Damage identification of arch bridges based on dense convolutional networks and attention mechanisms

XIN Jingzhou1, 2, LIU Qianru2, TANG Qizhi1, 2, LI Jie3, ZHANG Hong1, ZHOU Jianting1, 2

Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (14) : 18-28.

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (14) : 18-28.

Damage identification of arch bridges based on dense convolutional networks and attention mechanisms

  • XIN Jingzhou1,2,LIU Qianru2,TANG Qizhi1,2,LI Jie3,ZHANG Hong1,ZHOU Jianting1,2
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Abstract

Traditional deep learning methods lack the differentiated utilization of network features and the damage identification accuracy is easily affected by the environmental factors. To this end, this paper proposes an arch bridge damage identification method based on the dense convolutional network (DenseNet121) and the attention mechanism (CBAM). Firstly, the arch bridge acceleration data are obtained and converted into time-frequency maps using continuous wavelet transform to construct the damage identification dataset. Secondly, the CBAM is embedded into the DenseNet121 model to enhance the propagation of the fault features and the differential utilization of the features, and the arch bridge damage identification model is obtained after the training. Then, the performance of the damage identification model is evaluated based on the test set and the t-SNE nonlinear dimensionality reduction technique is introduced to visualize the features. Finally, the feasibility and robustness of the method are verified by numerical cases, and the proposed method is further applied to the damage identification of the stiff skeleton arch ribs. The results indicate that the proposed method can increase the weight of useful information and realize the differentiated utilization of network features. Compared with the traditional method, this method obtain a accuracy of 91.67% and 92.78% respectively in single damage identification and multi-damage identification, with higher identification accuracy, strong robustness, and practical value.

Key words

bridge health monitoring / arch bridge / damage identification / DenseNet121 / attention mechanism / feature visualization

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XIN Jingzhou1, 2, LIU Qianru2, TANG Qizhi1, 2, LI Jie3, ZHANG Hong1, ZHOU Jianting1, 2. Damage identification of arch bridges based on dense convolutional networks and attention mechanisms[J]. Journal of Vibration and Shock, 2024, 43(14): 18-28

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