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dc.contributor.authorJorge Molines
dc.contributor.authorJosep R. Medina
dc.date.accessioned2017-12-30T13:02:35Z
dc.date.available2017-12-30T13:02:35Z
dc.date.issued2016
dc.identifier.other%28ASCE%29WW.1943-5460.0000322.pdf
dc.identifier.urihttp://138.201.223.254:8080/yetl1/handle/yetl/4244926
dc.description.abstractBased on the Crest Level Assessment of Coastal Structures (CLASH) Neural Network Overtopping prediction method, a new 16-parameter overtopping estimator (Q6) was developed for conventional mound breakwaters with crown walls, both with and without toe berms. Q6 was built up using the overtopping estimations given by the CLASH Neural Network and checked using the CLASH database. Q6 was compared to other conventional overtopping formulas, and the Q6 obtained the lowest prediction errors. Q6 provides overtopping predictions similar to the CLASH Neural Network for conventional mound breakwaters but using only six explanatory dimensionless variables (Rc/Hm0,Ir,Rc/h,Gc/Hm0,Ac/Rc, and a toe berm variable based on Rc/h) and two reduction factors (γf and γβ). Q6 describes explicit relationships between input variables and overtopping discharge, and hence it facilitates use in engineering design to identify cost-effective solutions and to quantify the influence of variations in wave and structural parameters.
dc.publisherAmerican Society of Civil Engineers
dc.titleExplicit Wave-Overtopping Formula for Mound Breakwaters with Crown Walls Using CLASH Neural Network–Derived Data
dc.typeJournal Paper
dc.journal.volume142
dc.journal.issue3
dc.journal.titleJournal of Waterway, Port, Coastal, and Ocean Engineering
dc.identifier.doi10.1061/(ASCE)WW.1943-5460.0000322
dc.page04015024
dc.treeJournal of Waterway, Port, Coastal, and Ocean Engineering:;2016:;Volume ( 142 ):;issue: 003
dc.contenttypeFulltext


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