An overview of 38 least squares–based frameworks for structural damage tomography

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Smyl, Danny
dc.contributor.author Bossuyt, Sven
dc.contributor.author Ahmad, Waqas
dc.contributor.author Vavilov, Anton
dc.contributor.author Liu, Dong
dc.date.accessioned 2019-06-03T14:16:04Z
dc.date.available 2019-06-03T14:16:04Z
dc.date.issued 2019-04-15
dc.identifier.citation Smyl , D , Bossuyt , S , Ahmad , W , Vavilov , A & Liu , D 2019 , ' An overview of 38 least squares–based frameworks for structural damage tomography ' Structural Health Monitoring . https://doi.org/10.1177/1475921719841012 en
dc.identifier.issn 1475-9217
dc.identifier.issn 1741-3168
dc.identifier.other PURE UUID: 98423b07-0c84-4f6c-b7a1-0544d0c2ebdb
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/an-overview-of-38-least-squaresbased-frameworks-for-structural-damage-tomography(98423b07-0c84-4f6c-b7a1-0544d0c2ebdb).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85064674004&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/33778734/ENG_Smyl_et_al_An_overview_of_38_Structural_Health_Monitoring.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/38325
dc.description.abstract The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher SAGE Publications Ltd
dc.relation.ispartofseries Structural Health Monitoring en
dc.rights openAccess en
dc.subject.other Biophysics en
dc.subject.other Mechanical Engineering en
dc.subject.other 212 Civil and construction engineering en
dc.title An overview of 38 least squares–based frameworks for structural damage tomography en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Mechanical Engineering
dc.contributor.department Advanced Manufacturing and Materials
dc.contributor.department University of Science and Technology of China
dc.subject.keyword Elasticity imaging
dc.subject.keyword electrical imaging
dc.subject.keyword inverse problems
dc.subject.keyword structural health monitoring
dc.subject.keyword Biophysics
dc.subject.keyword Mechanical Engineering
dc.subject.keyword 212 Civil and construction engineering
dc.identifier.urn URN:NBN:fi:aalto-201906033410
dc.identifier.doi 10.1177/1475921719841012
dc.type.version acceptedVersion


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