A virtual laboratory for stability tests of rubble-mound breakwaters

被引：22

作者：

Iglesias, G. ^{[1
]}

Rabunal, J. ^{[2
]}

Losada, M. A. ^{[3
]}

Pachon, H. ^{[2
]}

Castro, A. ^{[2
]}

Carballo, R. ^{[1
]}

机构：

[1] Univ Santiago de Compostela, Dept Agroforestry Engn, Lugo 27002, Spain

[2] Univ A Coruna, CITEEC, Coruna 15192, Spain

[3] Univ Granada, CEAMA, Granada 18006, Spain

来源：

OCEAN ENGINEERING | 2008年 / 35卷 / 11-12期

关键词：

artificial intelligence; artificial neural networks; coastal engineering; coastal structures; breakwater; armor damage;

D O I：

10.1016/j.oceaneng.2008.04.014

中图分类号：

U6 [水路运输]; P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project. C 2008 Elsevier Ltd. All rights reserved.

引用

页码：1113 / 1120

页数：8

共 18 条

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