{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T14:03:36Z","timestamp":1787321016615,"version":"build-2736575974"},"reference-count":193,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.<\/jats:p>","DOI":"10.3390\/s23063290","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T02:36:22Z","timestamp":1679366182000},"page":"3290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3918-2297","authenticated-orcid":false,"given":"Kareem","family":"Eltouny","sequence":"first","affiliation":[{"name":"Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5465-6631","authenticated-orcid":false,"given":"Mohamed","family":"Gomaa","sequence":"additional","affiliation":[{"name":"Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4788-8759","authenticated-orcid":false,"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Civil, Structural and Environmental Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, H.-P., and Ni, Y.-Q. (2018). Structural Health Monitoring of Large Civil Engineering Structures, John Wiley & Sons.","DOI":"10.1002\/9781119166641"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Farrar, C.R., and Worden, K. (2012). Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons.","DOI":"10.1002\/9781118443118"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1098\/rsta.2006.1928","article-title":"An introduction to structural health monitoring","volume":"365","author":"Farrar","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1098\/rsta.2000.0717","article-title":"Vibration\u2013based structural damage identification","volume":"359","author":"Farrar","year":"2001","journal-title":"Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_5","unstructured":"Rytter, A. (1993). Vibrational Based Inspection of Civil Engineering Structures. [Ph.D. Thesis, Department of Building Technology and Structural Engineering, Aalborg University]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","article-title":"Advances in computer vision-based civil infrastructure inspection and monitoring","volume":"5","author":"Spencer","year":"2019","journal-title":"Engineering"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1177\/1475921720935585","article-title":"A review of computer vision\u2013based structural health monitoring at local and global levels","volume":"20","author":"Dong","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11803-022-2074-7","article-title":"A review of the research and application of deep learning-based computer vision in structural damage detection","volume":"21","author":"Lingxin","year":"2022","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Friswell, M.I., and Mottershead, J.E. (1995). Finite Element Model Updating in Structural Dynamics, Springer.","DOI":"10.1007\/978-94-015-8508-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.jsv.2003.10.041","article-title":"Structural damage identification of the highway bridge Z24 by FE model updating","volume":"278","author":"Teughels","year":"2004","journal-title":"J. Sound Vib."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.jsv.2007.03.044","article-title":"Sensitivity-based finite element model updating using constrained optimization with a trust region algorithm","volume":"305","author":"Bakir","year":"2007","journal-title":"J. Sound Vib."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1061\/(ASCE)0733-9399(2009)135:4(243)","article-title":"Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters","volume":"135","author":"Cheung","year":"2009","journal-title":"J. Eng. Mech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1111\/mice.12358","article-title":"Full Gibbs Sampling Procedure for Bayesian System Identification Incorporating Sparse Bayesian Learning with Automatic Relevance Determination","volume":"33","author":"Huang","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/0885-9507.00210","article-title":"Significance of modeling error in structural parameter estimation","volume":"16","author":"Sanayei","year":"2001","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Marwala, T. (2010). Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics, Springer.","DOI":"10.1007\/978-1-84996-323-7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"015003","DOI":"10.1088\/0964-1726\/22\/1\/015003","article-title":"Wavelet-based AR\u2013SVM for health monitoring of smart structures","volume":"22","author":"Kim","year":"2012","journal-title":"Smart Mater. Struct."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2488","DOI":"10.1002\/stc.2488","article-title":"A data-driven framework for near real-time and robust damage diagnosis of building structures","volume":"27","author":"Sajedi","year":"2020","journal-title":"Struct. Control Health Monit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.engstruct.2015.05.003","article-title":"Structural modification assessment using supervised learning methods applied to vibration data","volume":"99","author":"Alves","year":"2015","journal-title":"Eng. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.engstruct.2008.11.010","article-title":"Prediction of seismic-induced structural damage using artificial neural networks","volume":"31","author":"Omenzetter","year":"2009","journal-title":"Eng. Struct."