Abstract
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.
Highlights
Introduction to SDAE ModelSDAE is a common model in deep learning, it is formed by stacking several denoising autoencoders
The encoder is responsible for mapping the input vector to the hidden layer through the activation function, so as to obtain the feature expression of a higher level, as shown in formula (1) [9]. 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]
X represents the original data, x1 represents the data with noise, and y represents the feature obtained by encoding x1 in the hidden layer of the denoising autoencoder, Z represents the original data restored by decoding y, and LD (x,z) represents the error function
Summary
The encoder is responsible for mapping the input vector to the hidden layer through the activation function, so as to obtain the feature expression of a higher level, as shown in formula (1) [9]. 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]. 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. Formula (2) shows that x′ represents the output matrix, W(2) represents the input weight of the output layer, and b(2). E weight and bias can be updated according to the error back propagation and gradient descent algorithm, and the optimal parameter θ can be obtained
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