Two-StageDamageDetectionMethodBasedon ImprovedParticleSwarm OptimizationAlgorithm
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摘要: 为解决结构多损伤情况下的位置识别和损伤程度判定问题,提出了一种基于改进粒子群优化算法和贝 叶斯理论的两阶段损伤识别方法.该方法采用频率和模态应变能作为损伤定位源数据,分别用基于频率改变和 基于应变能耗散率的识别方法进行损伤信息的初步提取,再利用贝叶斯融合理论对损伤位置进行较为精确的判 定.然后,利用粒子群优化(PSO)算法对损伤位置和程度进行更为精确的二次识别.考虑到简单PSO 算法易陷 入局部最优解,提出了3种改进措施,即粒子位置突变、最优记忆粒子微搜索和双收敛措施.数值仿真结果表明: 采用贝叶斯融合理论可以有效地识别出可能的损伤单元,在此基础上用改进的PSO 算法可以更精确地识别损 伤的位置和程度,同时采用3种改进措施的PSO 算法的识别精度明显优于其他PSO 算法和遗传算法.Abstract: In orderto solvestructural multi-damageidentification problem,atwo-stage identificationmethodbasedontheparticleswarm optimization (PSO)algorithm andthe Bayesiantheorywasproposed.Inthismethod,structuralmodalstrainenergy (MSE)and frequencyareconsideredastwoinformationsources,andthe methodsbasedonfrequency changeand MSE dissipationratioareutilizedtoextractdamageinformation.Then,the Bayesiantheoryisutilizedtointegratethetwoinformationsourcesandpreliminarilydetect structuraldamagelocations.Finally,the PSO algorithm isadoptedto preciselyidentify structuraldamagelocationsandextents.Inordertoimprovetheidentificationresultsofa simplePSOalgorithm,threeimprovedstrategies,particleposition mutation,elitistmicro- searchanddoubleconvergencecriterion,werepresented.Thesimulationresultsforatwo- dimensionaltrussstructureshowthattheBayesiantheorycanidentifythesuspecteddamage locations,theimproved PSO algorithm can preciselydetectthedamageextent,andthe identificationprecisionofthePSOalgorithm withthethreeimprovedstrategiesisobviously betterthanthoseoftheotherPSOalgorithmsandthegeneticalgorithm.
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