A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

被引:13
|
作者
Dee, Goh Lyn [1 ]
Bakhary, Norhisham [2 ]
Rahman, Azlan Abdul [2 ]
Ahmad, Baderul Hisham [2 ]
机构
[1] Univ Teknol MARA, Fac Civil Engn, Shah Alam, Malaysia
[2] Univ Teknol Malaysia, Fac Civil Engn, Johor Baharu, Malaysia
来源
ADVANCES IN STRUCTURES, PTS 1-5 | 2011年 / 163-167卷
关键词
Neural Network; Damage Detection; Learning Algorithm;
D O I
10.4028/www.scientific.net/AMR.163-167.2756
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:2756 / 2760
页数:5
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