Unsupervised learning for structural health monitoring: A multi-station GNSS-RTK approach for operational state detection in cable-stayed bridges
Email:
gianglk@utc.edu.vn
Từ khóa:
cable-stayed bridge, GNSS-RTK, structural health monitoring, unsupervised learning, clustering, operational state detection
Tóm tắt
Monitoring the operational condition of cable-stayed bridges is essential for ensuring structural safety and long-term serviceability. GNSS-RTK technology has emerged as an effective solution for continuous, high-precision displacement measurement in structural health monitoring (SHM). However, high-frequency GNSS data are often noisy and unlabeled, posing significant challenges for traditional analysis methods. This study addresses the problem of identifying structural operational states from unlabeled GNSS-RTK data using an unsupervised learning framework. Displacement data collected from two GNSS stations installed symmetrically on a cable-stayed bridge were segmented into hourly windows and preprocessed using Hampel filtering and Kalman smoothing. Feature vectors were then standardized and reduced in dimensionality using Principal Component Analysis, while t-SNE was applied for visualization. Clustering algorithms, including K-Means, hierarchical agglomerative clustering, Gaussian mixture models, and DBSCAN, were implemented and evaluated using multiple internal validation indices and stability metrics. The results reveal a consistent two-state structure in the horizontal displacement component, with K-Means achieving the best performance (Silhouette score up to 0.7006) and perfect stability (ARI = 1.0). Importantly, the clustering results are highly consistent across both stations, indicating strong spatial reliability. These findings demonstrate that unsupervised learning can effectively extract meaningful operational states from GNSS-RTK data, providing a robust and scalable approach for structural health monitoring of large-scale bridges.Tài liệu tham khảo
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[2]. K. G. Le, D. C. Tran, T. L. H. Ho, Self-supervised deep learning for GNSS time series imputation: A comparative study of neural network architectures, Transport and Communications Science Journal, 77 (2026) 127–141. https://doi.org/10.47869/tcsj.77.1.10
[3]. P. J. G. Teunissen, O. Montenbruck, Springer Handbook of Global Navigation Satellite Systems, Springer International Publishing, Cham, 2017.
[4]. K. Worden, C. R. Farrar, G. Manson, G. Park, The fundamental axioms of structural health monitoring, in: Proceedings of the Royal Society A, 463 (2082) (2007) 1639–1664. https://doi.org/10.1098/rspa.2007.1834
[5]. T. L. H. Ho, D. C. Tran, V. V. Le, K. G. Le, Latent pattern recognition in GNSS-based SHM using t-SNE and adaptive time-series modeling, Journal of Civil Structural Health Monitoring, 15 (2025) 3743–3766. https://doi.org/10.1007/s13349-025-01014-9
[6]. D. C. Tran, T. L. H. Ho, K. G. Le, V. H. Le, Application of unsupervised clustering algorithms in GNSS-RTK data analysis for cable-stayed bridge monitoring, Transport and Communications Science Journal, 76 (2025) 1138-1150. https://doi.org/10.47869/tcsj.76.8.8 (in Vietnamese).
[7]. Ministry of Transport of Vietnam, Design and installation documentation of Nhat Tan Bridge monitoring system, 2015.
[8]. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 12 (2011) 2825–2830.
[9]. H. Abdi, L. J. Williams, Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2010) 433–459. https://doi.org/10.1002/wics.101
[10]. L. van der Maaten, G. Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, 9 (2008).
[11]. A. Dudek, Silhouette index as clustering evaluation tool, in: Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, Springer, (2019) 19–33. https://doi.org/10.1007/978-3-030-52348-0_2
[12]. D. A. Reynolds, Gaussian mixture models, Encyclopedia of Biometrics (2009) 659–663. https://doi.org/10.1007/978-0-387-73003-5_196
[13]. F.-M. Bi, W. Wei, C. Long, DBSCAN: Density-based spatial clustering of applications with noise, Journal of Nanjing University (Natural Sciences), 48 (2015) 491–498.
[14]. X. Wang, Y. Xu, An improved index for clustering validation based on Silhouette index and Calinski–Harabasz index, IOP Conference Series: Materials Science and Engineering, 569 (2019) 052024. https://doi.org/10.1088/1757-899X/569/5/052024
[15]. M. Mughnyanti, S. Efendi, M. Zarlis, Analysis of determining centroid clustering X-means algorithm with Davies–Bouldin index evaluation, IOP Conference Series: Materials Science and Engineering, 725 (2020) 012128. https://doi.org/10.1088/1757-899X/725/1/012128
[16]. L. Hubert, P. Arabie, Comparing partitions, Journal of Classification, 2 (1985) 193–218. https://doi.org/10.1007/BF01908075
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Nhận bài
04/04/2026
Nhận bài sửa
05/11/2026
Chấp nhận đăng
11/11/2026
Xuất bản
15/11/2026
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Kiểu trích dẫn
Tran Duc, C., Le Khanh, G., & Ho Thi Lan, H. (1794675600). Unsupervised learning for structural health monitoring: A multi-station GNSS-RTK approach for operational state detection in cable-stayed bridges. Tạp Chí Khoa Học Giao Thông Vận Tải, 77(9), 1259-1274. https://doi.org/10.47869/tcsj.77.9.10
