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Title: A 2D Hopfield Neural Network approach to mechanical beam damage detection
Author: Almeida, Juliana
Alonso, Hugo
Ribeiro, Pedro
Rocha, Paula
Keywords: 2D Hopfield Neural Network
Euler-Bernoulli beam model
Timoshenko beam model
Damage detection
Issue Date: Oct-2015
Publisher: Springer
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.
Peer review: yes
DOI: 10.1007/s11045-015-0342-7
ISSN: 1573-0824
Appears in Collections:CIDMA - Artigos
SCG - Artigos

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