华东交通大学学报2025，Vol.42Issue(6)：17-30,14.
基于深度学习的钢桥面板U肋-顶板节点Lamb波损伤检测
Research on Lamb Wave Damage Detection in U-Rib-Deck Joints of Steel Bridge Decks Based on Deep Learning
摘要
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 convolu-tional neural networks(CNN),combined with constructing datasets using Hilbert transform envelope curves,ef-fective identification of typical fatigue damages in U-Rib-Deck joints is achieved.The research results demon-strate:①Under damage conditions,the direct wave packet exhibits a rightward phase shift and amplitude attenu-ation,confirming the feasibility of using time-domain signal changes for damage detection.②The SE-LSTM-CNN model achieved validation accuracy and test accuracy of 93.67%and 95.00%,respectively,with the recog-nition accuracy for all types of damage exceeding 90%,indicating the model's excellent applicability for dam-age detection tasks in steel bridge deck U-Rib-Deck joints.③The 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 valida-tion 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.④The 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 detec-tion in steel bridge deck U-Rib-Deck joints.关键词
桥梁工程/钢桥面板/Lamb波/深度学习/数值仿真Key words
bridge engineering/steel bridge deck/Lamb waves/deep learning/numerical simulation分类
交通工程引用本文复制引用
田亮,宋鹏飞,张海顺,肖飞知,樊立龙,赵健..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):17-30,14.基金项目
天津市自然科学基金项目(24JCYBJC00850) （24JCYBJC00850）
中国铁建股份有限公司科研重大专项(2023-A01) （2023-A01）
中国铁建大桥局集团有限公司科技创新项目(DQJ-2024-B05) （DQJ-2024-B05）
