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Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints

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  • Keshtegar, Behrooz
  • Chakraborty, Souvik

Abstract

For satisfactory performance of reliability-based design optimization (RBDO) tools, stable and efficient estimation of the nonlinear probabilistic constraints is of utter importance. Unfortunately, popular methods for reliability analysis, such as hybrid chaos control, self-adaptive chaos control and adaptive chaos control, have several drawbacks which include unstable results and slow rate of convergence. To address this issue, a dynamical accelerated chaos control (DCC) –based beta-circle search direction algorithm is proposed. In order to compute the chaos control factor within DCC, a novel merit function is also proposed in this work. The efficiency and robustness of the proposed DCC method have been illustrated with four nonlinear reliability problems and four RBDO examples. Compared to available state-of-the-art methods, the proposed approach is found to be efficient and accurate. This certifies its possible application to realistic RBDO problems.

Suggested Citation

  • Keshtegar, Behrooz & Chakraborty, Souvik, 2018. "Dynamical accelerated performance measure approach for efficient reliability-based design optimization with highly nonlinear probabilistic constraints," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 69-83.
    • Handle: RePEc:eee:reensy:v:178:y:2018:i:c:p:69-83
    DOI: 10.1016/j.ress.2018.05.015
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Wang, Lei & Liu, Yaru & Li, Min, 2022. "Time-dependent reliability-based optimization for structural-topological configuration design under convex-bounded uncertain modeling," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Zhang, Zheng & Wang, Pan & Hu, Huanhuan & Li, Lei & Li, Haihe & Yue, Zhufeng, 2022. "Efficient reliability-based design optimization for hydraulic pipeline with adaptive sampling region," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. 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).
    6. Hao, Peng & Yang, Hao & Wang, Yutian & Liu, Xuanxiu & Wang, Bo & Li, Gang, 2021. "Efficient reliability-based design optimization of composite structures via isogeometric analysis," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

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