dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T15:26:44Z
dc.date.accessioned2022-10-05T16:39:22Z
dc.date.available2014-05-20T15:26:44Z
dc.date.available2022-10-05T16:39:22Z
dc.date.created2014-05-20T15:26:44Z
dc.date.issued1999-01-01
dc.identifierStructural Health Montoring 2000. Lancaster: Technomic Publ Co Inc., p. 976-985, 1999.
dc.identifierhttp://hdl.handle.net/11449/36831
dc.identifierWOS:000083947300096
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3908475
dc.description.abstractThis paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.
dc.languageeng
dc.publisherTechnomic Publ Co Inc
dc.relationStructural Health Montoring 2000
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.titleSmart structures health monitoring using artificial neural network
dc.typeTrabalho apresentado em evento


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