Abstract
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
Highlights
Glaser et al [1] as the experts and leaders of bridge structural health monitoring and control point out that in the last thirty years, there are quite many large-scale bridges, railways and highways are built, so in the thirty years, the primary task is maintenance and management to prevent catastrophic accidents, i.e. failure prognostics and health management will be of great concern in future
Zhang et al.: Structural Damage Recognition Based on the FEM and QPSO Algorithm system because of its high excitation voltage, severe energy dissipation, and multiple signal reflections, etc
There are many feature selection methods like firefly algorithm [12], PSO [13], differential evolution [14], and genetic algorithm [15], but in this article, the EMI method sensitive to a structural state change, combined with the AI optimization algorithm based on the concept of PSO is first proposed to recognize minor structural damages, which is mainly because that the PSO algorithm has the characteristics of fewer parameters, simple implementation, fast computing speed, etc. [16]
Summary
Glaser et al [1] as the experts and leaders of bridge structural health monitoring and control point out that in the last thirty years, there are quite many large-scale bridges, railways and highways are built, so in the thirty years, the primary task is maintenance and management to prevent catastrophic accidents, i.e. failure prognostics and health management will be of great concern in future. The EMI method is used to sense the slight structural state changes caused by minor damage, and the QPSO combined with FEM is introduced to identify the structural damage parameter information. In the last few years, the damage index representation method based on statistical analysis is mainly adopted in the structural damage degree identification based on EMI technology, which generally includes RMSD (root mean square deviation, RMSD), MAPD (mean absolute percentage deviation, MAPD), covariance and CCD (correlation coefficient deviation, CCD). This approach is simple and easy to operate, and can quickly determine structural damage. The FEM tool can be used to analyze the structural health status, determine the damage propagation process and extract all kinds of response signals, to lay a solid foundation for structural health monitoring (SHM) and NDT&E in reality
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