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基于GA-BP神经网络的结构损伤位置识别.docx

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基于GA-BP神经网络的结构损伤位置识别 Abstract StructuredamagedetectionusingGA-BPneuralnetworkisapromisingapproachforidentifyingthelocationofstructuraldamage.Inthispaper,weproposeanovelapproachthatcombinesgeneticalgorithmandbackpropagationneuralnetworktoeffectivelyidentifythestructuraldamagelocation.Theproposedapproachistestedonareal-worldstructurewithsimulateddamage,andtheresultsdemonstratetheeffectivenessoftheproposedapproachindetectingthedamagelocation. Introduction Structuredamagecanhavesignificantimpactsonthesafetyandfunctionalityofcivilstructures.Therefore,itiscrucialtoidentifythelocationofstructuraldamagetopreventfailuresandensurethesafetyandreliabilityofstructures.Structuredamagedetectionmethodscanbebroadlycategorizedintotwogroups:model-basedanddata-drivenapproaches.Model-basedmethodsrelyonanalyticalmodelsofthestructure,whiledata-drivenmethodsusemeasuredresponsedatatoidentifythedamagelocation. Amongthedata-drivenmethods,neuralnetworkshavebeenwidelyusedtodetectstructuraldamage.Backpropagation(BP)neuralnetworkisoneofthemostpopularneuralnetworkalgorithmsbecauseofitssimplicityandeffectiveness.However,BPneuralnetworkhassomedisadvantages,suchasgettingtrappedinlocalminima,slowconvergence,anddifficultyindeterminingoptimalparameters.Toovercometheseproblems,geneticalgorithm(GA)techniquecanbeusedtooptimizetheparametersoftheBPneuralnetwork. Inthispaper,weproposeaGA-BPneuralnetworkapproachtodetectthelocationofstructuraldamage.TheGA-BPneuralnetworkcombinesGAandBPneuralnetworktooptimizetheparametersoftheneuralnetworkforbetterperformanceindamagedetection. Methodology TheGA-BPneuralnetworkapproachconsistsofthreemainsteps:preparingthedata,trainingtheneuralnetwork,andidentifyingthedamagelocation.Thedetailsofeachstepareasfollows: Preparingthedata:Thefirststepistocollectresponsedatafromthestructurebeforeandafterdamage.Inthisstudy,afiniteelementmodeliscreatedtosimulatethestructure,anddamageissimulatedbyreducingthestiffnessofcertainelementsinthestructure.Theresponsedataisobtainedbyperformingdynami