Distribution-free stochastic model updating of dynamic systems with parameter dependencies

verfasst von

Masaru Kitahara, Sifeng Bi, Matteo Broggi, Michael Beer

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).

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

University of Strathclyde
The University of Liverpool
Tongji University

Typ

Artikel

Journal

Structural safety

Band

97

ISSN

0167-4730

Publikationsdatum

07.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Bauwesen, Sicherheit, Risiko, Zuverlässigkeit und Qualität

Elektronische Version(en)

https://doi.org/10.1016/j.strusafe.2022.102227 (Zugang: Geschlossen)