Structural damage detection by using densely connected convolutional neural network
A structure damage identification network model(E-DenseNet)that combines empirical mode decomposition(EMD)and densely connected convolutional network(DenseNet)is proposed.The collected acceleration signals undergo EMD to obtain multiple intrinsic mode function(IMF)components,and then the weakly correlated IMF components with small absolute values of Pearson correlation coefficients are removed.According to the organization of the input data,three types of E-DenseNet models are set.E-DenseNet1 reconstructs the signal using strongly correlated IMF components to establish one-dimensional single-channel input data.E-DenseNet2 treats each strongly correlated IMF component as a channel to establish one-dimensional multi-channel input data.E-DenseNet3 uses all strongly correlated IMF components to form a two-dimensional matrix to establish two-dimensional single-channel input data.The numerical analysis of a simply supported beam shows that:E-DenseNet1 runs quickly with poor damage detection accuracy.E-DenseNet2 is computationally efficient with high damage detection accuracy.E-DenseNet3 provides good damage detection results but is time-consuming.Compared with one-dimensional multi-channel residual convolutional neural network(ResNet)and standard convolutional neural network(CNN),E-DenseNet2 performs much better in damage detection accuracy.It is thus concluded that E-DenseNet2 ensures both the computational efficiency and the damage detection accuracy.The visualization analysis of E-DenseNet2 exhibits its damage detection process that for different samples of the same damage scenario,a deeper layer outputs more similar features until the fully connected layer provides the most similar output features.
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