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.asoc.2017.05.029","article-title":"Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise buildings","volume":"58","author":"Oh","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.neucom.2017.09.069","article-title":"1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data","volume":"275","author":"Abdeljaber","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1111\/mice.12523","article-title":"Vibration-based semantic damage segmentation for large-scale structural health monitoring","volume":"35","author":"Sajedi","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1111\/mice.12642","article-title":"Dual Bayesian inference for risk-informed vibration-based damage diagnosis","volume":"36","author":"Sajedi","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"04022196","DOI":"10.1061\/(ASCE)ST.1943-541X.0003501","article-title":"Filter Banks and Hybrid Deep Learning Architectures for Performance-Based Seismic Assessments of Bridges","volume":"148","author":"Sajedi","year":"2022","journal-title":"J. Struct. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/mice.12425","article-title":"Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization","volume":"34","author":"Liang","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1111\/mice.12580","article-title":"Uncertainty-assisted deep vision structural health monitoring","volume":"36","author":"Sajedi","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_27","unstructured":"Eltouny, K., Sajedi, S., and Liang, X. (2022, January 5\u20138). High-Fidelity Visual Structural Inspections through Transformers and Learnable Resizers. Proceedings of the 8th World Conference on Structural Control and Monitoring, Orlando, FL, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1006\/jsvi.1996.0747","article-title":"Structural fault detection using a novelty measure","volume":"201","author":"Worden","year":"1997","journal-title":"J. Sound Vib."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1006\/jsvi.1999.2514","article-title":"Damage detection using outlier analysis","volume":"229","author":"Worden","year":"2000","journal-title":"J. Sound Vib."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1006\/jsvi.2002.5168","article-title":"Experimental validation of a structural health monitoring methodology: Part I. Novelty detection on a laboratory structure","volume":"259","author":"Worden","year":"2003","journal-title":"J. Sound Vib."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1016\/j.ymssp.2009.02.013","article-title":"Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications","volume":"23","author":"Gul","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1106\/104538902030904","article-title":"Statistical damage classification under changing environmental and operational conditions","volume":"13","author":"Sohn","year":"2002","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.engstruct.2004.12.006","article-title":"Damage detection in bridges using neural networks for pattern recognition of vibration signatures","volume":"27","author":"Yeung","year":"2005","journal-title":"Eng. Struct."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1002\/nme.1964","article-title":"Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings","volume":"71","author":"Jiang","year":"2007","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1111\/j.1467-8667.2008.00541.x","article-title":"Reference-free damage classification based on cluster analysis","volume":"23","author":"Sohn","year":"2008","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1016\/j.ymssp.2008.01.004","article-title":"Structural damage detection by fuzzy clustering","volume":"22","author":"Junior","year":"2008","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Doebling, S.W., Farrar, C.R., Prime, M.B., and Shevitz, D.W. (1996). Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review.","DOI":"10.2172\/249299"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., and Czarnecki, J.J. (2003). A Review of Structural Health Monitoring Literature: 1996\u20132001.","DOI":"10.1117\/12.434158"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1177\/1475921704047500","article-title":"Vibration based condition monitoring: A review","volume":"3","author":"Carden","year":"2004","journal-title":"Struct. Health Monit."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1177\/1475921710365419","article-title":"Vibration-based damage identification methods: A review and comparative study","volume":"10","author":"Fan","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/S0141-0296(96)00149-6","article-title":"Detection of structural damage through changes in frequency: A review","volume":"19","author":"Salawu","year":"1997","journal-title":"Eng. Struct."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012054","DOI":"10.1088\/1742-6596\/305\/1\/012054","article-title":"Structural damage detection using changes in natural frequencies: Theory and applications","volume":"305","author":"He","year":"2011","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.ymssp.2013.01.020","article-title":"Damage localization using transmissibility functions: A critical review","volume":"38","author":"Deraemaeker","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_44","first-page":"317954","article-title":"Application of Hilbert-Huang transform in structural health monitoring: A state-of-the-art review","volume":"2014","author":"Chen","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11831-014-9135-7","article-title":"Signal processing techniques for vibration-based health monitoring of smart structures","volume":"23","author":"Adeli","year":"2016","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"115741","DOI":"10.1016\/j.jsv.2020.115741","article-title":"Review on the new development of vibration-based damage identification for civil engineering structures: 2010\u20132019","volume":"491","author":"Hou","year":"2021","journal-title":"J. Sound Vib."