Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81279
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorWan, HPen_US
dc.creatorNi, YQen_US
dc.date.accessioned2019-09-20T00:54:53Z-
dc.date.available2019-09-20T00:54:53Z-
dc.identifier.issn1475-9217en_US
dc.identifier.urihttp://hdl.handle.net/10397/81279-
dc.language.isoenen_US
dc.publisherSAGE Publicationsen_US
dc.rights© The Author(s) 2018en_US
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)en_US
dc.rightsArticle reuse guidelines: sagepub.com/journals-permissionsen_US
dc.rightsThe following publication Wan, H. P., & Ni, Y. Q. (2019). Bayesian multi-task learning methodology for reconstruction of structural health monitoring data. Structural Health Monitoring, 18(4), 1282-1309 is available at https://dx.doi.org/10.1177/1475921718794953en_US
dc.subjectData reconstructionen_US
dc.subjectBayesian multi-task learningen_US
dc.subjectGaussian process prioren_US
dc.subjectStructural health monitoringen_US
dc.subjectSupertall structureen_US
dc.titleBayesian multi-task learning methodology for reconstruction of structural health monitoring dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1282en_US
dc.identifier.epage1309en_US
dc.identifier.volume18en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1177/1475921718794953en_US
dcterms.abstractReconstruction of structural health monitoring data is a challenging task, since it involves time series data forecasting especially in the case with a large block of missing data. In this study, we propose a novel methodology for structural health monitoring data recovery in the context of Bayesian multi-task learning with multi-dimensional Gaussian process prior. The proposed methodology stands to model a series of tasks simultaneously rather than modeling each task independently while explicitly encoding the correlations among tasks that can be learnt efficiently from data. The primary advantage of Bayesian multi-task learning for data reconstruction is that it makes more efficient use of the data available and gives rise to enhanced reconstruction capability by making use of the underlying task relatedness. Since the modeling performance of the Gaussian process-based Bayesian approach heavily relies on the selected covariance function, particular focus has been laid on the influences of various kinds of covariance functions including the unblended and composite (hybrid) ones on reconstruction performance. The instrumented Canton Tower of 600 m high is used as a test bed to illustrate the effectiveness of the proposed method in reconstruction of structural health monitoring data. The traditional Bayesian single-task learning approach is also implemented for comparison purpose. The reconstruction results of the structural health monitoring data show that the proposed Bayesian multi-task learning methodology affords an excellent performance, while the Bayesian single-task learning method is unreliable in certain cases; yet, the selection of covariance function has a significant impact on the reconstruction performance of the proposed methodology. The work presented in this study also gains insight into how to choose an appropriate covariance function for reconstruction of missing structural health monitoring data.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationStructural health monitoring, 1 July 2019, v. 18, no. 4, p. 1282-1309en_US
dcterms.isPartOfStructural health monitoringen_US
dcterms.issued2019-07-01-
dc.identifier.isiWOS:000473498600018-
dc.identifier.eissn1741-3168en_US
dc.description.validate201909 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0709-n29, OA_Scopus/WOSen_US
dc.identifier.SubFormID1179-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextPolyU 152767/16E||P0001753en_US
dc.description.pubStatusPublisheden_US
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