Study On Structural Parameter Identification Based On QPSO

Since the last century, the rapid development of physical science constantly has beenpromoting the development of engineering technology and results in the rising of thedimensions and costs of structures continue as well. The safety and durability oflarge-scale structures playing a significante role in national economy and social activitieshas been required highly. Then the structure health monitoring has gradually become thefocus of the academic community.“The healthy statea” of structures, described anddiagnosed by structural parameters mainly including physical parameters and modalparameters, could be identified by using the structural health monitoring according toresponsive signals of structures’ vibration. Therefor, the research of structural parametersidentification is the key point for structural health monitoring.According to the fundamental principles and approaches of swarm intelligentoptimization algorithms, the article mainly studied on the application of the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm for the structural parametersidentification. Specific details are as follows:1)Briefly overviewes the state of the art about the identification of structuralparameters and the application of intelligent optimization algorithm in the field ofstructural health monitoring. In view of the deficiency existing in the traditionalparameter identification method, the method of QPSO algorithm for structural parametersidentification was adoped.2)The background, the basic idea and the calculating process of QPSO algorithm ismainly introduced, and the advantages of QPSO algorithm have been summarized throuthcomparing with the relatively mature research of PSO algorithm.3)A new method of structural parameters identification based on QPSO algorithm ispresented, a simulation for the parameter identification of a six-layer framework iscompleted, and compared the result, which is abtained by using PSO algorithm. It turnsout that the identification with QPSO algorithm indicated the application has theadvantage of high accuracy.The identificaiotn of structural mass and stiffness are notaffected by adding noise to the testing signal,however,the the identification accuracy ofstructural damping is effected.4)The specific application, based on QPSO algorithm and combining with the rationalfraction polynomial, is proposed to recognize structural modal parameters. Then, themethod was applied to the numerical simulation of one six-layer frame structure and asimply supported beam. By contrasting with the data, gained by adopting PSO algorithmand traditional peak method, identification of the approsch shows high precision of themodal parameters. Moreover，adding noise can obviously impact on the identifiedaccuracy of structural damping ratios, but influence on that of frequencies and modal shape less.5)The new method, built on QPSO algorithm and incorporated with cross powerspectrum, is presented in the paper. It was applied to one six-layer frame structure, asimply supported beam and a three-span continous beam in the form of numerialsimulation for modal parameter identification.Compared with the result obtained bytraditional peak method, results indicate that the method for idsentifed structuralfrequency and modal shapes have higher accuracy. Through the study on the method ofnoise resistance found that this method has excellent resistance to noise.