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Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties

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  • Zheng, Xiao-Wei
  • Li, Hong-Nan
  • Gardoni, Paolo

Abstract

The aleatory and epistemic uncertainties coming from various sources have significant impacts on the accuracy of risk estimates for structures under dynamic excitations. This paper presents a hybrid Bayesian-Copula-based method for assessing the wind-induced risk of tall buildings incorporating various uncertainties. Firstly, according to the recorded wind data and virtual dynamic analysis data, the Bayes theorem is used to develop the posterior probability distributions of the unknown parameters in the marginal probability distribution of wind speed and direction as well as in the demand model for fragility estimates. Then, in a Bayesian-based framework, the Copula technique is used to construct the joint probability model of wind speed and direction by linking their corresponding marginal distributions. The epistemic uncertainty in the unknown model parameters is incorporated into the risk estimates by the total probability theory. The application of this study indicates that the epistemic uncertainty has obvious influences on both the deformation- and comfort-based total damage probability. Compared to existing studies, the main contribution of this paper is that the presented Bayesian-based framework can well consider the epistemic uncertainty associated with unknown model parameters and the aleatory uncertainty, which are the significant difference between previous studies.

Suggested Citation

  • Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000157
    DOI: 10.1016/j.ress.2023.109100
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    5. Zeng, Zhiguo & Barros, Anne & Coit, David, 2023. "Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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