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dc.contributor.authorXu, Yuwang
dc.contributor.authorFenerci, Aksel
dc.contributor.authorØiseth, Ole Andre
dc.contributor.authorMoan, Torgeir
dc.date.accessioned2021-05-07T12:45:50Z
dc.date.available2021-05-07T12:45:50Z
dc.date.created2020-09-28T14:29:41Z
dc.date.issued2020
dc.identifier.citationOcean Engineering. 2020, 217 .en_US
dc.identifier.issn0029-8018
dc.identifier.urihttps://hdl.handle.net/11250/2754188
dc.description.abstractLong-term extreme load effects are one of the primary concerns in the design of civil and offshore structures. Such load effects can be evaluated using the accurate but computationally demanding full long-term method or the more efficient but approximate first-order and second-order reliability methods. Monte Carlo based methods enhanced with machine learning algorithms offer efficient alternatives to the traditional methods. Therefore, artificial neural networks and support vector machines are used as surrogate models for the limit state function to speed up the prediction of long-term extreme load effects. A three-span suspension bridge with two floating pylons under combined wind and wave actions is used as a case study. The cumulative density functions of the long-term extreme values corresponding to a bending moment value due to vertical deflections at the critical position of the girder are calculated. It is then shown that the artificial neural network and support vector machine-based approaches require less computational effort and yield more accurate results than the first- and second-order reliability methods.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleEfficient prediction of wind and wave induced long-term extreme load effects of floating suspension bridges using artificial neural networks and support vector machinesen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber13en_US
dc.source.volume217en_US
dc.source.journalOcean Engineeringen_US
dc.identifier.doi10.1016/j.oceaneng.2020.107888
dc.identifier.cristin1834331
dc.description.localcodePublisher embargo applies until December 1, 2022, © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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