Statistical Damage Identification Using Substructure-Based Stochastic Finite Element Model Updating

Structural damage identification for large-scale structures with uncertainties consumes numerous computational time and memory. The substructuring method is a model order reduction method, which is helpful to alleviate the computational burden. This paper proposes a statistical damage identification method using substructure-based stochastic finite element model updating to rapidly identify the damage of each element. First, the reduced order model of global structure is derived by retaining a few substructural modes as the reduction basis, where the inertial and elastic effects of discarded modes are considered. Next, the mean and standard deviation of identified structural parameters are rapidly solved by performing first-order perturbation analysis on the finite element model updating-based damage identification function of the reduced order model. Finally, the damage probability of each element is obtained using the mean and deviation of identified structural parameters before and after damage. A numerical one-bay plane frame and a numerical highway bridge are applied to verify the accuracy and efficiency of the proposed method.