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Proceedings Paper

Artificial neural networks for structural damage detection and classification
Author(s): Carlos M. Ferregut; Roberto A. Osegueda; Jaime Ortiz
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Paper Abstract

An analysis of artificial neural networks on damage assessment of an aluminum cantilever beam was conducted. The neural networks were trained and tested with deterministic data of resonant frequency information to test their ability in determining the magnitude, location and type of damage on the beam. Being a preliminary study, no experimental data has been included, since no information was found in the literature where neural networks were used in determining the type of damage on a structure. This paper includes a discussion on the theory of neural network and the process involved in developing the architecture for three layer backpropagation neural networks for damage assessment. The neural networks were tested for three types of damage using four damage magnitudes.

Paper Details

Date Published: 20 April 1995
PDF: 13 pages
Proc. SPIE 2446, Smart Structures and Materials 1995: Smart Systems for Bridges, Structures, and Highways, (20 April 1995); doi: 10.1117/12.207718
Show Author Affiliations
Carlos M. Ferregut, Univ. of Texas/El Paso (United States)
Roberto A. Osegueda, Univ. of Texas/El Paso (United States)
Jaime Ortiz, Univ. of Texas/El Paso (United States)

Published in SPIE Proceedings Vol. 2446:
Smart Structures and Materials 1995: Smart Systems for Bridges, Structures, and Highways
Larryl K. Matthews, Editor(s)

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