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Comparative analysis of Isolation Forest and One-Class SVM for outlier detection in GNSS-RTK cable-stayed bridge monitoring data | LE | Journal of Materials and Engineering Structures « JMES »

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Accurate GNSS-RTK displacement data are essential for reliable structural health monitoring (SHM) of long-span cable-stayed bridges; however,...

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In: Proceedings of 2nd International Conference on Engineering Surveying - INGEO, Bratislava, Slovakia, 2002, pp.
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Comparative analysis of Isolation Forest and One-Class SVM for outlier detection in GNSS-RTK cable-stayed bridge monitoring data Abstract Keywords Full Text: PDFReferences - X.W. Ye, Y.H. Su, J.P. Han, Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review. Sci. World J., 1 (2014) 652329. https://doi.org/10.1155/2014/652329 - A. Wieser, F.K. Brunner, Analysis of bridge deformations using continuous GPS measurements. In: Proceedings of 2nd International Conference on Engineering Surveying - INGEO, Bratislava, Slovakia, 2002, pp. 45-52. - 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. Transp. Commun. Sci. J., 77(1) (2026) 127-141. https://doi.org/10.47869/tcsj.77.1.10 - F. Moschas, S. Stiros, Noise characteristics of high-frequency, short-duration GPS records from analysis of identical, collocated instruments. 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J., 73(1) (2022) 1-15. https://doi.org/10.47869/tcsj.73.1.1 - AASHTO, AASHTO LRFD Bridge Design Specifications. 9th Edition, American Association of State Highway and Transportation Officials (AASHTO), 2020. Refbacks - There are currently no refbacks. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. eISSN 2170-127X This work is made available under the terms of the Creative Commons Attribution-ShareAlike 4.0 International Licence . Based on a work available at http://revue.ummto.dz.