A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network

2022 ◽  
Author(s):  
Yingying He ◽  
Likai Zhang ◽  
Zengshun Chen ◽  
Cruz Y. Li
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Structural Damage ◽  
Echo State Network ◽  
Structural Damage Detection ◽  
Civil Structures ◽  
Multi Scale

Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning

2019 ◽  
Vol 23 (10) ◽  
pp. 4493-4502 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Shuang Wang ◽  
Yonglong Li ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Structural Damage ◽  
Deep Convolutional Neural Network ◽  
Structural Damage Detection

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

scholarly journals Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3371 ◽  
Author(s):  
Tao Yin ◽  
Hong-ping Zhu
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Damage Detection ◽  
Network Architecture ◽  
Structural Damage ◽  
Multiple Target ◽  
Structural Damage Detection ◽  
Bayesian Neural Network ◽  
Bridge Model ◽  
Hidden Layer

Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number of hidden neurons, the type of transfer function used in the hidden layer cannot be neglected for the ANN design. Neural network learning can be further presented in the framework of Bayesian statistics, but the issues of selection for the hidden layer transfer function with respect to the Bayesian neural network has not yet been reported in the literature. In addition, most of the research works in the literature for addressing the predictive distribution of neural network output is only for a single target variable, while multiple target variables are rarely involved. In the present paper, for the purpose of probabilistic structural damage detection, Bayesian neural networks with multiple target variables are optimally designed, and the selection of the number of neurons, and the transfer function in the hidden layer, are carried out simultaneously to achieve a neural network architecture with suitable complexity. Furthermore, the nonlinear network function can be approximately linear by assuming the posterior distribution of network parameters is a sufficiently narrow Gaussian, and then the input-dependent covariance matrix of the predictive distribution of network output can be obtained with the Gaussian assumption for the situation of multiple target variables. Structural damage detection is conducted for a steel truss bridge model to verify the proposed method through a set of numerical case studies.

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