A brief introduction to recent developments in population-based structural health monitoring
dc.contributor.author | Worden, K | |
dc.contributor.author | Bull, LA | |
dc.contributor.author | Gardner, P | |
dc.contributor.author | Gosliga, J | |
dc.contributor.author | Rogers, TJ | |
dc.contributor.author | Cross, EJ | |
dc.contributor.author | Papatheou, E | |
dc.contributor.author | Lin, W | |
dc.contributor.author | Dervilis, N | |
dc.date.accessioned | 2020-09-09T09:19:55Z | |
dc.date.issued | 2020-09-09 | |
dc.description.abstract | One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examples | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Vol. 6, article 146 | en_GB |
dc.identifier.doi | 10.3389/fbuil.2020.00146 | |
dc.identifier.grantnumber | EP/R003645/1 | en_GB |
dc.identifier.grantnumber | EP/R004900/1 | en_GB |
dc.identifier.grantnumber | EP/S001565/1 | en_GB |
dc.identifier.grantnumber | EP/R006768/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/122794 | |
dc.language.iso | en | en_GB |
dc.publisher | Frontiers Media | en_GB |
dc.rights | Copyright © 2020 Worden, Bull, Gardner, Gosliga, Rogers, Cross, Papatheou, Lin and Dervilis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_GB |
dc.subject | machine learning | en_GB |
dc.subject | graph theory | en_GB |
dc.subject | complex networks | en_GB |
dc.subject | transfer learning | en_GB |
dc.subject | semi-supervised learning | en_GB |
dc.subject | population-based structural health monitoring (PBSHM) | en_GB |
dc.title | A brief introduction to recent developments in population-based structural health monitoring | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-09-09T09:19:55Z | |
dc.description | This is the final version. Available from the publisher via the DOI in this record. | en_GB |
dc.identifier.eissn | 2297-3362 | |
dc.identifier.journal | Frontiers in Built Environment | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-08-03 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-09-09 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-09-09T09:16:57Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2020-09-09T09:20:00Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as Copyright © 2020 Worden, Bull, Gardner, Gosliga, Rogers, Cross, Papatheou, Lin and
Dervilis. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in
other forums is permitted, provided the original author(s) and the copyright owner(s)
are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.