A Data-Driven Structural Damage Detection Framework Based on Parallel Convolutional Neural Network and Bidirectional Gated Recurrent Unit

Author(s):  
Jianxi Yang ◽  
Fei Yang ◽  
Yingxin Zhou ◽  
Di Wang ◽  
Ren Li ◽  
...  
2019 ◽  
Vol 23 (10) ◽  
pp. 4493-4502 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Shuang Wang ◽  
Yonglong Li ◽  
Haoran Wang ◽  
...  

2011 ◽  
Vol 243-249 ◽  
pp. 5475-5480
Author(s):  
Zhang Jun

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.


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

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