Artículos de revistas
Damage detection in beams by using artificial neural networks and dynamical parameters
Date
2012-06Registration in:
REVISTA FACULTAD DE INGENIERIA-UNIVERSIDAD DE ANTIOQUIA, MEDELLIN, v. 32, n. 63, pp. 141-153, JUN, 2012
0120-6230
Author
Villalba, Jesus D.
Gomez, Ivan D.
Laier, Jose E.
Institutions
Abstract
In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.