Vibration-based damage detection in cable-stayed bridges using a novel 1D-ConvNeXt-LSTM network
Email:
nguyenngoclong@utc.edu.vn
Từ khóa:
Cable-stayed bridge; time-series data; structural health monitoring; damage detection; one dimensional-convnext; long short-term memory.
Tóm tắt
Structural health monitoring (SHM) plays a crucial role in maintaining the safety and serviceability of civil infrastructure, such as cable-stayed bridges. However, simultaneously extracting detailed local signal features and long-range temporal dependencies from vibration data remains a significant challenge for standalone deep learning architectures like LSTM or ResNet1D. To address this limitation, this study proposes an advanced hybrid architecture, 1D-ConvNeXt-LSTM, for structural damage detection. This framework integrates the multi-scale feature extraction capabilities of 1D-ConvNeXt with the sequential modeling proficiency of Long Short-Term Memory (LSTM) networks. The proposed method was evaluated using vibration time-series data acquired from a scaled cable-stayed bridge model under five distinct simulated damage scenarios. Experimental results demonstrate that the 1D-ConvNeXt-LSTM model achieves superior classification performance, yielding a macro–Area Under the Curve (AUC) of 0.971 and a macro F1-score of 0.814, significantly outperforming baseline architectures including FCN, ResNet1D, and LSTM. Ultimately, the proposed architecture provides a robust, stable, and highly accurate solution for structural condition assessment, thereby enhancing the effectiveness of damage identification in practical SHM applications.Tài liệu tham khảo
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[4]. B. F. Spencer Jr, T. Nagayama, J. A. Rice, Decentralized structural health monitoring using smart sensors, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE, 6932 (2008) 693202. https://doi.org/10.1117/12.791077
[5]. B. Peeters, G. De Roeck, One-year monitoring of the Z24-Bridge: Environmental effects versus damage events, Earthquake Engineering and Structural Dynamics, (2001). https://doi.org/10.1002/1096-9845(200102)30:2%3C149::AID-EQE1%3E3.0.CO;2-Z
[6]. A. Tjirkallis, A. Kyprianou, Damage detection under varying environmental and operational conditions using Wavelet Transform Modulus Maxima decay lines similarity, Mechanical Systems and Signal Processing, 66-67 (2015) 282-297. https://doi.org/10.1016/j.ymssp.2015.04.008
[7]. S. Doebling, C. Farrar, M. Prime, A Summary Review of Vibration-Based Damage Identification Methods, Shock Vib. Dig., 30 (1998) 91-105. https://doi.org/10.1177/058310249803000201
[8]. M. Khanahmadi, B. Mirzaei, B. Dezhkam, O. Rezaifar, M. Gholhaki, G. G. Amiri, Vibration-based health monitoring and damage detection in beam-like structures with innovative approaches based on signal processing: A numerical and experimental study, Structures, 68 (2024) 107211. https://doi.org/10.1016/j.istruc.2024.107211
[9]. V. M. Karbhari, L. S.-W. Lee, Vibration-based damage detection techniques for structural health monitoring of civil infrastructure systems, Structural Health Monitoring of Civil Infrastructure Systems, (2009) 177-212. https://doi.org/10.1533/9781845696825.1.177
[10]. Yu. Yu. Shatilov, A. A. Lyapin, A. V. Cherpakov, S. G. Glushko, Vibration based damage detection of bridge structures, E3S Web of Conferences, 402 (2023) 12016. https://doi.org/10.1051/e3sconf/202340212016
[11]. V.-M. Trung, H.-T. Ngoc, M.-T. Duc, L.-B. Phuc, L.-N. Duc, An Effective Damage Detection Approach for a Truss Bridge Using a Hybrid Deep Learning Model, (2025) 91-101. https://doi.org/10.1007/978-981-96-5206-8_10
[12]. H. Tran-Ngoc, Q. Nguyen-Huu, T. Nguyen-Chi, T. Bui-Tien, Enhancing damage detection in truss bridges through structural stiffness reduction using 1DCNN, BiLSTM, and data augmentation techniques, Structures, 68 (2024) 107035. https://doi.org/10.1016/j.istruc.2024.107035
[13]. S.-Y. Kim, M. Mukhiddinov, Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network, Sensors, 23 (2023) 8525. https://doi.org/10.3390/s23208525
[14]. N. C. Thi Nguyen, T. M. Vu, Damage detection in structural health monitoring using BiLSTM-1DCNN hybrid network: a case study on a large-scale steel truss bridge, Eng. Comput., 42 (2025) 2226-2242. https://doi.org/10.1108/EC-08-2024-0714
[15]. S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Comput., 9 (1997) 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[16]. Y. Lee, J. H. Lee, J.-S. Kim, H. Yoon, A Hybrid Approach of Long Short-Term Memory and Machine Learning With Acoustic Emission Sensors for Structural Damage Localization, IEEE Sens. J., 24 (2024) 39529-39539. https://doi.org/10.1109/JSEN.2024.3481411
[17]. N. Lu, Y. Liu, J. Cui, X. Xiao, C. Kang, ConvNeXt-DSAN: An efficient unsupervised learning framework for structural damage identification with monitoring data, Measurement, 261 (2025) 119951. https://doi.org/10.1016/j.measurement.2025.119951
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Nhận bài
26/03/2026
Nhận bài sửa
23/04/2026
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27/04/2026
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15/05/2026
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Kiểu trích dẫn
Ho Xuan, N., Vu Manh, T., Nguyen Nam, S., & Nguyen Ngoc, L. (1778778000). Vibration-based damage detection in cable-stayed bridges using a novel 1D-ConvNeXt-LSTM network. Tạp Chí Khoa Học Giao Thông Vận Tải, 77(4), 485-499. https://doi.org/10.47869/tcsj.77.4.11
