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dc.contributor.authorTu, Ying
dc.contributor.authorCheng, Zhengshun
dc.contributor.authorMuskulus, Michael
dc.date.accessioned2019-01-16T06:26:46Z
dc.date.available2019-01-16T06:26:46Z
dc.date.created2018-12-26T13:39:52Z
dc.date.issued2018
dc.identifier.isbn978-0-7918-5126-5
dc.identifier.urihttp://hdl.handle.net/11250/2580764
dc.description.abstractPlunging breaking waves that occur in the vicinity of offshore structures can lead to high impulsive slamming loads, which are significant for the structural loading. The occurrence of plunging breaking waves is usually identified based on criteria that are derived from theoretical analyses and experimental studies. Given a large amount of data, detecting plunging breaking waves can be treated as a typical classification problem, which can be solved by a machine learning approach. In this study, logistic regression algorithm is used together with the experimental data from the WaveSlam project to train a classifier for the detection. Three normalized dimensionless features are introduced based on the measured data for the training. A classifier with respect to four wave parameters (i.e. water depth, wave height, crest height and wave period) is then explicitly developed for detecting plunging breaking waves. It is found that the trained classifier has an accuracy of 98.7% and F1 score of 99.2% for the tested data. Among the three dimensionless parameters, the ratio of wave height to water depth, H/d, is the most decisive factor for the detection of plunging breaking waves.nb_NO
dc.language.isoengnb_NO
dc.publisherASMEnb_NO
dc.relation.ispartofASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering - Volume 7A: Ocean Engineering
dc.titleDetection of plunging breaking waves based on machine learningnb_NO
dc.typeChapternb_NO
dc.description.versionpublishedVersionnb_NO
dc.identifier.doi10.1115/OMAE2018-77671
dc.identifier.cristin1647179
dc.description.localcodeCopyright © 2018 by ASMEnb_NO
cristin.unitcode194,64,91,0
cristin.unitcode194,64,20,0
cristin.unitnameInstitutt for bygg- og miljøteknikk
cristin.unitnameInstitutt for marin teknikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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