Bayesian RC-Frame Finite Element Model Updating and Damage Estimation Using Nested Sampling with Nonlinear Time History

被引：1
|
作者：
Wang, Kunyang [1 ]
Kajita, Yukihide [2 ]
Yang, Yaoxin [3 ]
机构：
[1] Kyushu Univ, Grad Sch Engn, Dept Civil Engn, Div Struct & Earthquake Engn, Fukuoka 8190385, Japan
[2] Kyushu Univ, Fac Engn, Dept Civil & Struct Engn, Fukuoka 8190385, Japan
[3] YJK Bldg Software, Beijing 100013, Peoples R China
来源：
BUILDINGS | 2023年 / 13卷 / 05期
关键词：
Bayesian model updating; structural health monitoring; nested sampling; Bayesian model selection; finite element model; nonlinear model; damage degree estimation; MONTE-CARLO-SIMULATION; DYNAMIC-RESPONSE; SEISMIC DAMAGE;
D O I：
10.3390/buildings13051281
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are selected through nested sampling, and their probability density is estimated using nonlinear time history. In the first step, we estimate the error standard deviation and select the FEM parameters that are required to be updated by FEMU. In the second step, we estimate the probability density of the selected parameters and realize the FEMU through the resampling method and kernel density estimation (KDE). Additionally, we propose a damage state estimate approach, which is a derivative method of the FEMU sample. The numerical results demonstrate that the proposed approach is reliable for the Bayesian FEMU and damage state estimation using nonlinear time history.
引用
收藏
页数：15
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