Structural damage identification using Parzen-window approach and neural networks

2007 ◽  
Vol 14 (4) ◽  
pp. 576-590 ◽  
Author(s):  
Yuyin Qian ◽  
Akira Mita
Keyword(s):  
Neural Networks ◽  
Structural Damage ◽  
Damage Identification ◽  
Parzen Window ◽  
Structural Damage Identification ◽  
Window Approach

Structural damage identification based on autoencoder neural networks and deep learning

2018 ◽  
Vol 172 ◽  
pp. 13-28 ◽  
Author(s):  
Chathurdara Sri Nadith Pathirage ◽  
Jun Li ◽  
Ling Li ◽  
Hong Hao ◽  
Wanquan Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Damage Identification

Structural damage identification based on fast S-transform and convolutional neural networks

Structures ◽  
2021 ◽  
Vol 29 ◽  
pp. 1199-1209
Author(s):  
Behzad Ghahremani ◽  
Maryam Bitaraf ◽  
Amir K. Ghorbani-Tanha ◽  
Reza Fallahi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Structural Damage ◽  
Damage Identification ◽  
S Transform ◽  
Structural Damage Identification

scholarly journals Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

2021 ◽  
Vol 11 (6) ◽  
pp. 2610
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Soojin Jang ◽  
Kyohoon Jin ◽  
Youngbin Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Input Data ◽  
Damage Identification ◽  
Beam Model ◽  
Data Normalization ◽  
Structural Damage Detection ◽  
Structural Damage Identification

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

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