A model updating method based on an improved MCMC algorithm

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (4) : 236-245.

PENG Zhenrui,ZHENG Jie,BAI Yu,YIN Hong

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Abstract

The standard Markov Chain Monte Carlo (MCMC) algorithm is not easy to converge and the rejection rate is high, which limits its application.The maximum entropy method was introduced into the Bayesian method to estimate the maximum value of the posterior probability density function of the parameters, and then the updating idea of new bird nest in the cuckoo algorithm was integrated into the Metropolis-Hasting (MH) sampling algorithm to obtain an improved MH sampling algorithm.Meanwhile, support vector machine (SVM) was used to establish the surrogate model between the parameters to be updated and the output of the finite element model to improve the computational efficiency of model updating.A linear system with three degrees of freedom (DOFs) and a plane truss model were used to verify the effectiveness of the proposed method.The results show that the Markov chain of the updated sample has better mixing performance, and low stagnation probability, and the relative error of the updated parameters is less than 2%.

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model updating / Bayesian estimates / support vector machine (SVM) / Markov Chain Monte Carlo（MCMC）algorithm / cuckoo algorithm

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PENG Zhenrui,ZHENG Jie,BAI Yu,YIN Hong. A model updating method based on an improved MCMC algorithm[J]. Journal of Vibration and Shock, 2020, 39(4): 236-245

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