Structural integrity identification based on smart materials and neural networks
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
20/05/2014
20/05/2014
01/01/2000
|
Resumo |
This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically>30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, two sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with an experimental example, an investigation on a massive quarter scale model of a steel bridge section, in order to verify the performance of this proposed methodology. |
Formato |
510-515 |
Identificador |
http://www.thieme-connect.com/ejournals/abstract/10.1055/s-2006-949763 Imac-xviii: A Conference on Structural Dynamics, Vols 1 and 2, Proceedings. Bethel: Soc Experimental Mechanics Inc., v. 4062, p. 510-515, 2000. 0277-786X http://hdl.handle.net/11449/9889 WOS:000086462600077 |
Idioma(s) |
eng |
Publicador |
Soc Experimental Mechanics Inc |
Relação |
Imac-xviii: A Conference on Structural Dynamics, Vols 1 and 2, Proceedings |
Direitos |
closedAccess |
Tipo |
info:eu-repo/semantics/conferencePaper |