Details
| Original language | English |
|---|---|
| Article number | 102227 |
| Journal | Structural safety |
| Volume | 97 |
| Publication status | Published - Jul 2022 |
Abstract
This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of the multivariate correlated parameters. In this framework, the marginal distributions are defined as the staircase density functions and the correlation structure is described by the Gaussian copula function. The first four moments of the staircase density functions and the correlation coefficients are updated by an approximate Bayesian computation, in which the Bhattacharyya distance-based metric is proposed to define an approximate likelihood that is capable of capturing the stochastic discrepancy between model outputs and observations. The feasibility of the framework is demonstrated on two illustrative examples and a followed engineering application to the updating of a nonlinear dynamic system using observed time signals. The results demonstrate the capability of the proposed updating procedure in the very challenging condition where the prior knowledge about the distribution of the parameters is extremely limited (i.e., no information on the marginal distribution families and correlation structure is available).
Keywords
- Bayesian model updating, Bhattacharyya distance, Gaussian copula function, Staircase density function, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: Structural safety, Vol. 97, 102227, 07.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Distribution-free stochastic model updating of dynamic systems with parameter dependencies
AU - Kitahara, Masaru
AU - Bi, Sifeng
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding Information: The first author acknowledges the support of the Deutsche Forschungsgemensschaft (DFG, German Research Foundation) — SFB1463-434502799.
PY - 2022/7
Y1 - 2022/7
N2 - This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of the multivariate correlated parameters. In this framework, the marginal distributions are defined as the staircase density functions and the correlation structure is described by the Gaussian copula function. The first four moments of the staircase density functions and the correlation coefficients are updated by an approximate Bayesian computation, in which the Bhattacharyya distance-based metric is proposed to define an approximate likelihood that is capable of capturing the stochastic discrepancy between model outputs and observations. The feasibility of the framework is demonstrated on two illustrative examples and a followed engineering application to the updating of a nonlinear dynamic system using observed time signals. The results demonstrate the capability of the proposed updating procedure in the very challenging condition where the prior knowledge about the distribution of the parameters is extremely limited (i.e., no information on the marginal distribution families and correlation structure is available).
AB - This work proposes a distribution-free stochastic model updating framework to calibrate the joint probabilistic distribution of the multivariate correlated parameters. In this framework, the marginal distributions are defined as the staircase density functions and the correlation structure is described by the Gaussian copula function. The first four moments of the staircase density functions and the correlation coefficients are updated by an approximate Bayesian computation, in which the Bhattacharyya distance-based metric is proposed to define an approximate likelihood that is capable of capturing the stochastic discrepancy between model outputs and observations. The feasibility of the framework is demonstrated on two illustrative examples and a followed engineering application to the updating of a nonlinear dynamic system using observed time signals. The results demonstrate the capability of the proposed updating procedure in the very challenging condition where the prior knowledge about the distribution of the parameters is extremely limited (i.e., no information on the marginal distribution families and correlation structure is available).
KW - Bayesian model updating
KW - Bhattacharyya distance
KW - Gaussian copula function
KW - Staircase density function
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85129513314&partnerID=8YFLogxK
U2 - 10.1016/j.strusafe.2022.102227
DO - 10.1016/j.strusafe.2022.102227
M3 - Article
AN - SCOPUS:85129513314
VL - 97
JO - Structural safety
JF - Structural safety
SN - 0167-4730
M1 - 102227
ER -