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"04020073","DOI":"10.1061\/(ASCE)ST.1943-541X.0002535","article-title":"Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection","volume":"146","author":"Sun","year":"2020","journal-title":"J. Struct. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"88058","DOI":"10.1109\/ACCESS.2022.3199443","article-title":"Artificial intelligence and structural health monitoring of bridges: A review of the state-of-the-art","volume":"10","author":"Zinno","year":"2022","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1007\/s11831-021-09665-9","article-title":"A critical review on structural health monitoring: Definitions, methods, and perspectives","volume":"29","author":"Gharehbaghi","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"110939","DOI":"10.1016\/j.measurement.2022.110939","article-title":"State-of-the-art review on advancements of data mining in structural health monitoring","volume":"193","author":"Gordan","year":"2022","journal-title":"Measurement"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1177\/1475921717750047","article-title":"Structural health monitoring: Closing the gap between research and industrial deployment","volume":"17","author":"Cawley","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.engstruct.2018.05.084","article-title":"Emerging artificial intelligence methods in structural engineering","volume":"171","author":"Salehi","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Azimi, M., Eslamlou, A.D., and Pekcan, G. (2020). Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review. Sensors, 20.","DOI":"10.3390\/s20102778"},{"key":"ref_54","first-page":"567","article-title":"A review on deep learning-based structural health monitoring of civil infrastructures","volume":"24","author":"Ye","year":"2019","journal-title":"Smart Struct. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1007\/s11831-020-09471-9","article-title":"Machine learning algorithms in civil structural health monitoring: A systematic review","volume":"28","author":"Flah","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"107077","DOI":"10.1016\/j.ymssp.2020.107077","article-title":"A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications","volume":"147","author":"Avci","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111347","DOI":"10.1016\/j.engstruct.2020.111347","article-title":"A systematic review of convolutional neural network-based structural condition assessment techniques","volume":"226","author":"Sony","year":"2021","journal-title":"Eng. Struct."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1111\/mice.12845","article-title":"Large-scale structural health monitoring using composite recurrent neural networks and grid environments","volume":"38","author":"Eltouny","year":"2023","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"109713","DOI":"10.1016\/j.ymssp.2022.109713","article-title":"Structural damage assessment through a new generalized autoencoder with features in the quefrency domain","volume":"184","author":"Li","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"04022226","DOI":"10.1061\/JSENDH.STENG-11309","article-title":"Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data","volume":"149","author":"Sadeghi","year":"2023","journal-title":"J. Struct. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109910","DOI":"10.1016\/j.ymssp.2022.109910","article-title":"On the effectiveness of dimensionality reduction for unsupervised structural health monitoring anomaly detection","volume":"187","author":"Sarlo","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1177\/14759217211069842","article-title":"Non-parametric empirical machine learning for short-term and long-term structural health monitoring","volume":"21","author":"Entezami","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1177\/14759217211041684","article-title":"Deep learning enhanced principal component analysis for structural health monitoring","volume":"21","author":"Omella","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Giglioni, V., Venanzi, I., Poggioni, V., Milani, A., and Ubertini, F. (2022). Autoencoders for unsupervised real-time bridge health assessment. Comput.-Aided Civ. Infrastruct. Eng.","DOI":"10.1111\/mice.12943"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"04022006","DOI":"10.1061\/(ASCE)EM.1943-7889.0002066","article-title":"Near-real-time identification of seismic damage using unsupervised deep neural network","volume":"148","author":"Kim","year":"2022","journal-title":"J. Eng. Mech."