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dc.contributor.authorPunurai, Wonsiri
dc.contributor.authorAzad, Md Samdani
dc.contributor.authorPholdee, Nantiwat
dc.contributor.authorBureerat, Sujin
dc.contributor.authorSinsabvarodom, Chana
dc.date.accessioned2020-04-01T09:43:15Z
dc.date.available2020-04-01T09:43:15Z
dc.date.created2019-12-10T12:20:03Z
dc.date.issued2019
dc.identifier.citationComputational intelligence. 2020, 36 (1), 1-19.en_US
dc.identifier.issn0824-7935
dc.identifier.urihttps://hdl.handle.net/11250/2649829
dc.description.abstractOffshore jacket platforms are widely used for oil and gas extraction as well as transportation in shallow to moderate water depth. Tubular cross‐sectional elements are used to construct offshore platforms. Tubular cross sections impart higher resistance against hydrodynamic forces and have high torsional rigidity. During operation, the members can be partially or fully damaged due to lateral impacts. The lateral impacts can be due to ship collisions or through the impact of falling objects. The impact forces can weaken some members that influence the overall performance of the platform. This demonstrates an urgent need to develop a framework that can accurately forecast dent depth as well as dent angle of the affected members. This study investigates the use of an adaptive metaheuristics algorithm to provide automatic detection of denting damage in an offshore structure. The damage information includes dent depth and the dent angle. A model is developed in combination with the percentage of the dent depth of the damaged member and is used to assess the performance of the method. It demonstrates that small changes in stiffness of individual damaged bracing members are detectable from measurements of global structural motion.en_US
dc.language.isoengen_US
dc.publisherWiley Periodicals Inc.en_US
dc.titleA novel hybridized metaheuristic technique in enhancing the diagnosis of cross-sectional dent damaged offshore platform membersen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber1-19en_US
dc.source.volume36en_US
dc.source.journalComputational intelligenceen_US
dc.source.issue1en_US
dc.identifier.doi10.1111/coin.12247
dc.identifier.cristin1758798
dc.description.localcodeLocked until 11 February 2021 due to copyright restrictions. This is the peer reviewed version of an article, which has been published in final form at https://doi.org/10.1111/coin.12247. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en_US
cristin.unitcode194,64,20,0
cristin.unitnameInstitutt for marin teknikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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