Dynamic learning rate neural network training and composite structural damage detection

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 1522-1527
Author(s):  
H. Luo ◽  
S. Hanagud
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Learning Rate ◽  
Neural Network Training ◽  
Structural Damage Detection ◽  
Dynamic Learning ◽  
Network Training

Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Structural Damage ◽  
The State ◽  
Structural Damage Detection ◽  
Bp Neural Networks ◽  
Simply Supported ◽  
Modal Flexibility

Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

Structural Damage Detection Using an Iterative Neural Network

2000 ◽  
Vol 11 (1) ◽  
pp. 32-42 ◽  
Author(s):  
C. C. Chang ◽  
T. Y. P. Chang ◽  
Y. G. Xu ◽  
M. L. Wang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection
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