Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure

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dc.contributor.advisorNagarajaiah, Satishen_US
dc.contributor.committeeMemberPadgett, Jamieen_US
dc.contributor.committeeMemberMeade, Andrewen_US
dc.creatorYang, Yongchaoen_US
dc.date.accessioned2016-01-07T21:44:46Zen_US
dc.date.available2016-01-07T21:44:46Zen_US
dc.date.created2014-12en_US
dc.date.issued2014-08-22en_US
dc.date.submittedDecember 2014en_US
dc.date.updated2016-01-07T21:44:46Zen_US
dc.description.abstractCivil structures are subjected to ambient loads, natural hazards, and man-made extreme events, which can cause deterioration, damage, and even catastrophic failure of structures. Dense networks of sensors embedded in structures, which continuously record structural data, make possible real-time health monitoring and diagnosis of structures. Effectively and efficiently sensing and processing the massive sensor data, potentially from hundreds of channels, is required to identify (update) structural information and detect damage as early as possible to inform immediate decisionmaking. Different from traditional model-based and parametric methods that usually require intensive computation and expert attendance, this thesis explores a new data-driven methodology towards rapid, unsupervised, and automated system identification and damage detection of structures as well as data management by harnessing the data structure itself. Specifically, the sparse representation and low-rank structure inherent but implicit in the multi-channel structural response data are exploited for efficient data sensing, processing, and management in real-time health monitoring and non-destructive assessment of structures. Numerical simulations, laboratory experiments on bench-scale structures, and real-world structures examples, including seismically excited buildings and a super high-rise TV tower, are investigated.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationYang, Yongchao. "Harnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure." (2014) Diss., Rice University. <a href="https://hdl.handle.net/1911/87779">https://hdl.handle.net/1911/87779</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/87779en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectStructural health monitoringen_US
dc.subjectsystem identificationen_US
dc.subjectdamage detectionen_US
dc.subjectdata-driven methodsen_US
dc.subjectsparse representationen_US
dc.subjectblind source separationen_US
dc.titleHarnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structureen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentCivil and Environmental Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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