Artículo
Crack detection in beam-like structures
Fecha de publicación:
10/2009
Editorial:
Elsevier
Revista:
Engineering Structures
ISSN:
0141-0296
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Sensibility analysis of experimentally measured frequencies as a criterion for crack detection has been extensively used in the last decades due to its simplicity. However the inverse problem of the crack parameters (location and depth) determination is not straightforward. An efficient numerical technique is necessary to obtain significant results. Two approaches are herein presented: The solution of the inverse problem with a power series technique (PST) and the use of artificial neural networks (ANNs). Cracks in a cantilever Bernoulli-Euler (BE) beam and a rotating beam are detected by means of an algorithm that solves the governing vibration problem of the beam with the PST. The ANNs technique does not need a previous model, but a training set of data is required. It is applied to the crack detection in the cantilever beam with a transverse crack. The first methodology is very simple and straightforward, though no optimization is included. It yields relative small errors in both the location and depth detection. When using one network for the detection of the two parameters, the ANNs behave adequately. However better results are found when one ANN is used for each parameter. Finally, a combination between the two techniques is suggested.
Palabras clave:
Artificial Neural Network
,
Beam
,
Crack Detection
,
Inverse Method
,
Spinning Beam
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Identificadores
Colecciones
Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Rosales, Marta Beatriz; Filipich, Carlos Pedro; Buezas, Fernando Salvador; Crack detection in beam-like structures; Elsevier; Engineering Structures; 31; 10; 10-2009; 2257-2264
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