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Luc\u00e0, F., Manzoni, S., Cerutti, F., and Cigada, A. (2022). A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model. Sensors, 22.","DOI":"10.3390\/s22218336"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"108268","DOI":"10.1016\/j.ymssp.2021.108268","article-title":"Online unsupervised detection of structural changes using train\u2013induced dynamic responses","volume":"165","author":"Meixedo","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e3042","DOI":"10.1002\/stc.3042","article-title":"A novel unsupervised real-time damage detection method for structural health monitoring using machine learning","volume":"29","author":"Shi","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1111\/mice.12812","article-title":"Toward a general unsupervised novelty detection framework in structural health monitoring","volume":"37","author":"Sepasdar","year":"2022","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1177\/10775463211006965","article-title":"Multivariate empirical mode decomposition\u2013based structural damage localization using limited sensors","volume":"28","author":"Sony","year":"2022","journal-title":"J. Vib. Control"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e3070","DOI":"10.1002\/stc.3070","article-title":"Towards probabilistic data-driven damage detection in SHM using sparse Bayesian learning scheme","volume":"29","author":"Wang","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"108009","DOI":"10.1016\/j.ymssp.2021.108009","article-title":"Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme","volume":"162","author":"Yan","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xie, X., Li, H., and Zhou, B. (2022). An unsupervised tunnel damage identification method based on convolutional variational auto-encoder and wavelet packet analysis. Sensors, 22.","DOI":"10.3390\/s22062412"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1111\/mice.12680","article-title":"Bayesian-optimized unsupervised learning approach for structural damage detection","volume":"36","author":"Eltouny","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1111\/mice.12641","article-title":"A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders","volume":"36","author":"Jiang","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1177\/1475921720988666","article-title":"Novelty detection of cable-stayed bridges based on cable force correlation exploration using spatiotemporal graph convolutional networks","volume":"20","author":"Li","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e2698","DOI":"10.1002\/stc.2698","article-title":"Probabilistic principal component analysis-based anomaly detection for structures with missing data","volume":"28","author":"Ma","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1177\/1475921720924601","article-title":"Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders","volume":"20","author":"Mao","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"108297","DOI":"10.1016\/j.measurement.2020.108297","article-title":"Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data","volume":"168","author":"Mousavi","year":"2021","journal-title":"Measurement"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"107766","DOI":"10.1016\/j.ymssp.2021.107766","article-title":"An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade","volume":"159","author":"Movsessian","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1111\/mice.12635","article-title":"Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold","volume":"36","author":"Sarmadi","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e2663","DOI":"10.1002\/stc.2663","article-title":"Ensemble learning-based structural health monitoring by Mahalanobis distance metrics","volume":"28","author":"Sarmadi","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e2714","DOI":"10.1002\/stc.2714","article-title":"Damage-sensitive feature extraction with stacked autoencoders for unsupervised damage detection","volume":"28","author":"Silva","year":"2021","journal-title":"Struct. Control Health Monit."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"124549","DOI":"10.1109\/ACCESS.2021.3100419","article-title":"Deep learning-based anomaly detection to classify inaccurate data and damaged condition of a cable-stayed bridge","volume":"9","author":"Son","year":"2021","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1177\/1475921720934051","article-title":"Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage","volume":"20","author":"Wang","year":"2021","journal-title":"Struct. Health Monit."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"125563","DOI":"10.1016\/j.conbuildmat.2021.125563","article-title":"An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization","volume":"313","author":"Yuan","year":"2021","journal-title":"Constr. Build. Mater."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"6658575","DOI":"10.1155\/2021\/6658575","article-title":"Unsupervised structural damage detection technique based on a deep convolutional autoencoder","volume":"2021","author":"Rastin","year":"2021","journal-title":"Shock Vib."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Entezami, A., Sarmadi, H., Behkamal, B., and Mariani, S. (2020). Big data analytics and structural health monitoring: A statistical pattern recognition-based approach. Sensors, 20.","DOI":"10.3390\/s20082328"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"102923","DOI":"10.1016\/j.advengsoft.2020.102923","article-title":"Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection","volume":"150","author":"Entezami","year":"2020","journal-title":"Adv. Eng. Softw."},{"key":"ref_90","first-page":"17","article-title":"An unsupervised learning approach for early damage detection by time series analysis and deep neural network to deal with output-only (big) data","volume":"2","author":"Entezami","year":"2020","journal-title":"Eng. Proc."