Research on Lamb Wave Damage Detection in U-Rib-Deck Joints of Steel Bridge Decks Based on Deep Learning
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1.College of Civil Engineering, Tianjin Chengjian University, Tianjin 300384 , China ; 2.China Railway Construction BridgeEngineering Bureau Group Co., Ltd., Tianjin 300300 , China ; 3.Tianjin Key Laboratory of Prefabricated Bridge IntelligentConstruction Technology and Equipment, Tianjin 300300 , China ;4.Department of Civil Engineering, Tsinghua University, Beijing, 100084 , China

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[U24];U441

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    Abstract:

    To address the challenges in identifying damage characteristics caused by multimodal Lamb wave propagation, dispersion effects, and signal attenuation in complex structures like steel bridge decks, this study proposes a deep learning-based damage detection method for U-Rib-Deck joints in steel bridge decks. By embed-ding squeeze-excitation (SE) attention mechanisms and long short-term memory (LSTM) networks into convolutional neural networks (CNN), combined with constructing datasets using Hilbert transform envelope curves, effective identification of typical fatigue damages in U-Rib-Deck joints is achieved. The research results demonstrate: ① Under damage conditions, the direct wave packet exhibits a rightward phase shift and amplitude attenuation, confirming the feasibility of using time-domain signal changes for damage detection. ② The SE-LSTMCNN model achieved validation accuracy and test accuracy of 93.67% and 95.00%, respectively, with the recognition accuracy for all types of damage exceeding 90%, indicating the model’s excellent applicability for damage detection tasks in steel bridge deck U-Rib-Deck joints. ③ The classification accuracy of the SE-CNN and LSTM-CNN models improved by 1.00% and 3.33%, respectively, compared to the baseline CNN model, while the SE-LSTM-CNN model further improved accuracy by 7.33% and 5.00% compared to the single-improvement models, validating the synergistic effectiveness of SE attention mechanism and LSTM for damage detection in steel bridge deck U-Rib-Deck joints; furthermore, using the envelope curve dataset increased the model’s validation accuracy by 21.33% compared to raw signals, demonstrating this method’s effectiveness in enhancing the SE-LSTM-CNN model’s ability to identify Lamb wave damage features. ④ The intelligent detection software developed based on MATLAB APP Designer achieved full-process optimization for damage detection, reducing errors from human intervention. This research is expected to provide a new technical solution for damage detection in steel bridge deck U-Rib-Deck joints.

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田亮,宋鹏飞,张海顺,等. 基于深度学习的钢桥面板U肋-顶板节点Lamb波损伤检测[J]. 华东交通大学学报, 2025,42(6):17-30.

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  • Received:May 27,2025
  • Revised:
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  • Online: January 15,2026
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