Structural damage detection by wavelet transform and probabilistic neural network

2005 ◽  
Author(s):  
Guirong Yan ◽  
Zhongdong Duan ◽  
Jinping Ou
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Structural Damage Detection

Effect of Attribute Reduction on Rough-Probabilistic Neural Network for Structural Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2482-2487
Author(s):  
Shao Fei Jiang ◽  
Zhao Qi Wu
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Probabilistic Neural Network ◽  
Attribute Reduction ◽  
Measurement Data ◽  
Noise Tolerance ◽  
Structural Damage Detection ◽  
Proposed Model

In this paper, a new rough-probabilistic neural network (RSPNN) model, whereby rough set data and a probabilistic neural network (PNN) are integrated, is proposed. This model is used for structural damage detection, particularly for cases where the measurement data has many uncertainties. To verify the proposed method, an example is presented to identify both single and multi-damage case patterns. The effects of measurement noise and attribute reduction on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also reduces data storage memory requirements.

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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