Applying artificial neural network and wavelet analysis for multiple cracks identification in beams Online publication date: Fri, 29-Aug-2014
by Hossein Aminpour; Foad Nazari; Sara Baghalian
International Journal of Vehicle Noise and Vibration (IJVNV), Vol. 8, No. 1, 2012
Abstract: In this research, two methods for crack detection in structures are presented and compared. The considered structure is a cantilever beam with rectangular cross section. In order to find cracks, firstly, a new technique based on wavelet analysis and finite element method (FEM) is applied. The advantage of this technique is that the crack detection process is more clear and comfortable than previous works. Then the process of crack detection is performed using FEM and combination of two types of artificial neural network (ANN) including radial basis function (RBF) and back-error propagation (BEP) neural networks. For crack identification in the proposed method, firstly, a RBF neural network is used to detect the number of cracks of structure. Then a BEP neural network is trained to detect the locations of cracks. Training of neural networks is performed using obtained data from FEM. Finally obtained results from two methods are compared with each other.
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