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

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 QPSO, 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.

An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

“…e proposed method improves Dense-Net [19] by reducing the number of redundant connections, so as to fully reflect low-level local detail feature information. Finally, QPSO [20] is used to optimize the DenseNet structure and increase the number of hyperparameters, which makes the optimization of network structure more automatic and solves the uncertainty problem of artificial selection. Experimental results show that, on CAS_PEAL and self-built datasets, the accuracy of the QPSO-DenseNet algorithm is higher than that of the optimal DenseNet structure selected manually.…”

Utilizing the Improved QPSO Algorithm to Build a WSN Monitoring System

“…This experiment defines the error between the expected output value and the actual output value as a performance indicator, such as Eq. (15) shows:…”

“…Two extremums, the local optimization pbest and the global optimization gbest, are tracked during each iterative search in such a way that the position and the speed of each particle are dynamically updated and the optimization problem is resolved eventually. First proposed by Kennedy et al [15,16], the particle speed and position in a standard PSO are expressed as:…”

Crack identification in plates-type structures using natural frequencies coupled with successful history-based adaptive differential evolution algorithm

“…Mohan et al [24] has presented an approach for the detection of cracks on beams or plane trusses based on the dynamic characteristics of the structure combined with PSO or GA. Ding [25] also did the same work, but they chose other algorithms by comparing I-ABC, DE, PSO and GA algorithm and he favored I-ABC with a normalized cost function value of 0. 0035.Zhang [26] combined the FEM and QPSO to identify mechanical structures damage parameters. The fitness function is a critical element in the final success of the optimization method.…”