A model updating method based on an improved MCMC algorithm

被引：0
|
作者：
Peng Z. [1 ]
Zheng J. [1 ]
Bai Y. [1 ]
Yin H. [1 ]
机构：
[1] School of Mechatronic Engineering, Lanzhou Jiaotong University, Lanzhou
来源：
| 1600年 / Chinese Vibration Engineering Society卷 / 39期
关键词：
Bayesian estimates; Cuckoo algorithm; Markov Chain Monte Carlo(MCMC)algorithm; Model updating; Support vector machine (SVM);
D O I：
10.13465/j.cnki.jvs.2020.04.031
中图分类号：
学科分类号：
摘要：
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%. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：236 / 245
页数：9
相关论文
共 16 条
  • [1] Chen Z., He H., Chen G., Et al., Research on updating method of finite element model considering uncertainty factors, Journal of Vibration Engineering, 6, pp. 921-928, (2017)
  • [2] Liu G., Luo J., Qin Y., Et al., A finite element model updating method based on improved MCMC method, Engineering Mechanics, 33, 6, pp. 138-145, (2016)
  • [3] Beck J.L., Katafygiotis L.S., Updating models and their uncertainties. I: Bayesian Statistical Framework, Journal of Engineering Mechanics, 124, 4, pp. 455-461, (1998)
  • [4] Katafygiotis L.S., Beck J.L., Updating models and their uncertainties. II: Model Identifiability, Journal of Engineering Mechanics, 124, 4, pp. 463-467, (1998)
  • [5] Beck J.L., Au S.K., Bayesian updating of structural models and reliability using markov chain monte carlo simulation, Journal of Engineering Mechanics, 128, 4, pp. 380-391, (2002)
  • [6] Green P.L., Bayesian system identification of a nonlinear dynamical system using a novel variant of simulated annealing, Mechanical Systems & Signal Processing, 52-53, pp. 133-146, (2015)
  • [7] Cheung S.H., Beck J.L., Bayesian model updating using hybrid monte carlo simulation with application to structural dynamic models with many uncertain parameters, Journal of Engineering Mechanics, 135, 4, pp. 243-255, (2009)
  • [8] Yi W., Zhou Y., Li H., Damage assessment research on frame structure based on Bayesian statistical inference, Engineering Mechanics, 26, 5, pp. 121-129, (2009)
  • [9] Wan H., Ren W., Huang T., Stochastic model updating approach by using Bayesian inference, China Journal of Highway and Transport, 29, 4, pp. 67-76, (2016)
  • [10] Andrieu C., Freitas N.D., Doucet A., Et al., An introduction to MCMC for machine learning, Machine Learning, 50, 1-2, pp. 5-43, (2003)
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