Information-Theoretic Approach for Identifiability Assessment of Nonlinear Structural Finite-Element Models

AbstractThis paper presents an information-theoretic approach for identifiability assessment of model parameters in nonlinear finite-element (FE) model updating problems. Rooted in the Bayesian inference method, the proposed approach uses the Shannon information entropy as a measure of uncertainty i...

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Bibliographic Details
Published in:Journal of engineering mechanics 2019-07, Vol.145 (7)
Main Authors: Ebrahimian, Hamed, Astroza, Rodrigo, Conte, Joel P, Bitmead, Robert R
Format: Article
Language:English
Subjects:
Bayesian analysis
Design of experiments
Distribution functions
Dynamic models
Dynamical systems
Entropy
Entropy (Information theory)
Finite element method
Information theory
Model updating
Nonlinear dynamics
Parameter estimation
Parameter identification
Parameter uncertainty
Probability distribution functions
Statistical inference
System identification
Technical Papers
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Summary:AbstractThis paper presents an information-theoretic approach for identifiability assessment of model parameters in nonlinear finite-element (FE) model updating problems. Rooted in the Bayesian inference method, the proposed approach uses the Shannon information entropy as a measure of uncertainty in the model parameters. The difference in the entropy of a priori and a posteriori probability distribution functions of model parameters, which is referred to as the entropy gain, is used as a measure of information contained in each measurement channel about the model parameters. The entropy gain approach can be used for selection of estimation parameters, optimal sensor placement, and design of experiment. In this study, an approximate expression for the entropy gain is derived, and a three-step process is suggested for the identifiability assessment. The application of the proposed approach is demonstrated for a nonlinear structural system identification problem. Although the focus of this study is on nonlinear structural FE model identifiability, the provided approach can be used for identifiability assessment of other types of linear/nonlinear dynamic models.
ISSN:0733-9399
1943-7889
DOI:10.1061/(ASCE)EM.1943-7889.0001590