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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2011
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my.utm.241592017-10-10T04:39:24Z http://eprints.utm.my/id/eprint/24159/ A comparison of artificial neural network learning algorithms for vibration-based damage detection Goh, Lyn Dee Bakhary, Norhisham Abd. Rahman, Azlan Ahmad, Baderul Hisham TA Engineering (General). Civil engineering (General) 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. Trans Tech Publications 2011 Book Section PeerReviewed Goh, Lyn Dee and Bakhary, Norhisham and Abd. Rahman, Azlan and Ahmad, Baderul Hisham (2011) A comparison of artificial neural network learning algorithms for vibration-based damage detection. In: Advances in Structures. Trans Tech Publications, Switzerland, pp. 2756-2760. ISBN 978-087849206-0 http://dx.doi.org/10.4028/www.scientific.net/AMR.163-167.2756 10.4028/www.scientific.net/AMR.163-167.2756 |
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TA Engineering (General). Civil engineering (General) Goh, Lyn Dee Bakhary, Norhisham Abd. Rahman, Azlan Ahmad, Baderul Hisham A comparison of artificial neural network learning algorithms for vibration-based damage detection |
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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. |
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Book Section |
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Goh, Lyn Dee Bakhary, Norhisham Abd. Rahman, Azlan Ahmad, Baderul Hisham |
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Goh, Lyn Dee Bakhary, Norhisham Abd. Rahman, Azlan Ahmad, Baderul Hisham |
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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 |
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Trans Tech Publications |
publishDate |
2011 |
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http://eprints.utm.my/id/eprint/24159/ http://dx.doi.org/10.4028/www.scientific.net/AMR.163-167.2756 |
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