Structural Damage Recognition Based on the Finite Element Method and Quantum Particle Swarm Optimization Algorithm

Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or structural interior, especially somewhere sensors can't go, minor damage is often hard identified by not only traditional nondestructive testing methods like ultrasonic testing, radiographic testing, magnetic particle testing, penetrant testing, eddy current testing, but also the current popular ultrasonic guided wave based on the piezoelectric wafer, electromagnetic acoustic transducer or magnetostrictive sensor, which is mainly because the response signals are always affected by many structural features. In this article, the advanced global search algorithm, quantum particle swarm optimization algorithm is first combined with the finite element method to accurately recognize the structural damage based on the conductance-frequency spectrum resulted from electromechanical impedance method. Meanwhile, the objective function is designed to compare the difference of peak frequency variations in the experiment and finite element calculation respectively. By adopting the stiffness reduction method of the elements near the structural damage, the identification efficiency is largely improved for no need to repeatedly partition the model grid. And after multiple iteration optimization of the artificial intelligence algorithm - quantum particle swarm optimization algorithm, the identification error of damage parameters including location and degree can be reduced to below 4 percent. Therefore, the combination of finite element method and quantum particle swarm optimization algorithm is quite effective for guaranteeing high accuracy and efficiency for damage parameters' recognition in complex structures.