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"107811","DOI":"10.1016\/j.measurement.2020.107811","article-title":"Structural damage identification based on unsupervised feature-extraction via Variational Auto-encoder","volume":"160","author":"Ma","year":"2020","journal-title":"Measurement"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Mousavi, A.A., Zhang, C., Masri, S.F., and Gholipour, G. (2020). Structural damage localization and quantification based on a CEEMDAN Hilbert transform neural network approach: A model steel truss bridge case study. Sensors, 20.","DOI":"10.3390\/s20051271"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1111\/mice.12528","article-title":"Deep learning for data anomaly detection and data compression of a long-span suspension bridge","volume":"35","author":"Ni","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e2535","DOI":"10.1002\/stc.2535","article-title":"Bridge condition monitoring using fixed moving principal component analysis","volume":"27","author":"Nie","year":"2020","journal-title":"Struct. Control Health Monit."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.ijfatigue.2020.105475","article-title":"Semi-automated methodology for damage assessment of a scaled wind turbine tripod using enhanced empirical mode decomposition and statistical analysis","volume":"134","author":"Soman","year":"2020","journal-title":"Int. J. Fatigue"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"106386","DOI":"10.1016\/j.ymssp.2019.106386","article-title":"Damage detection under environmental and operational effects using cointegration analysis\u2013application to experimental data from a cable-stayed bridge","volume":"135","author":"Pimentel","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Tran, T.T., and Ozer, E. (2020). Automated and model-free bridge damage indicators with simultaneous multiparameter modal anomaly detection. Sensors, 20.","DOI":"10.3390\/s20174752"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"045029","DOI":"10.1088\/1361-665X\/ab79b3","article-title":"Anomaly detection for large span bridges during operational phase using structural health monitoring data","volume":"29","author":"Xu","year":"2020","journal-title":"Smart Mater. Struct."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.jsv.2019.03.025","article-title":"Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data","volume":"453","author":"Bull","year":"2019","journal-title":"J. Sound Vib."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"109364","DOI":"10.1016\/j.engstruct.2019.109364","article-title":"Automated real-time damage detection strategy using raw dynamic measurements","volume":"196","author":"Cury","year":"2019","journal-title":"Eng. Struct."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.measurement.2018.10.095","article-title":"Structural health monitoring by a new hybrid feature extraction and dynamic time warping methods under ambient vibration and non-stationary signals","volume":"134","author":"Entezami","year":"2019","journal-title":"Measurement"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1177\/1475921718800306","article-title":"Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods","volume":"18","author":"Entezami","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.measurement.2019.02.053","article-title":"Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD","volume":"138","author":"Han","year":"2019","journal-title":"Measurement"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1111\/mice.12511","article-title":"Machine learning based novelty detection using modal analysis","volume":"34","author":"Ozdagli","year":"2019","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_105","first-page":"e2434","article-title":"Online early damage detection and localisation using multivariate data analysis: Application to a cable-stayed bridge","volume":"26","author":"Pimentel","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Anaissi, A., Makki Alamdari, M., Rakotoarivelo, T., and Khoa, N.L.D. (2018). A tensor-based structural damage identification and severity assessment. Sensors, 18.","DOI":"10.3390\/s18010111"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1177\/1475921717691260","article-title":"Unsupervised novelty detection\u2013based structural damage localization using a density peaks-based fast clustering algorithm","volume":"17","author":"Cha","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1177\/1475921717693572","article-title":"An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification","volume":"17","author":"Entezami","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.engstruct.2017.10.070","article-title":"A novel unsupervised deep learning model for global and local health condition assessment of structures","volume":"156","author":"Rafiei","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ymssp.2018.03.054","article-title":"Vibration-based damage detection for a population of nominally identical structures: Unsupervised Multiple Model (MM) statistical time series type methods","volume":"111","author":"Sakellariou","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1177\/1077546316674544","article-title":"Damage detection using transmissibility compressed by principal component analysis enhanced with distance measure","volume":"24","author":"Zhou","year":"2018","journal-title":"J. Vib. Control"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.ymssp.2016.10.033","article-title":"A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge","volume":"87","author":"Alamdari","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1016\/j.proeng.2017.09.280","article-title":"Statistical methods for damage detection applied to civil structures","volume":"199","author":"Gres","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"e1998","DOI":"10.1002\/stc.1998","article-title":"Damage