Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/110106
Author(s): Almeida, J
Alonso, H
Pedro Leal Ribeiro
Rocha, P
Title: A 2D Hopfield Neural Network approach to mechanical beam damage detection
Issue Date: 2015
Abstract: The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions.
URI: https://hdl.handle.net/10216/110106
Document Type: Artigo em Revista Científica Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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