Wave Reflection at Submerged Breakwaters

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Author(s): Alberte Castro Ponte (University of Santiago de Compostela, Spain), Gregorio Iglesias (University of Santiago de Compostela, Spain), Francisco Taveira Pinto (University of Porto, Portugal)and Rodrigo Carballo (University of Santiago de Compostela, Spain)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch235

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Several types of structures are used in Coastal Engineering with the aim of preventing shoreline erosion, such as groynes, detached breakwaters, submerged breakwaters, etc. Submerged breakwaters have the advantage of their minimal visual impact, which has made them ever more popular (Chang & Liou, 2007). When the incoming waves impinge on a submerged breakwater, a process of energy transformation occurs. Many laboratory and numerical studies have been carried out in order to investigate this process (Kobayashi & Wurjanto, 1989) (Losada, Losada & Martin, 1995) (Losada, Silva & Losada, 1996) (Liu, Lin, Hsu, Chang, Losada, Vidal & Sakakiyama, 2000). The energy of the incident wave is transformed as follows: (i) one part of this energy is transmitted above the crest of the structure and ? in the case of permeable submerged breakwaters ? through its interior; (ii) another part is dissipated by wave breaking and by friction with the structure during the transmission process and finally, (iii) the remaining energy is reflected seaward. The reflection level is related with the scour in front of the structure. Therefore, a good knowledge about the reflection process may be helpful in order to avoid or at least mitigate the possible problems in the structure foundations. However, due to the complexity of the problem, the influence of all the relevant parameters (the structure slope and submergence, the water depth, the wave period and height, etc.) is not entirely understood yet and new approaches are needed. In this work, an Artificial Neural Network (ANN) has been applied to a series of results obtained from a previous study of Taveira-Pinto (2001), in which several physical models were tested. Once trained and validated, the ANN has been used to estimate the wave reflection coefficient.

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