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Sensitivity‐based singular value decomposition parametrization and optimal regularization in finite element model updating
Author(s) -
Bartilson Daniel T.,
Jang Jinwoo,
Smyth Andrew W.
Publication year - 2020
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2539
Subject(s) - singular value decomposition , regularization (linguistics) , parametrization (atmospheric modeling) , sensitivity (control systems) , finite element method , mathematics , mathematical optimization , model selection , minification , matrix decomposition , algorithm , bayesian inference , bayesian probability , computer science , eigenvalues and eigenvectors , statistics , engineering , artificial intelligence , physics , structural engineering , quantum mechanics , electronic engineering , radiative transfer
Summary Model updating is used to reduce error between measured structural responses and corresponding finite element (FE) model outputs, which allows accurate prediction of structural behavior in future analyses. In this work, reduced‐order parametrizations of an underlying FE model are developed from singular value decomposition (SVD) of the sensitivity matrix, thereby improving efficiency and posedness in model updating. A deterministic error minimization scheme is combined with asymptotic Bayesian inference to provide optimal regularization with estimates for model evidence and parameter efficiency. Natural frequencies and mode shapes are targeted for updating in a small‐scale example with simulated data and a full‐scale example with real data. In both cases, SVD‐based parametrization is shown to have good or better results than subset selection with very strong results on the full‐scale model, as assessed by Bayes factor.