Smart Structures and Systems

Techno-Press (테크노프레스)
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SHM-based probabilistic representation of wind properties: Bayesian inference and model optimization

  • Ye, X.W. (Department of Civil Engineering, Zhejiang University) ;
  • Yuan, L. (Department of Civil Engineering, Zhejiang University) ;
  • Xi, P.S. (Department of Civil Engineering, Zhejiang University) ;
  • Liu, H. (China Railway Major Bridge (Nanjing) Bridge and Tunnel Inspect & Retrofit Co., Ltd.)
  • Received : 2017.12.06
  • Accepted : 2018.03.25
  • Published : 2018.05.25
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Abstract

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

Keywords

Acknowledgement

Supported by : National Science Foundation of China, Central Universities of China

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