Abstract
A frequency response function- (FRF-) based surrogate model for finite element model updating (FEMU) is presented in this paper. Extreme learning machine (ELM) is introduced as the surrogate model of the finite element model (FEM) to construct the relationship between updating parameters and structural responses. To further improve the generalization ability, the input weights and biases of ELM are optimized by Lévy flight trajectory-based whale optimization algorithm (LWOA). Then, LWOA is also applied to obtain the best updating results, where the objective function is defined by the difference between analytical FRF data and experimental data. Finally, a plane truss is used to demonstrate the performance of the proposed method. The results show that, compared with second-order response surface (RS), radial basis function (RBF), traditional ELM, and other optimized ELM, a LWOA-ELM model has higher prediction accuracy. After updating, the FRF data and frequencies have a significant match to the experimental model. The proposed FEMU method is feasible.
Highlights
Due to the capacity for structural identification and health monitoring, finite element method has attracted much more attentions in the past few years
In order to be used for further analysis, the initial FEM should be updated to minimize the error between the analytical responses and the experimental ones, which is called finite element model updating (FEMU) [4]
To the authors’ best knowledge, Extreme learning machine (ELM) and Levy flight trajectory-based whale optimization algorithm (LWOA) have not been explored to solve the FEMU problem. To expand their applications and improve the efficiency of FEMU, in this paper, LWOA is firstly used to optimize input weights and the biases of ELM. en, a LWOA-ELM based surrogate model is established by the updating parameters and corresponding frequency response function (FRF) data to replace the initial FEM. e objective function is established using the residual between the analytical values and the experimental ones
Summary
Due to the capacity for structural identification and health monitoring, finite element method has attracted much more attentions in the past few years. Is kind of technique can be classified into two main categories: the methods based on modal data and the ones based on frequency response function (FRF) data [5,6,7,8,9]. For the former, natural frequencies and mode shapes are introduced to define the objective function [10]. En, a LWOA-ELM based surrogate model is established by the updating parameters and corresponding FRF data to replace the initial FEM. Considering the peak values at specified frequencies, which may overwhelm the influence of the smaller values, the logarithmic (log10H(ωi)) is adopted
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