A methodology for treating non-identifiability in model updating

Abstract

The problem of updating a structural model and its associated uncertainties by utilizing structural response data is addressed. The present paper focuses on the problem of model updating in the general nonidentifiable case for which certain simplifying assumptions available for identifiable cases are not valid. It is shown that in this case, the PDF is distributed in the neighborhood of an extended and extremely complicated manifold of the parameter space. The computational difficulties associated with calculating the highly complex posterior PDF are discussed and an algorithm for an efficient approximate representation of the above manifold and the posterior PDF is presented. Using this approximation, expressions for calculating the uncertain predictive response are established. A numerical example involving noisy data is presented to demonstrate the proposed method