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An uncertainty-aware dynamic shape optimization framework: Gravity dam design

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  • Abdollahi, Azam
  • Amini, Ali
  • Hariri-Ardebili, Mohammad Amin

Abstract

Uncertainties such as material randomness, manufacturing anomalies, and external loading play an important role in the design of engineering structures. Therefore, reliability-based design optimization (RBDO) is frequently used as a tool to guarantee economic aspects without compromising safety. In this paper, an uncertainty-aware framework is proposed for seismic optimization of structures. This algorithm is founded on the stochastic dynamic stressor, and therefore, reduces the bias due to the aleatory nature of ground motion. Safety constraints are evaluated in quantiles instead of the exact solution of reliability analysis. Kriging approach approximates the actual model, while a global search algorithm solves the RBDO problem. The proposed algorithm is used for the optimal shape design of gravity dams with a series of local and global time-variant/invariant performance indices. Two sets of deterministic and probabilistic shape optimization algorithms were compared to demonstrate the impact of uncertainty quantification. Finally, the framework is extended to a class of generic dams with different heights, concrete strengths, and foundation-to-concrete flexibility. By probabilistic seismic performance evaluation, system safety is guaranteed using the proposed dynamic RBDO compared to the median response in applying a set of as-recorded ground motions. This paper provides a new paradigm for RBDO of structures with dynamic loading and a robust decision-making during dam design.

Suggested Citation

  • Abdollahi, Azam & Amini, Ali & Hariri-Ardebili, Mohammad Amin, 2022. "An uncertainty-aware dynamic shape optimization framework: Gravity dam design," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s095183202200076x
    DOI: 10.1016/j.ress.2022.108402
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    1. Fluixá-Sanmartín, Javier & Escuder-Bueno, Ignacio & Morales-Torres, Adrián & Castillo-Rodríguez, Jesica Tamara, 2020. "Comprehensive decision-making approach for managing time dependent dam risks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. Peng, Yongbo & Ma, Yangying & Huang, Tianchen & De Domenico, Dario, 2021. "Reliability-based design optimization of adaptive sliding base isolation system for improving seismic performance of structures," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Morales-Torres, Adrián & Escuder-Bueno, Ignacio & Serrano-Lombillo, Armando & Castillo Rodríguez, Jesica T., 2019. "Dealing with epistemic uncertainty in risk-informed decision making for dam safety management," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. 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).
    5. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    6. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    7. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    8. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    9. Leimeister, Mareike & Kolios, Athanasios, 2021. "Reliability-based design optimization of a spar-type floating offshore wind turbine support structure," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    10. ToÄŸan, Vedat & Karadeniz, Halil & DaloÄŸlu, AyÅŸe T., 2010. "An integrated framework including distinct algorithms for optimization of offshore towers under uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 847-858.
    11. Ni, Pinghe & Li, Jun & Hao, Hong & Yan, Weimin & Du, Xiuli & Zhou, Hongyuan, 2020. "Reliability analysis and design optimization of nonlinear structures," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    12. Shi, Lei & Lin, Shih-Po, 2016. "A new RBDO method using adaptive response surface and first-order score function for crashworthiness design," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 125-133.
    13. 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).
    14. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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