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dc.contributor.authorGarcia-Sanchez, D.
dc.contributor.authorFernandez-Navamuel, A. 
dc.contributor.authorZamora, D.
dc.contributor.authorAlvear, D.
dc.contributor.authorPardo, D. 
dc.date.accessioned2020-10-16T13:37:32Z
dc.date.available2020-10-16T13:37:32Z
dc.date.issued2020-09
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1167
dc.description.abstractThis work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of Principal Component Analysis. We validate the method and show its efficiency against false positives and negatives by using several metrics: accuracy, precision, recall and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectStructural Health Monitoring (SHM)en_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectDamage Detection.en_US
dc.titleBearing assessment tool for longitudinal bridge performanceen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.dois13349-020-00432-1
dc.relation.publisherversionhttps://link.springer.com/epdf/10.1007/s13349-020-00432-1?sharing_token=Wuwz5aMNMdBykWo67SYoDPe4RwlQNchNByi7wbcMAY43DUyX9AwkyOcesp2EYTY_RMBtHrVeUexOlE-8_Ty6h5Wv6_SxlPG8rpx3Ljqbd3kykzY0m-4zj_2rIsXnr5h8y14Wd4rcidWmz0J-5AHKh4RY30JuhH18K9bb8ryX7jI%3Den_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/777778en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/2PE/PID2019-108111RB-I00en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionen_US
dc.journal.titleJournal of Civil Structural Health Monitoringen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España