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Time-dependent reliability-based optimization for structural-topological configuration design under convex-bounded uncertain modeling

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  • Wang, Lei
  • Liu, Yaru
  • Li, Min

Abstract

In this work, a novel convexity-oriented time-dependent reliability-based topology optimization (CTRBTO) framework is investigated with overall consideration of universal uncertainties and time-varying natures in configuration design. For uncertain factors, the initial static ones are quantified by the convex set model and nodal dynamic responses are then expressed by the convex process model, where both the boundary rules and time-dependency properties are revealed by the full-dimensional convex-set collocation theorem. Unlike the original deterministic constraints in topology optimization schemes, a new convex time-dependent reliability (CTR) index is defined to give a reasonable failure judgment of local dynamic stiffness and impel the overall CTRBTO strategy. In addition, the gradient-based iterative algorithm is utilized to guarantee the computational robustness and the CTR-driven design sensitivities are explicitly analyzed by the Lagrange multiplier method. Several numerical examples are used to illustrate the effectiveness of the proposed method, and numerical results reflect the significance of this study to a certain extent.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:221:y:2022:i:c:s0951832022000400
    DOI: 10.1016/j.ress.2022.108361
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    References listed on IDEAS

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    1. Cheng, Kai & Lu, Zhenzhou, 2019. "Time-variant reliability analysis based on high dimensional model representation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 310-319.
    2. Hall, Jim W., 2006. "Uncertainty-based sensitivity indices for imprecise probability distributions," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1443-1451.
    3. 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.
    4. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Kroetz, H.M. & Moustapha, M. & Beck, A.T. & Sudret, B., 2020. "A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. 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).
    8. Cheng, Kai & Lu, Zhenzhou, 2021. "Adaptive Bayesian support vector regression model for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
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    Cited by:

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    2. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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