Improved approximate Bayesian computation for probabilistic damage identification of structures

被引：0
|
作者：
Fang S.-E. [1 ,2 ]
Chen S. [1 ]
Dong Z.-L. [1 ]
机构：
[1] School of Civil Engineering, Fuzhou University, Fuzhou
[2] National and Local United Research Center for Seismic and Disaster Informatization of Civil Engineering, Fuzhou University, Fuzhou
来源：
Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2019年 / 32卷 / 02期
关键词：
Approximate Bayesian computation; Metropolis Hastings sampling; Posterior probability distribution; Probabilistic damage identification; Stochastic response surface;
D O I：
10.16385/j.cnki.issn.1004-4523.2019.02.005
中图分类号：
学科分类号：
摘要：
An improved approximate Bayesian computation method is developed by incorporating approximate Bayesian computation (ABC), Metropolis Hastings (MH) sampling and stochastic response surface (SRS). The improved method aims to fast estimate the posterior probability distributions of random parameters, which are further used to identify structural damage. Firstly, ABC is combined with the MH sampling where the concepts of "proposal distribution", "acceptance probability" and "dynamic tolerance" are introduced to evaluate the sampling accuracy. Meanwhile, in order to avoid solving the likelihood function of the Bayesian formula, an error function is established to evaluate the similarity between simulated and measured samples. Secondarily, SRS is used to express the relationship between the random parameters and responses of a structure. Then the statistical features of the responses can be fast calculated even facing a large number of samples. Lastly, the improved ABC method is applied to evaluate the parameter posterior probability distributions of a reinforced concrete beam. Probabilistic damage indices before and after damage are also constructed and then compared in order to identify the damage locations and severities of the beam. The analysis results demonstrate that the improved ABC method can effectively reduce the solution difficulty for estimating parameter posterior probability distributions. Simultaneously, the solution efficiency of a Bayesian parameter identification problem can be improved. Most important of all, multiple damage locations can be successfully identified by the improved ABC method, instead of the traditional Bayesian method. © 2019, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
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页码：224 / 233
页数：9
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