Updating Finite Element Model Using Stochastic Subspace Identification Method and Bees Optimization Algorithm

Abstract

This study investigates the application of operational modal analysis along with bees optimization algorithm for updating the finite element model of structures. Bees algorithm applies instinctive behavior of honeybees as they look for nectar of flowers. The parameters that needed to be updated are uncertain parameters such as geometry and material properties of the structure. To determine these uncertain parameters, local and global sensitivity analyses have been performed. An objective function is defined based on the sum of the squared errors between the natural frequencies obtained from operational modal analysis and finite element method. The natural frequencies of physical structure are determined by stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. To verify the accuracy of this method, the proposed algorithm is implemented on a three-story structure to update parameters of its finite element model. Moreover, to study the efficiency of bees algorithm, its results are compared with those of the particle swarm optimization, and Nelder and Mead methods. The comparison indicates that this algorithm leads more accurate results with faster convergence.

Finite element model updating using crow search algorithm with Levy flight

“…Moradi investigated the application of bees algorithm in FEMU, which was compared with other optimization methods. Alimouri and Khademi‐Zahedi recognized the natural frequencies of physical structure by stochastic subspace identification method and used bees optimization algorithm to update parameters of its FE model. Bussetta used the discrete‐time Volterra series to update parameters in a nonlinear FE model, which stems from the decoupling of linear and nonlinear parameters and the use of global nonlinear model.…”