Finite Element Model Updating Using Primal-Relaxed Dual Global Optimization Algorithm

Abstract

Finite element (FE) modeling has become a powerful tool in predicting the response of various engineering structures. However, predictions from the numerical model often differ from in-situ experimental measurements due to numerous approximations and inaccuracies in the model. The in-situ experimental data obtained from the as-built structure can be used to update selected model parameters to obtain a more accurate FE model that truly reflects the behavior of the as-built structure. This research investigates FE model updating by the modal property difference approach using eigenvalues and eigenvectors. The modal property difference approach is a nonconvex optimization problem, for which generic solvers cannot guarantee global optimality. However, the problem can be reformulated into a biconvex problem so that the global optimum can be found using a primal-relaxed dual (P-RD) decomposition approach. The formulation of the model updating algorithm and examples that demonstrate its functionality are presented in this paper.