Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-11661
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dc.contributor.authorSakiyama, Felipe Isamu H.-
dc.contributor.authorLehmann, Frank-
dc.contributor.authorGarrecht, Harald-
dc.date.accessioned2021-08-31T09:17:04Z-
dc.date.available2021-08-31T09:17:04Z-
dc.date.issued2021de
dc.identifier.issn1424-8220-
dc.identifier.other176958997X-
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/11678-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-116781de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-11661-
dc.description.abstractThe ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.en
dc.language.isoende
dc.relation.uridoi:10.3390/s21082871de
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc620de
dc.titleA novel runtime algorithm for the real-time analysis and detection of unexpected changes in a real-size SHM network with quasi-distributed FBG sensorsen
dc.typearticlede
ubs.bemerkung.externThis work was collectively funded by the research agency CAPES - Brazilian Federal Agency for Support and Evaluation of Graduate Education - (grant no. 001), the Federal University of the Jequitinhonha and Mucuri Valleys (UFVJM, Brazil), the Materials Testing Institute University of Stuttgart (MPA, Germany), and the Regierungspräsidium Stuttgart (RPS, Germany). The APC was funded by the MPA University of Stuttgart.de
ubs.fakultaetZentrale Einrichtungende
ubs.fakultaetFakultätsübergreifend / Sonstige Einrichtungde
ubs.institutMaterialprüfungsanstalt Universität Stuttgart (MPA Stuttgart, Otto-Graf-Institut (FMPA))de
ubs.institutFakultätsübergreifend / Sonstige Einrichtungde
ubs.publikation.seiten32de
ubs.publikation.sourceSensors 21 (2021), No. 2871de
ubs.publikation.typZeitschriftenartikelde
Appears in Collections:13 Zentrale Universitätseinrichtungen

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