Distribution-free stochastic model updating of dynamic systems with parameter dependencies. (July 2022) - Kitahara, Masaru; Bi, Sifeng; Broggi, Matteo; Beer, Michael
Highlights: A distribution-free stochastic model updating framework is proposed. This framework enables to calibrate the dependence structure among parameters. This framework requires no limiting hypotheses on the target distributions. Gaussian copula function is described by marginal staircase densities. Bhattacharyya distance quantifies discrepancy between model outputs and measurements. 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).