to reliability-based design," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
  • Handle: RePEc:eee:reensy:v:196:y:2020:i:c:s0951832019309639
    DOI: 10.1016/j.ress.2019.106755
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    References listed on IDEAS

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    1. Rocchetta, R. & Li, Y.F. & Zio, E., 2015. "Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 47-61.
    2. Norbert Kuschel & Rüdiger Rackwitz, 1997. "Two basic problems in reliability-based structural optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 46(3), pages 309-333, October.
    3. Jianyu Xu & Min Xie & Qingpei Hu, 2019. "Reliability assessment for load‐sharing systems with exponential components using statistical expansion as a correction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(4), pages 998-1010, July.
    4. Clark, Caitlyn E. & DuPont, Bryony, 2018. "Reliability-based design optimization in offshore renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 390-400.
    5. M. C. Campi & S. Garatti, 2011. "A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality," Journal of Optimization Theory and Applications, Springer, vol. 148(2), pages 257-280, February.
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    Cited by:

    1. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Rocchetta, Roberto & Crespo, Luis G., 2021. "A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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