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| Stochastic finite element model updating based on Kriging model and improved MCMC algorithm |
| Received:October 19, 2020 Revised:December 26, 2020 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20201019001 |
| KeyWord:model updating bayesian estimates Markov Chain Monte Carlo (MCMC) algorithm flower pollination algorithm (FPA) kriging model |
| Author | Institution |
| 张雪萍 |
兰州交通大学 机电工程学院, 兰州 |
| 彭珍瑞 |
兰州交通大学 机电工程学院, 兰州 |
| 张亚峰 |
兰州交通大学 机电工程学院, 兰州 |
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| Abstract: |
| Aiming at the problems of poor convergence and high rejection rate of standard Markov Chain Monte Carlo (MCMC) algorithm when the dimension of parameters to be updated is high,an efficient updating method combining flower pollination algorithm and Kriging model is proposed.Firstly the Kriging model is established by using the parameters to be updated as the inputs and the strain mode as the output.The correlation coefficients of the Kriging model are optimized by bat algorithm.Then,the maximum entropy Bayesian method is adopted to estimate the posterior probability density function of parameters.The flower pollination algorithm is incorporated to Metropolis-Hastings (MH) sampling algorithm to improve the local and global search ability.Finally,numerical examples of a three-DOF spring-mass system and a three-dimensional truss are used to verify the proposed model updating method,and relative error of the updated parameters are found to be less than 0.86%.The results show that the updated Markov chains with higher dimensional parameters can converge rapidly and the sample acceptance rate is high,and the proposed method is also robust to random noise. |