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

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dc.contributor.advisorNagarajaiah, Satish
dc.contributor.committeeMemberPadgett, Jamie
dc.contributor.committeeMemberMeade, Andrew
dc.creatorYang, Yongchao
dc.date.accessioned2016-01-07T21:44:46Z
dc.date.available2016-01-07T21:44:46Z
dc.date.created2014-12
dc.date.issued2014-08-22
dc.date.submittedDecember 2014
dc.date.updated2016-01-07T21:44:46Z
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.
dc.format.mimetypeapplication/pdf
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>.
dc.identifier.urihttps://hdl.handle.net/1911/87779
dc.language.isoeng
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.
dc.subjectStructural health monitoring
dc.subjectsystem identification
dc.subjectdamage detection
dc.subjectdata-driven methods
dc.subjectsparse representation
dc.subjectblind source separation
dc.titleHarnessing data structure for health monitoring and assessment of civil structures: sparse representation and low-rank structure
dc.typeThesis
dc.type.materialText
thesis.degree.departmentCivil and Environmental Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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