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Comparative analysis of Isolation Forest and One-Class SVM for outlier detection in GNSS-RTK cable-stayed bridge monitoring data
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- 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. Measurement, 46(4) (2013) 1488-1506. https://doi.org/10.1016/j.measurement.2012.12.015
- F. Moschas, S. Stiros, Dynamic deflections of a stiff footbridge using 100-Hz GNSS and accelerometer data. J. Surv. Eng., 141(4) (2015) 04015003. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000146
- J. Yu, X. Meng, X. Shao, B. Yan, L. Yang, Identification of dynamic displacements and modal frequencies of a medium-span suspension bridge using multimode GNSS processing. Eng. Struct., 81 (2014) 432-443. https://doi.org/10.1016/j.engstruct.2014.10.010
- T. Kieu, B. Yang, C.S. Jensen, Outlier detection for multidimensional time series using deep neural networks. 19th IEEE International Conference on Mobile Data Management (MDM), (2018) 125-134. https://doi.org/10.1109/MDM.2018.00028
- D. Yang, Y. Zhao, H. Ju, T.H. Yi, A. Li, Abnormal data detection for structural health monitoring: State-of-the-art review. Dev. Built Environ., 17 (2024) 100337. https://doi.org/10.1016/j.dibe.2024.100337
- Z. Wang, Y.J. Cha, Unsupervised machine and deep learning methods for structural damage detection: A comparative study. Eng. Rep., 7(1) (2022) e12551. https://doi.org/10.1002/eng2.12551
- Z. Sun, D.M. Siringoringo, S.Z. Chen, J. Lu, Cumulative displacement-based detection of damper malfunction in bridges using data-driven isolation forest algorithm. Eng. Fail. Anal., 143 (2023) 106849. https://doi.org/10.1016/j.engfailanal.2022.106849
- H.T.L. Huong, T.D. Cong, L.V. Vu, L.K. Giang, Latent pattern recognition in GNSS-based SHM using t-SNE and adaptive time-series modeling. J. Civ. Struct. Health Monit., 15 (2025) 3743–3766. https://doi.org/10.1007/s13349-025-01014-9
- H.T.L. Huong, T.D. Cong, L.V. Hien, L.K. Giang, A study on the application of unsupervised clustering algorithms in GNSS-RTK data analysis for cable-stayed bridges monitoring. Transp. Commun. Sci. J., 76(8) (2025) 1138-1150. https://doi.org/10.47869/tcsj.76.8.8
- N. Shen, B. Wang, G. Gao, L. Chen, 3-D Displacement Detection Based on Enhanced Clustering From GNSS Positioning in a Kinematic Mode for Deformation Monitoring. IEEE Trans. Instrum. Meas., 72 (2022) 1-10. https://doi.org/10.1109/TIM.2022.3223072
- J. Long, O. Buyukozturk, Automated structural damage detection using one-class machine learning. In: Dynamics of Civil Structures, Volume 4: Proceedings of the 32nd IMAC, A Conference and Exposition on Structural Dynamics, 2014, pp. 117-128. https://doi.org/10.1007/978-3-319-04546-7_14
- O. Çetindemir, E. Tepe, A.C. Zülfikar, A. Yeşilyurt, N.M. Apaydın, Long-term structural health monitoring of long-span suspension bridges and anomaly detection using statistical indicators. In: Proceedings of the 11th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-11), Turin, Italy, 2022, pp. 241-244.
- N.L. Khoa, B. Zhang, Y. Wang, F. Chen, S. Mustapha, Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit., 13(4) (2014) 406-417. https://doi.org/10.1177/1475921714532989
- I. Bayane, J. Leander, R. Karoumi, An unsupervised machine learning approach for real-time damage detection in bridges. Eng. Struct., 308(1) (2024) 117971. https://doi.org/10.1016/j.engstruct.2024.117971
- M. Mousavi, A.H. Gandomi, Unsupervised condition monitoring of structures using VMD and isolation forest. Struct. Health Monit., (2021). https://doi.org/10.1177/14759217211013532
- V.H. Le, D.H. Vo, A case study of processing abnormal GNSS monitoring data of a cable-stayed bridge in Vietnam. IOP Conference Series: Materials Science and Engineering, 1289(1) (2023) 012035. https://doi.org/10.1088/1757-899X/1289/1/012035
- T.T. Duong, N.Q. Long, B.V. Duc, Hybrid-Precision GNSS Positioning Strategies for Landslide Monitoring. Int. J. Environ. Sci., 11(13s) (2025) 11–22. https://doi.org/10.64252/pfbba722
- T.D. Tran, T.D. Dao, T.S. Vu, D.N. Luong, C.D. Vu, S.B. Bui, H.T. Ha, Outlier detection in GNSS position time series. Sci. Technol. Dev. J., 19(2) (2016) 43-50. https://doi.org/10.32508/stdj.v19i2.665
- H.D. Nguyen, T.D. Tran, Detecting outliers in GNSS position time series using machine learning techniques. J. Min. Earth Sci., 64(4) (2023) 22–30. https://doi.org/10.46326/JMES.2023.64(4).03
- F.T. Liu, K.M. Ting, Z.H. Zhou, Isolation forest. 2008 Eighth IEEE International Conference on Data Mining, (2008) 413-422. https://doi.org/10.1109/ICDM.2008.17
- Y. Regaya, F. Fadli, A. Amira, Point-Denoise: Unsupervised outlier detection for 3D point clouds enhancement. Multimed. Tools Appl., 80 (2021) 28161-28177. https://doi.org/10.1007/s11042-021-10924-x
- B. Schölkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, R.C. Williamson, Estimating the support of a high-dimensional distribution. Neural Comput., 13(7) (2001) 1443-1471. https://doi.org/10.1162/089976601750264965
- C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3) (2011) 1-27. https://doi.org/10.1145/1961189.1961199
- L.M. Manevitz, M. Yousef, One-class SVMs for document classification. J. Mach. Learn. Res., 2 (2001) 139-154.
- L.K. Giang, H.H.T. Lan, D.V. Manh, T.Q. Hoc, Applying a two-step cluster algorithm in traffic accident data analysis. Transp. Commun. Sci. J., 75(4) (2024) 1673-1687. https://doi.org/10.47869/tcsj.75.4.16
- K.R. Shahapure, C. Nicholas, Cluster quality analysis using silhouette score. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), (2020) 747-748. https://doi.org/10.1109/dsaa49011.2020.00096
- L.H. Viet, T.T. Thi, B.H. Xuan, Swarm intelligence-based technique to enhance performance of ANN in structural damage detection. Transp. Commun. Sci. 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.
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