Structural Engineering and Mechanics

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Nonlinear identification of Bouc-Wen hysteretic parameters using improved experience-based learning algorithm

  • Luo, Weili (School of Civil Engineering, Guangzhou University) ;
  • Zheng, Tongyi (School of Civil Engineering, Guangzhou University) ;
  • Tong, Huawei (School of Civil Engineering, Guangzhou University) ;
  • Zhou, Yun (School of Civil Engineering, Guangzhou University) ;
  • Lu, Zhongrong (Department of Applied Mechanics, Sun Yat-sen University)
  • Received : 2020.02.12
  • Accepted : 2020.05.18
  • Published : 2020.10.10
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Abstract

In this paper, an improved experience-based learning algorithm (EBL), termed as IEBL, is proposed to solve the nonlinear hysteretic parameter identification problem with Bouc-Wen model. A quasi-opposition-based learning mechanism and new updating equations are introduced to improve both the exploration and exploitation abilities of the algorithm. Numerical studies on a single-degree-of-freedom system without/with viscous damping are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of seven lead-filled steel tube dampers is presented and their hysteretic parameters are also successfully identified with normalized mean square error values less than 2.97%. Both numerical and laboratory results confirm that, in comparison with EBL, CMFOA, SSA, and Jaya, the IEBL is superior in nonlinear hysteretic parameter identification in terms of convergence and accuracy even under measurement noise.

Keywords

Acknowledgement

This work is supported by a research grant from the National Natural Science Foundation of China (51808147).

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