CRACK DETECTION IN BEAM-LIKE STRUCTURES

MARTA B. ROSALES; CARLOS P. FILIPICH; FERNANDO S. BUEZAS

ENGINEERING STRUCTURES

Elsevier

Año: 2009 vol. 31 p. 2257 - 2264

0141-0296

Sensibility analysis of experimentally measured frequencies as a criterion for crack detection has been extensively used in the last decades given 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 (ANN?s). 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 ANN?s technique does not need of a previous model but a training set of data. 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.