Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

2017 ◽  
Vol 388 ◽  
pp. 154-170 ◽  
Author(s):  
Osama Abdeljaber ◽  
Onur Avci ◽  
Serkan Kiranyaz ◽  
Moncef Gabbouj ◽  
Daniel J. Inman
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
One Dimensional

One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery

2021 ◽  
pp. 73-83
Author(s):  
Onur Avci ◽  
Osama Abdeljaber ◽  
Serkan Kiranyaz ◽  
Sadok Sassi ◽  
Abdelrahman Ibrahim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Rotating Machinery ◽  
One Dimensional

scholarly journals A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

2021 ◽  
Vol 11 (19) ◽  
pp. 9345
Author(s):  
Yingying He ◽  
Hongyang Chen ◽  
Die Liu ◽  
Likai Zhang
Keyword(s):  
Neural Networks ◽  
Fourier Transform ◽  
Damage Detection ◽  
Fast Fourier Transform ◽  
Convolutional Neural Networks ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Deep Convolutional Neural Networks ◽  
Civil Structures ◽  
Frequency Information

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

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.

Sign in / Sign up

Export Citation Format

Share Document