Приказ основних података о документу

dc.creatorIsailović, Dušan
dc.creatorHajdin, Rade
dc.creatorMatos, J.C.
dc.date.accessioned2019-04-19T14:30:47Z
dc.date.available2019-04-19T14:30:47Z
dc.date.issued2019
dc.identifier.urihttps://grafar.grf.bg.ac.rs/handle/123456789/992
dc.description.abstractBridge quality, as a measure of its compliance with the performance goals is tracked by the quality control plan. In other words, the quality could be understood as a comparison between performance indicators and performance goals. Reliability is widely recognized as the most important key performance indicator. This paper proposes a new approach to the mentioned comparison that provides valuable information and consequently improve decision-making regarding future course of action. A current quality control plan is facing certain limitations. Defects are being detected in the inspection and documented in the inspection report. In the most cases, however the essential information for reliability assessment i.e. the extent and location of the defects is not recorded. The proposed approach uses a Bayesian net for the prior analysis of the bridge. Afterwards, the net is updated after each inspection, allowing a posterior analysis of the bridge reliability. A damage free state of the bridge, so-called “virgin state” is adopted as a baseline, while inspectors are asked to estimate the severity of the defect and consider the vulnerability of the structural system for the locations of defects.en
dc.publisherInternational Association for Bridge and Structural Engineering (IABSE)
dc.rightsrestrictedAccess
dc.sourceIABSE Symposium, Nantes 2018: Tomorrow's Megastructures
dc.subjectBayesian networken
dc.subjectBridge inspectionen
dc.subjectKey performance indicatoren
dc.subjectPerformance goalen
dc.subjectReliabilityen
dc.subjectVulnerable zoneen
dc.titleBridge quality control using Bayesian neten
dc.typeconferenceObject
dc.rights.licenseARR
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_grafar_992
dc.identifier.scopus2-s2.0-85059401095
dc.type.versionpublishedVersion


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