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Investigación de vangarda para o desenvolvemento sostible

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Investigación de vangarda para o desenvolvemento sostible

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2015

Castro, Alberte; Pérez-Ordóñez, Juan Luis; Rabuñal-Dopico, Juan R.; Iglesias, G.

Genetic programming and floating boom performance Journal Article

In: Ocean Engineering, vol. 104, pp. 310-318, 2015, ISSN: 00298018, (cited By 15).

Abstract | Links | BibTeX | Etiquetas: artificial intelligence; artificial neural network; drainage; failure analysis; floating boom; genetic algorithm; performance assessment, Artificial intelligence; Complex networks; Genetic algorithms; Neural networks, Dimensionless variables; Drainage failure; Effective draft; Floating booms; Input and outputs; Mathematical expressions; Physical model; Waves and currents, Genetic programming

@article{Castro2015310,

title = {Genetic programming and floating boom performance},

author = {Alberte Castro and Juan Luis Pérez-Ordóñez and Juan R. Rabuñal-Dopico and G. Iglesias},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933522330&doi=10.1016%2fj.oceaneng.2015.05.023&partnerID=40&md5=d597daf8e08b7ba3bd2af1276bba03d0},

doi = {10.1016/j.oceaneng.2015.05.023},

issn = {00298018},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Ocean Engineering},

volume = {104},

pages = {310-318},

publisher = {Elsevier Ltd},

abstract = {In this paper the performance of floating booms under waves and currents is investigated by means of genetic programming (GP). This artificial intelligence (AI) technique is used to establish a mathematical expression of the significant effective draft, an essential parameter in predicting the containment capability of floating booms, and more specifically the occurrence of drainage failure. Obtained by applying GP to a comprehensive dataset of wave-current flume experiments, the expression makes the relationships among the relevant variables explicit - an advantage relative to other AI techniques such as artificial neural networks (ANN). The expression was selected as the most adequate to represent this physical problem from various expressions generated in two different stages in which dimensional and dimensionless variables were considered as input and output variables respectively. The most representative expressions obtained in both stages are presented and compared taking into account their goodness-of-fit, physical meaning, coherence and complexity. In addition, the adjustment with the experimental data obtained with these expressions is also discussed and compared with a previously developed ANN model. © 2015 Elsevier Ltd.All rights reserved.},

note = {cited By 15},

keywords = {artificial intelligence; artificial neural network; drainage; failure analysis; floating boom; genetic algorithm; performance assessment, Artificial intelligence; Complex networks; Genetic algorithms; Neural networks, Dimensionless variables; Drainage failure; Effective draft; Floating booms; Input and outputs; Mathematical expressions; Physical model; Waves and currents, Genetic programming},

pubstate = {published},

tppubtype = {article}

}