Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies
Author
dc.contributor.author
Meruane Naranjo, Viviana
Author
dc.contributor.author
Mahu Sinclair, Javier Antonio
es_CL
Admission date
dc.date.accessioned
2014-12-19T17:35:21Z
Available date
dc.date.available
2014-12-19T17:35:21Z
Publication date
dc.date.issued
2014
Cita de ítem
dc.identifier.citation
Shock and Vibration Volume 2014, Article ID 653279, 14 pages
en_US
Identifier
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dx.doi.org/10.1155/2014/653279
Identifier
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https://repositorio.uchile.cl/handle/2250/126730
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural
damage given the structure’s dynamic characteristics. The most successful applications of vibration-based damage assessment are
model updating methods based on global optimization algorithms. However, these algorithms run quite slowly, and the damage
assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health
monitoring applications. Artificial neural networks (ANN) have recently been introduced as an alternative to model updating
methods. Once a neural network has been properly trained, it can potentially detect, locate, and quantify structural damage in a
short period of time and can therefore be applied for real-time damage assessment. The primary contribution of this research is the
development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can
be identified more easily and more accurately than mode shapes, and they provide the same information.This research addresses
the setup of the neural network parameters and provides guidelines for the selection of these parameters in similar damage
assessment problems. Two experimental cases validate this approach: an 8-DOF mass-spring system and a beam with multiple
damage scenarios.
en_US
Patrocinador
dc.description.sponsorship
This research has been partially funded by Program UINICIA
VID 2011, Grant U-INICIA 11/01, University of
Chile, and by the Fondo Nacional de Desarrollo Cient´ıfico
y Tecnol´ogico (FONDECYT) of the Chilean Government,
Project 11110046.