Research on modal parameters identification of bridge structure based on adaptive signal de-noising method

Ye, Xijun; Sun, Zhuo; Chen, Bingcong

doi:10.1007/s10586-018-2301-1

Cited by 4 publications

(4 citation statements)

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“…By selecting SNR as fitness function, the genetic algorithm is introduced to automatically optimize p and r parameters. en the inverse singular value decomposition is performed to obtain the denoised signal [1,2]. is method is helpful to improve the identification accuracy of bridge structural damage location.…”

Section: Intoductionmentioning

confidence: 99%

“…e decoder is responsible for mapping the hidden layer feature representation to the original input, and its function expression is shown in formula (2) [10]. z � f(x) � s W (1) x + b (1) 􏼐 􏼑,…”

Section: Introduction To Autoencodermentioning

confidence: 99%

“…Formula (1) shows that x represents the input vector, z represents the encoder output vector, W (1) represents the input weight of the hidden layer, b (1) represents the input bias of the hidden layer, and s represents the activation function.…”

Section: Introduction To Autoencodermentioning

confidence: 99%

“…e training method of the autoencoder is unsupervised learning, and the network parameter θ � W (1) , W (2) 􏼈 b (1) , b (2) } is adjusted to make the final output X * as close as possible to the original input X. Its error function is defined as square error, as shown in…”

Section: Introduction To Autoencodermentioning

confidence: 99%

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Research on Bridge Structural Damage Identification

Sun

Sheng

et al. 2022

Scientific Programming

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The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

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Section: Intoductionmentioning

confidence: 99%