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A comparison of artificial neural network learning algorithms for vibration-based damage detection

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM),...

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Main Authors: Goh, Lyn Dee, Dee, Dee, Bakhary, Norhisham, Ahmad, Baderul Hisham
Format: Conference or Workshop Item
Published: 2011
Online Access:http://eprints.utm.my/id/eprint/45474/
http://dx.doi.org/10.4028/www.scientific.net/AMR.163-167.2756
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spelling my.utm.454742017-09-20T00:50:06Z http://eprints.utm.my/id/eprint/45474/ A comparison of artificial neural network learning algorithms for vibration-based damage detection Goh, Lyn Dee Dee, Dee Bakhary, Norhisham Ahmad, Baderul Hisham This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance 2011 Conference or Workshop Item PeerReviewed Goh, Lyn Dee and Dee, Dee and Bakhary, Norhisham and Ahmad, Baderul Hisham (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. In: 2011 International Conference On Structures And Building Materials (ICSBM 2011). http://dx.doi.org/10.4028/www.scientific.net/AMR.163-167.2756
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
description This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance
format Conference or Workshop Item
author Goh, Lyn Dee
Dee, Dee
Bakhary, Norhisham
Ahmad, Baderul Hisham
spellingShingle Goh, Lyn Dee
Dee, Dee
Bakhary, Norhisham
Ahmad, Baderul Hisham
A comparison of artificial neural network learning algorithms for vibration-based damage detection
author_facet Goh, Lyn Dee
Dee, Dee
Bakhary, Norhisham
Ahmad, Baderul Hisham
author_sort Goh, Lyn Dee
title A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_short A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_full A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_fullStr A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_full_unstemmed A comparison of artificial neural network learning algorithms for vibration-based damage detection
title_sort comparison of artificial neural network learning algorithms for vibration-based damage detection
publishDate 2011
url http://eprints.utm.my/id/eprint/45474/
http://dx.doi.org/10.4028/www.scientific.net/AMR.163-167.2756
_version_ 1643651751431110656
score 13.211869

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