Bayesian identification of a cracked plate using a population-based Markov Chain Monte Carlo method

被引：47

作者：

Nichols, J. M. ^{[1
]}

Moore, E. Z. ^{[2
]}

Murphy, K. D. ^{[2
]}

机构：

[1] USN, Res Lab, Washington, DC 20375 USA

[2] Univ Connecticut, Dept Mech Engn, Storrs, CT 06269 USA

来源：

COMPUTERS & STRUCTURES | 2011年 / 89卷 / 13-14期

关键词：

Bayesian inference; Population-based Markov Chain Monte Carlo; System identification; Damage identification; DIFFERENTIAL EVOLUTION; DAMAGE DETECTION; SIMULATION; OPTIMIZATION; MODELS;

D O I：

10.1016/j.compstruc.2011.03.013

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Estimating damage in structural systems is a challenging problem due to the complexity of the likelihood function describing the observed data. From a Bayesian perspective a complicated likelihood means efficient sampling of the posterior distribution is difficult and standard Markov Chain Monte Carlo samplers may no longer be sufficient. This work describes a population-based Markov Chain Monte Carlo approach for efficient sampling of the damage parameter posterior distributions. The approach is shown to accurately estimate the state of damage in a cracked plate structure using simulated, free-decay response data. The use of this approach in identifying structural damage has not previously been explored. Published by Elsevier Ltd.

引用

页码：1323 / 1332

页数：10

共 25 条

[1] Application of subset simulation methods to reliability benchmark problems [J].