Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization

Abstract

Finite element model updating seeks to modify a structural model to reduce discrepancies between predicted and measured data, often from vibration studies. An updated model provides more accurate prediction of structural behavior in future analyses. Sensitivity-based parameter clustering and regularization are two techniques used to improve model updating solutions, particularly for high-dimensional parameter spaces and ill-posed updating problems. In this paper, a novel parameter clustering scheme is proposed which considers the structure of the objective function to facilitate simultaneous updating of disparate data, such as natural frequencies and mode shapes. In a small-scale updating example with simulated data, the proposed clustering scheme is shown to provide moderate to excellent improvement over existing parameter clustering methods, depending on the accuracy of initial model. A full-scale updating example on a large suspension bridge shows similar improvement using the proposed parametrization scheme. Levenberg-Marquardt minimization with Bayesian regularization is also implemented, providing an optimal regularized solution and insight into parametrization efficiency