Identification of structural system parameters with hysteresis behavior using particle swarm optimization algorithm

In the present study, the identification of unknown parameters of structural systems with hysteresis behavior has been investigated by the particle swarm optimization algorithm and the Bouc-Wen model has been used to describe the nonlinear behavior as well as the system formulation for simulation. The particle swarm optimization algorithm can detect the Bouc-Wen parameters of a structural system with hysteresis behavior faster and closer than other detection methods due to the lack of need for initial values of model parameters and the lack of early convergence to local optimal regions. In previous studies, the performance of identifying and estimating the unknown parameters of linear systems has been investigated to some extent, but the function of particle swarm optimization algorithm in systems with hysteresis, which is a nonlinear model, has not been investigated. In this study, first, the good performance of particle swarm optimization algorithm in terms of speed and accuracy in identifying unknown parameters of a nonlinear system with hysteresis behavior is investigated and then by comparing the final value of the identified parameters from the simulation, the superiority of this method is shown by estimating the least squares as well as the genetic algorithm with three methods of random selection, roulette wheel and competitive. In the following, the ability of methods to immediately follow the parameters of a structural system, in case the system stiffness changes due to failure, has been evaluated in this study.