Identification of Bouc-Wen model parameters based on improved dung beetle optimizer algorithm

To improve the robustness and accuracy of parameter identification of Bouc-Wen model, a parameter identification method based on the improved dung beetle optimizer (IDBO) is proposed. Firstly, the global optimization and local exploration ability of the DBO algorithm are improved by three different strategies; secondly, the parameters are limited to a reasonable range, and then the optimal solution of each parameter can be obtained by adopting a small number of iterations. Finally, the parameter identification of Bouc-Wen model of buckling-restrained brace (BRB) was conducted numerically to verify the effectiveness and robustness of the proposed method. On this basis, the quasi-static loading tests of BRB were conducted to verify the practicality of the proposed method. The results show that the proposed method can reconstruct the real curve well even under 20% noise, and the maximum relative error of the identified parameters is only 4. 86% . Compared with the dung beetle optimizer(DBO)、grey wolf optimizer(GWO)、Harris hawks optimization(HHO)、whale optimization algorithm(WOA), and subtration-average-based optimizer(SABO), the accuracy of the proposed method is significantly improved, and the mean root mean square error (RMSE) is increased by 30. 68%, 8. 03%, 43. 26%, 52. 63%, and 49. 25%, respectively. The proposed method can be applied to the identification of structural hysteretic model and the simulation of structural nonlinear behavior. © 2024 Southeast University. All rights reserved.