[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127993-en":3,"doc-seo-127993-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127993,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)","Accurate, comprehensive information on bridge health is essential to guide maintenance, repair, modernization, and reinforcement, especially for long-span structures where poor assessment can trigger unnecessary or misdirected interventions. Structural Health Monitoring (SHM) relies on time series from periodic measurements to reveal abnormal behavior, anomalies, and deterioration. This work develops an SHM approach for the Valgadena viaduct using low-cost dual-frequency GNSS, combining time-series analysis, random decrements, subspace identification, and clustering (DBSCAN, GMM) for vibration mode estimation under operational conditions.","remote sensing  \nArticle  \nMachine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)  \nAndrea Masiero 1, *, Alberto Guarnieri 1, Valerio Baiocchi 2, Domenico Visintini 3 and Francesco Pirotti 1  \nCitation: Masiero, A.; Guarnieri, A.; Baiocchi, V.; Visintini, D.; Pirotti, F. Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy) . Remote Sens. 2024, 16, 3971. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)rs16213971  \nAcademic Editors: Antonio Miguel Ruiz Armenteros and Wen Liu  \nReceived: 2 August 2024  \nRevised: 16 October 2024  \nAccepted: 21 October 2024  \nPublished: 25 October 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 CIRGEO–Interdepartmental Research Center of Geomatics, Padua University, 35020 Legnaro, Italy; [alberto.guarnieri@unipd.it](alberto.guarnieri@unipd.it) (A.G.); [francesco.pirotti@unipd.it](francesco.pirotti@unipd.it) (F.P.)  \n2 DICEA–Dipartimento di Ingegneria Civile Edile e Ambientale, Sapienza University of Rome,  \n00184 Rome, Italy; [valerio.baiocchi@uniroma1.it](valerio.baiocchi@uniroma1.it)  \n3 DPIA–Dipartimento Politecnico di Ingegneria e Architettura, Udine University, 33100 Udine, Italy; [domenico.visintini@uniud.it](domenico.visintini@uniud.it)  \n* Correspondence: [andrea.masiero@unipd.it](andrea.masiero@unipd.it)  \nAbstract: The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applica","cbCaihOIVORb6keX","https://ap.wps.com/l/cbCaihOIVORb6keX","pdf",2772188,3,1,26,"English","en",105,"# Introduction\n## Structural Health Monitoring and Bridge Deformation Assessment\n# Proposed Methodology\n## Low-Cost Dual-Frequency GNSS Data Collection\n## Time-Series Analysis and Random Decrements\n## Time-Domain Subspace Identification\n## Clustering for System Pole Estimation","[{\"question\":\"What is the main goal of the proposed structural health monitoring method?\",\"answer\":\"To determine the viaduct’s vibration characteristics by extracting static and dynamic bridge movement components from GNSS-derived time series and supporting an effective automatic operational modal analysis procedure.\"},{\"question\":\"How is data collected and transformed for modal identification?\",\"answer\":\"Affordable dual-frequency GNSS receivers provide measurement time series, then random decrements is used to make vibration characteristics more apparent before applying time-domain-based subspace identification.\"},{\"question\":\"Why are DBSCAN and GMM used, and how do they compare?\",\"answer\":\"They are used to estimate the system poles from vibration signals. The results on the Valgadena bridge show that GMM clustering reduces sensitivity to parameter-value choices compared with DBSCAN in the considered case.\"}]","Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy) | PDF",1785943710,66,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-clustering-techniques-to-support-structural-monitoring-of-the-valgadena-bridge-viaduct-italy","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-clustering-techniques-to-support-structural-monitoring-of-the-valgadena-bridge-viaduct-italy/127993/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-29","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the proposed structural health monitoring method?","Question",{"text":76,"@type":77},"To determine the viaduct’s vibration characteristics by extracting static and dynamic bridge movement components from GNSS-derived time series and supporting an effective automatic operational modal analysis procedure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is data collected and transformed for modal identification?",{"text":81,"@type":77},"Affordable dual-frequency GNSS receivers provide measurement time series, then random decrements is used to make vibration characteristics more apparent before applying time-domain-based subspace identification.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are DBSCAN and GMM used, and how do they compare?",{"text":85,"@type":77},"They are used to estimate the system poles from vibration signals. 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