Parameter identi?cation of Bouc-Wen type models using a modified experience-based learning algorithm

The experience-based learning (EBL) algorithm is a new global optimization algorithm that is free from any algorithm-specific control parameters and has been applied to solve structural damage identification problems. However, similar to other metaheuristic algorithms, the EBL algorithm has its disadvantages. To obtain better searching performance, a modified EBL algorithm is proposed for solving the nonlinear hysteretic parameter identi?cation problem with a Bouc-Wen type model. A new updating equation is introduced to improve the global optimization ability of the algorithm. Numerical studies on a single-degree-of-freedom system with or without degradation and pinching are conducted to investigate the efficiency and robustness of the proposed algorithm. A laboratory test of four precast concrete infill walls is presented and their hysteretic parameters are also successfully identified. Both numerical and laboratory results are compared with those obtained from the original EBL algorithm, a cloud model based fruit fly optimization algorithm, the Jaya algorithm, a particle swarm optimization algorithm and a squirrel search algorithm, demonstrating the superiority of the proposed method for nonlinear hysteretic parameter identi?cation.

Automated summary

A modified EBL algorithm is proposed to solve the nonlinear hysteretic parameter identification problem and is demonstrated to outperform other algorithms in both numerical and laboratory studies.