Damage Detection of Thin Plates Using GA-PSO Algorithm Based on Modal Data

In this study, a new approach for detecting damage, its location, and its severity in plate structures using a genetic-particle swarm optimization, which is a hybrid algorithm, is presented. To evaluate the proposed approach, three numerical examples have been simulated; the examples consist of three different plates including an L-shaped two-clamped supported plate, one quarter of a plate with a hole, and a rectangular two-clamped plate. These plate structures have been modeled using thin plate theory, so they are called thin plate. Additionally, dynamic method based on modal data such as natural frequencies and mode shapes is used to formulate objective function. In order to demonstrate the effectiveness of the new proposed approach and the hybrid algorithm, several structures are tested by several different scenarios with and without noise. Then, the scenarios are simulated with genetic and particle swarm optimization algorithms separately. Finally, the obtained results are compared using two sum error indexes which reveal that the results of the hybrid algorithm have less error.