基于Kriging模型的多次尝试差分进化贝叶斯有限元模型修正

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Abstract The standard Differential Evolution Adaptive Metropolis (DREAM) algorithm requires parallel computing with multiple Markov chains, which suffers from low convergence efficiency and high computational costs. To address these challenges, this study proposes a novel framework called MT-DREAM(ZS), based on the Kriging model, for multiple-try differential evolution Bayesian finite element model updating. This framework extends the DREAM algorithm by introducing historical vector differential sampling, Snooker update, and multiple-try Metropolis sampling. Furthermore, it utilizes the Kriging model as a surrogate for the finite element model to enable efficient random sampling, allowing for rapid exploration of high-dimensional posterior distributions of correction parameters using a minimal number of parallel chains. To validate the effectiveness of the proposed MT-DREAM(ZS) method, this study conducts the experiment on a consolidated steel plate beam model and compare the updating performance with the standard DREAM algorithm. The results demonstrate that MT-DREAM(ZS) achieves fast convergence of the Markov chains, improving the convergence efficiency by a significant factor of 3.42 compared to DREAM. Moreover, the updating accuracy and stability are enhanced. Additionally, the utilization of the Kriging model substantially reduces computational costs. The proposed framework provides a new approach to address the challenges of low convergence efficiency and high computational costs in the updating of multi-parameter uncertain models.

Key words： Finite element model updating Bayesian estimation Multiple-try Metropolis sampling Differential Evolution Adaptive Metropolis (DREAM) Kriging model

Received: 10 July 2023 Published: 15 May 2024

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	Articles by authors
	QIN Shiqiang
	LI Ning
	SONG Renxian

Cite this article:

QIN Shiqiang,LI Ning,SONG Renxian. Multiple attempts of differential evolution Bayesian finite element model revision based on Kriging model[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(9): 204-213.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I9/204

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