Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/14915
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 |
URI: | http://hdl.handle.net/10773/14915 |
DOI: | 10.1007/s11045-015-0342-7 |
ISSN: | 1573-0824 |
Appears in Collections: | CIDMA - Artigos SCG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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MULT-D-15-00043.pdf | 761.15 kB | Adobe PDF | View/Open |
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