detection under varying temperature using artificial neural networks","volume":"24","author":"Gu","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ymssp.2016.12.002","article-title":"Automated structural health monitoring based on adaptive kernel spectral clustering","volume":"90","author":"Langone","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s13349-017-0252-5","article-title":"Structural health monitoring of bridges: A model-free ANN-based approach to damage detection","volume":"7","author":"Neves","year":"2017","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"e1886","DOI":"10.1002\/stc.1886","article-title":"Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges","volume":"24","author":"Santos","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"04016124","DOI":"10.1061\/(ASCE)BE.1943-5592.0001003","article-title":"In-service condition assessment of a long-span suspension bridge using temperature-induced strain data","volume":"22","author":"Xia","year":"2017","journal-title":"J. Bridge Eng."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1177\/1475921716680849","article-title":"Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure","volume":"16","author":"Zhou","year":"2017","journal-title":"Struct. Health Monit."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"065034","DOI":"10.1088\/0964-1726\/24\/6\/065034","article-title":"Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures","volume":"24","author":"Adeli","year":"2015","journal-title":"Smart Mater. Struct."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"04015043","DOI":"10.1061\/(ASCE)CF.1943-5509.0000801","article-title":"Self-organizing maps for structural damage detection: A novel unsupervised vibration-based algorithm","volume":"30","author":"Avci","year":"2016","journal-title":"J. Perform. Constr. Facil."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s13349-016-0160-0","article-title":"A clustering approach for structural health monitoring on bridges","volume":"6","author":"Diez","year":"2016","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1002\/stc.1774","article-title":"Damage detection with small data set using energy-based nonlinear features","volume":"23","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1002\/stc.1825","article-title":"On-line unsupervised detection of early damage","volume":"23","author":"Santos","year":"2016","journal-title":"Struct. Control Health Monit."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.engappai.2016.03.002","article-title":"A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges","volume":"52","author":"Silva","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1177\/1045389X14566520","article-title":"Structural damage detection using principal component analysis and damage indices","volume":"27","author":"Tibaduiza","year":"2016","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2015.07.021","article-title":"Structural damage localization by outlier analysis of signal-processed mode shapes\u2013Analytical and experimental validation","volume":"68","author":"Ulriksen","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1002\/stc.1741","article-title":"Novelty detection for SHM using raw acceleration measurements","volume":"22","author":"Alves","year":"2015","journal-title":"Struct. Control Health Monit."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jsv.2015.02.039","article-title":"On robust regression analysis as a means of exploring environmental and operational conditions for SHM data","volume":"347","author":"Dervilis","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1080\/15732479.2014.949277","article-title":"Structural damage detection and localisation using multivariate regression models and two-sample control statistics","volume":"11","author":"Shahidi","year":"2015","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ymssp.2013.10.023","article-title":"Subspace-based damage detection under changes in the ambient excitation statistics","volume":"45","author":"Mevel","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engstruct.2014.08.042","article-title":"A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variability","volume":"80","author":"Figueiredo","year":"2014","journal-title":"Eng. Struct."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1111\/mice.12059","article-title":"Localized structural damage detection: A change point analysis","volume":"29","author":"Nigro","year":"2014","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s13349-013-0038-3","article-title":"Linear approaches to modeling nonlinearities in long-term monitoring of bridges","volume":"3","author":"Figueiredo","year":"2013","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/stc.466","article-title":"Damage detection in an experimental bridge model using Hilbert\u2013Huang transform of transient vibrations","volume":"20","author":"Kunwar","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1061\/(ASCE)CP.1943-5487.0000289","article-title":"Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures","volume":"27","author":"Laory","year":"2013","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1002\/stc.476","article-title":"Bayesian methodology for diagnosis uncertainty quantification and health monitoring","volume":"20","author":"Sankararaman","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1260\/1369-4332.16.1.207","article-title":"Structural damage detection in a truss bridge model using fuzzy clustering and measured FRF data reduced by principal component projection","volume":"16","author":"Yu","year":"2013","journal-title":"Adv. Struct. Eng."