Structural physical parameter identification based on Bayesian theory and nested sampling
WANG Kunyang, GONG Maosheng, ZUO Zhanxuan
CEA Key Lab of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration (CEA), Harbin 150080, China
Abstract：In the identification of structural parameters based on Bayesian estimation, the most widely used Markov Chain Monte Carlo (MCMC) sampling often encounters the problems such as low sampling efficiency and non-convergence, especially in solving high-dimensional joint posterior function. The Nested sampling is proposed and modified to solve the high-dimensional joint posterior function problem instead of MCMC sampling in the structural parameter identification in this paper. The joint posterior function is derived from the structural acceleration response time-history firstly, and then the prior and likelihood are re-constructed to realize the sampling and identity the structural parameter. The parameters of a 10-story shear numerical building and a 3-story shaking table test structural model are identified by using the Nested sampling method, and the physical parameters such as stiffness and damping ratio obtained. The results show that the proposed method can be used to solve the high dimensional joint posterior function problem and identify the structural parameters efficiently and reliably. It is also shown that the proposed method can be used in parameter identification and damage detection for real engineering structure.
王坤阳，公茂盛，左占宣. 基于贝叶斯理论嵌套抽样的结构物理参数识别研究[J]. 振动与冲击, 2022, 41(7): 74-80.
WANG Kunyang, GONG Maosheng, ZUO Zhanxuan. Structural physical parameter identification based on Bayesian theory and nested sampling. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(7): 74-80.
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