STATISTICAL MODEL UPDATING AND STRUCTURAL PROBABILISTIC DAMAGE IDENTIFICATION BASED ON BAYESIAN METHOD

GAN Lu; CHEN Hui

doi:10.16579/j.issn.1001.9669.2022.01.018

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STATISTICAL MODEL UPDATING AND STRUCTURAL PROBABILISTIC DAMAGE IDENTIFICATION BASED ON BAYESIAN METHOD

更新时间：2022-09-22

- STATISTICAL MODEL UPDATING AND STRUCTURAL PROBABILISTIC DAMAGE IDENTIFICATION BASED ON BAYESIAN METHOD
- Journal of Mechanical Strength Vol. 44, Issue 1, Pages: 133-139(2022)
- 作者机构：
  
  1. 武汉工程大学邮电与信息工程学院机电学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2022.01.018
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GAN Lu, CHEN Hui. STATISTICAL MODEL UPDATING AND STRUCTURAL PROBABILISTIC DAMAGE IDENTIFICATION BASED ON BAYESIAN METHOD. [J]. 44(1):133-139(2022)
DOI：

GAN Lu, CHEN Hui. STATISTICAL MODEL UPDATING AND STRUCTURAL PROBABILISTIC DAMAGE IDENTIFICATION BASED ON BAYESIAN METHOD. [J]. 44(1):133-139(2022) DOI： 10.16579/j.issn.1001.9669.2022.01.018.

摘要

提出了一个用马尔科夫链抽样的贝叶斯模型修正和损伤识别方法,用于对梁结构的损伤识别。首先建立了基于结构振动频率测量,确定其振型的目标函数,然后采用延缓拒绝自适应的马尔科夫链蒙特卡洛方法进行随机抽样,得到在完好状态和损伤状态下结构参数的后验概率,通过比较这两种状态下识别参数的概率密度函数,最终得到结构损伤的概率和损伤程度。以钢悬臂梁为实验算例,验证了钢悬臂梁结构各区域的损伤概率和损伤程度与真实情况吻合。

Abstract

A Bayesian model updating and damage identification method based on Markov chain sampling was proposed for damage identification of stochastics structures. Firstly, an objective function based on the measured frequency and mode shapes was established, and then the Markov chain Monte Carlo method with delayed rejection was used for stochastics sampling, and the posterior probabilities of structural parameters in intact and damage states were obtained. By comparing the probability density functions of the identified parameters in the two states, the probability and degree of structural damage were finally obtained. The experimental example of steel cantilever beam indicated that based on the modified stochastics parameters, the damage probability and damage degree of each region of the structure are consistent with the real situation.

关键词

模型修正损伤识别贝叶斯方法随机结构

Keywords

Model updatingDamage identificationBayesian methodStochastics structure

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