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1002\/stc.462","article-title":"A wavelet-based damage diagnosis algorithm using principal component analysis","volume":"19","author":"Kesavan","year":"2012","journal-title":"Struct. Control Health Monit."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1155\/2012\/804590","article-title":"Empirical mode decomposition of the acceleration response of a prismatic beam subject to a moving load to identify multiple damage locations","volume":"19","author":"Meredith","year":"2012","journal-title":"Shock Vib."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1006\/mssp.2002.1548","article-title":"Description of Z24 Benchmark","volume":"17","author":"Maeck","year":"2003","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_142","unstructured":"Kr\u00e4mer, C., De Smet, C.A.M., and De Roeck, G. (1999, January 8\u201311). Z24 bridge damage detection tests. Proceedings of the 17th International Modal Analysis Conference (IMAC XVII), Kissimmee, FL, USA."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ymssp.2012.08.026","article-title":"Long-term monitoring and data analysis of the Tamar Bridge","volume":"35","author":"Cross","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.engstruct.2014.03.010","article-title":"Structural health monitoring with statistical methods during progressive damage test of S101 Bridge","volume":"69","author":"Hille","year":"2014","journal-title":"Eng. Struct."},{"key":"ref_145","unstructured":"VCE (2009). Progressive Damage Test S101 Flyover Reibersdorf (Draft), VIenna Consulting Engineers. Tech. Report 08\/2308."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_147","unstructured":"Avci, O. (2023, January 22). Qatar University Grandstand Simulator (QUGS). Available online: http:\/\/onur-avci.com\/benchmark\/."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Figueiredo, E., Park, G., Figueiras, J., Farrar, C., and Worden, K. (2009). Structural Health Monitoring Algorithm Comparisons Using Standard Data Sets.","DOI":"10.2172\/961604"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1061\/(ASCE)0733-9399(2004)130:1(3)","article-title":"Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data","volume":"130","author":"Johnson","year":"2004","journal-title":"J. Eng. Mech."},{"key":"ref_150","unstructured":"Dyke, S., Bernal, J., Beck, C., and Ventura, C. (2001, January 12\u201314). An experimental benchmark problem in structural health monitoring. Proceedings of the Third International Workshop on Structural Health Monitoring, Stanford, CA, USA."},{"key":"ref_151","unstructured":"Dyke, S.J., Bernal, D., Beck, J., and Ventura, C. (2003, January 16\u201318). Experimental phase II of the structural health monitoring benchmark problem. Proceedings of the 16th ASCE Engineering Mechanics Conference, Seattle, WA, USA."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"106341","DOI":"10.1016\/j.ymssp.2019.106341","article-title":"Towards robust statistical damage localization via model-based sensitivity clustering","volume":"134","author":"Allahdadian","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/stc.1559","article-title":"SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge","volume":"21","author":"Li","year":"2014","journal-title":"Struct. Control Health Monit."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"6650393","DOI":"10.1155\/2020\/6650393","article-title":"Replacement of Cable Stays in Yonghe Bridge in Tianjin, China","volume":"2020","author":"Li","year":"2020","journal-title":"Adv. Civ. Eng."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.engstruct.2018.09.029","article-title":"Structural response of a concrete cable-stayed bridge under thermal loads","volume":"176","author":"Pimentel","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1115\/1.2718241","article-title":"Time series based structural damage detection algorithm using Gaussian mixtures modeling","volume":"129","author":"Nair","year":"2006","journal-title":"J. Dyn. Syst. Meas. Control."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.aei.2007.02.002","article-title":"Model-free data interpretation for continuous monitoring of complex structures","volume":"22","author":"Posenato","year":"2008","journal-title":"Adv. Eng. Inform."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/T-C.1975.224208","article-title":"An optimal set of discriminant vectors","volume":"100","author":"Foley","year":"1975","journal-title":"IEEE Trans. Comput."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.jsv.2004.06.030","article-title":"A novel time-domain auto-regressive model for structural damage diagnosis","volume":"283","author":"Lu","year":"2005","journal-title":"J. Sound Vib."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1088\/0964-1726\/15\/1\/041","article-title":"Application of time series analysis for bridge monitoring","volume":"15","author":"Omenzetter","year":"2006","journal-title":"Smart Mater. Struct."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.jsv.2005.06.016","article-title":"Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure","volume":"291","author":"Nair","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1177\/1475921708102145","article-title":"Localized Damage Detection of Structures Subject to Multiple Ambient Excitations Using Two Distance Measures for Autoregressive Models","volume":"8","author":"Zheng","year":"2009","journal-title":"Struct. Health Monit."