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Pioneering research for sustainable development

CITEEC carries out pioneering research in civil engineering, materials, sustainability and infrastructure. Thanks to multidisciplinary projects and international collaborations, scientific papers are being published that address key challenges facing the sector. This section presents the main papers and results produced by our research groups.

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Pioneering research for sustainable development

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2008

Iglesias, Gregorio; Rabuñal-Dopico, Juan R.; Losada, Miguel A.; Pachón, H.; Castro, Alberte; Carballo, Rodrigo

A virtual laboratory for stability tests of rubble-mound breakwaters Journal Article

In: Ocean Engineering, vol. 35, no. 11-12, pp. 1113-1120, 2008, ISSN: 00298018, (cited By 22).

Abstract | Links | BibTeX | Tags: Activation function (AF); Applied (CO); Artificial neural network (ANNs); Elsevier (CO); Experimental campaign; In order; Physical model (PM); Physical model testing; Rubble mound breakwaters; Stability testing; Virtual laboratory (VL); wave actions, artificial intelligence; artificial neural network; breakwater; coastal engineering; model test; wave action, Artificial intelligence; Backpropagation; Breakwaters; Computer architecture; Computer networks; Electric fault location; Hydraulic structures; Lightning; Mathematical models; Metropolitan area networks; Network architecture; Network protocols; System stability; Testing; Vegetation, Neural networks

@article{Iglesias20081113,

title = {A virtual laboratory for stability tests of rubble-mound breakwaters},

author = {Gregorio Iglesias and Juan R. Rabuñal-Dopico and Miguel A. Losada and H. Pachón and Alberte Castro and Rodrigo Carballo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-46749118744&doi=10.1016%2fj.oceaneng.2008.04.014&partnerID=40&md5=a0210a41b2a52f5f2bf3222449993c6d},

doi = {10.1016/j.oceaneng.2008.04.014},

issn = {00298018},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {Ocean Engineering},

volume = {35},

number = {11-12},

pages = {1113-1120},

abstract = {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. © 2008 Elsevier Ltd. All rights reserved.},

note = {cited By 22},

keywords = {Activation function (AF); Applied (CO); Artificial neural network (ANNs); Elsevier (CO); Experimental campaign; In order; Physical model (PM); Physical model testing; Rubble mound breakwaters; Stability testing; Virtual laboratory (VL); wave actions, artificial intelligence; artificial neural network; breakwater; coastal engineering; model test; wave action, Artificial intelligence; Backpropagation; Breakwaters; Computer architecture; Computer networks; Electric fault location; Hydraulic structures; Lightning; Mathematical models; Metropolitan area networks; Network architecture; Network protocols; System stability; Testing; Vegetation, Neural networks},

pubstate = {published},

tppubtype = {article}

}