Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29439
DC FieldValueLanguage
dc.contributor.authorRafiq, Meena Imran-
dc.contributor.authorChryssanthopoulos, Marios K.-
dc.contributor.authorOnoufriou, Toula-
dc.date.accessioned2023-06-21T09:59:46Z-
dc.date.available2023-06-21T09:59:46Z-
dc.date.issued2005-
dc.identifier.citationSafety and Reliability of Engineering Systems and Structures: ICOSSAR, 2005, p.92en_US
dc.identifier.isbn9789059660564-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/29439-
dc.description.abstractUncertainties encompassing predictive models of concrete bridges make it difficult to decidethe timing of management activities. A methodology has been developed by the authors that use data obtained through proactive health monitoring of systems to increase the confidence in predicted performance by reduc-ing the associated uncertainties. The extent of deterioration varies considerably at different locations due tothe spatial and temporal effects of various deterioration variables involved. This limitation can be overcome by the use of sensors at various locations. Multiple sensors can also be used to increase the robustness/re-dundancy of the monitoring system at critical areas. This paper describes the development of updating proce-dures for the two mentioned scenarios, incorporating the information from multiple sensors (at different loca-tions of a system or its members) with a view of predicting the performance of the entire monitored domain with increased confidence. The advantages and limitations of the two approaches are discussed along with the scenarios where these approaches are applicable. The results for both of the cases are presented in terms of thenumber of sensors and how they influence the statistical properties of predicted performance.en_US
dc.language.isoenen_US
dc.rights© Millpressen_US
dc.subjectBayesian updatingen_US
dc.subjectStructural health monitoringen_US
dc.subjectsystems performanceen_US
dc.subjectpredictive modelsen_US
dc.titleThe role of proactive health monitoring in performance prediction: A systems approachen_US
dc.typeBook Chapteren_US
dc.collaborationUniversity of Brightonen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.urlhttps://research.brighton.ac.uk/en/publications/the-role-of-proactive-health-monitoring-in-performance-predictionen
cut.common.academicyear2004-2005en_US
dc.identifier.externalKzZ8RZgAAAAJ:J_g5lzvAfSwCen
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
item.openairetypebookPart-
item.languageiso639-1en-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-3361-1567-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Κεφάλαια βιβλίων/Book chapters
CORE Recommender
Show simple item record

Page view(s)

107
Last Week
3
Last month
11
checked on May 11, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.