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1098\/rsta.2006.1929","article-title":"Time-series methods for fault detection and identification in vibrating structures","volume":"365","author":"Fassois","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1098\/rsta.2006.1938","article-title":"The application of machine learning to structural health monitoring","volume":"365","author":"Worden","year":"2007","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.jsv.2005.03.016","article-title":"Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks","volume":"290","author":"Ni","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ymssp.2012.09.015","article-title":"A review on empirical mode decomposition in fault diagnosis of rotating machinery","volume":"35","author":"Lei","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Civera, M., and Surace, C. (2021). A comparative analysis of signal decomposition techniques for structural health monitoring on an experimental benchmark. Sensors, 21.","DOI":"10.3390\/s21051825"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"093001","DOI":"10.1088\/1361-665X\/aba539","article-title":"Empirical mode decomposition and its variants: A review with applications in structural health monitoring","volume":"29","author":"Barbosh","year":"2020","journal-title":"Smart Mater. Struct."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational Mode Decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to forget: Continual prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_176","unstructured":"Sch\u00f6lkopf, B., Williamson, R.C., Smola, A., Shawe-Taylor, J., and Platt, J. (December, January 30). Support vector method for novelty detection. Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS\u201999), Cambridge, MA, USA."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support vector data description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","article-title":"A comprehensive survey of clustering algorithms","volume":"2","author":"Xu","year":"2015","journal-title":"Ann. Data Sci."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Chawla, S., and Gionis, A. (2013, January 2\u20134). k-means\u2013: A unified approach to clustering and outlier detection. Proceedings of the 2013 SIAM International Conference on Data Mining, Austin, TX, USA.","DOI":"10.1137\/1.9781611972832.21"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","article-title":"A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters","volume":"3","author":"Dunn","year":"1973","journal-title":"J. Cybern."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Jeffreys, H. (1998). The Theory of Probability, OUP Oxford.","DOI":"10.1093\/oso\/9780198503682.001.0001"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Angelov, P.P., and Gu, X. (2019). Empirical Approach to Machine Learning, Springer.","DOI":"10.1007\/978-3-030-02384-3"},{"key":"ref_184","unstructured":"Farrar, C.R., and Doebling, S.W. (July, January 30). An Overview of Modal-Based Damage Identification Methods. Proceedings of the DAMAS \u201897, Sheffield, UK."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1002\/(SICI)1096-9845(199908)28:8<879::AID-EQE845>3.0.CO;2-V","article-title":"An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge","volume":"28","author":"Sohn","year":"1999","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_186","first-page":"20160736","article-title":"Efficient generation of receiver operating characteristics for the evaluation of damage detection in practical structural health monitoring applications","volume":"473","author":"Liu","year":"2017","journal-title":"Proc. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TPAMI.2013.248","article-title":"Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments","volume":"36","author":"Ionescu","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1177\/14759217221094500","article-title":"Damage quantification using transfer component analysis combined with Gaussian process regression","volume":"22","author":"Yano","year":"2023","journal-title":"Struct. Health Monit."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"116072","DOI":"10.1016\/j.jsv.2021.116072","article-title":"On the transfer of damage detectors between structures: An experimental case study","volume":"501","author":"Bull","year":"2021","journal-title":"J. Sound Vib."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"108519","DOI":"10.1016\/j.ymssp.2021.108519","article-title":"On the application of kernelised Bayesian transfer learning to population-based structural health monitoring","volume":"167","author":"Gardner","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1177\/14759217211010709","article-title":"Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves","volume":"21","author":"Zhang","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"118882","DOI":"10.1016\/j.apenergy.2022.118882","article-title":"Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy","volume":"313","author":"Sun","year":"2022","journal-title":"Appl. Energy"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:59:40Z","timestamp":1760122780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,20]]},"references-count":193,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063290"],"URL":"https:\/\/doi.org\/10.3390\/s23063290","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,20]]}}}