基于改进蜣螂优化算法的Bouc-Wen模型参数识别  

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

作　　者：陈明阳 辛景舟 杨纪鹏 史俊 张洪[1,2] 周建庭[1,2] Chen Mingyang;Xin Jingzhou;Yang Jipeng;Shi Jun;Zhang Hong;Zhou Jianting(School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China;State Key Laboratory of Mountain Bridge and Tunnel Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学土木工程学院,重庆400074 [2]重庆交通大学省部共建山区桥梁及隧道工程国家重点实验室,重庆400074

出　　处：《东南大学学报（自然科学版）》2024年第6期1496-1503,共8页Journal of Southeast University：Natural Science Edition

基　　金：国家自然科学基金资助项目(52208264);四川省交通运输科技资助项目(2023-ZL-03);中国博士后科学基金面上资助项目(2022M720589)。

摘　　要：为提升Bouc-Wen模型参数识别的鲁棒性及精度,提出了一种基于改进蜣螂优化算法(improved dung beetle optimizer,IDBO)的参数识别方法.首先,通过3种不同的策略对蜣螂优化算法进行改进,提高算法的全局寻优能力和局部探索能力;其次,通过合理限制参数的取值范围,采取较少的迭代次数即可得到各参数的最优解;最后,通过数值算例对屈曲约束支撑(BRB)的Bouc-Wen模型进行参数识别,验证所提方法的有效性和鲁棒性,并进一步开展BRB拟静力加载试验验证该方法的实用性.结果表明:该方法即使在20%噪声污染情况下仍能较好地重构出真实曲线,识别出参数的最大相对误差仅为4.86%;与蜣螂优化算法(DBO)、灰狼优化算法(GWO)、哈里斯鹰优化算法(HHO)、鲸鱼优化算法(WOA)和减法平均优化算法(SABO)相比,所提方法精度显著提升,均方根误差(RMSE)均值分别提高了30.68%、8.03%、43.26%、52.63%和49.25%.该方法可用于结构滞回模型识别和结构非线性行为模拟.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.

关 键 词：结构健康监测 参数识别 屈曲约束支撑 蜣螂优化算法 BOUC-WEN模型 

分 类 号：TU352.1[建筑科学—结构工程]