Model Updating of a ۵-story Shear Building by Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm

In recent years, Vibration-based Structural Health Monitoring (SHM) has become a dominant research topic in civil engineering applications. Model updating is extremely necessary to build a reliable model for health condition assessment and operational safety management of the structure. This study focuses on the model updating of a ۵-story shear building. The experimental measurements including story mass and stiffness are available, and a structural model is created in MATLAB to represent the physical behavior of the structure. By model updating, the differences between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm are employed to update the unknown model parameters. The results show that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. But both of the algorithms can accurately represent the dynamic characteristics of the proposed shear building.

کلیدواژه ها:

Model Updating ، Genetics Algorithm (GA) ، Particle Swarm Optimization (PSO) ، Shear Building