Neural network for design and reliability analysis of rubble mound breakwaters SCIE SCOPUS

Cited 48 time in WEB OF SCIENCE Cited 52 time in Scopus
Title
Neural network for design and reliability analysis of rubble mound breakwaters
Author(s)
Kim, DH; Park, WS
KIOST Author(s)
Park, Woo Sun(박우선)
Publication Year
2005-08
Abstract
Artificial neural networks were applied to the design of rubble mound breakwater. Five neural networks with different network structures were trained with the same training data. Then they were compared with conventional empirical model and one another. It was found that the neural network technique gives more accurate results than conventional empirical model and the extent of accuracy can be affected by the structure of neural network. After that, how to integrate the trained neural network into reliability analysis technique is proposed. Since the neural network technique shows better performance than empirical model based approach in breakwater design, it is expected that the neural network integrated reliability analysis gives more improved results for probability of failure than it is done with empirical model. (c) 2005 Elsevier Ltd. All rights reserved.
ISSN
0029-8018
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/5042
DOI
10.1016/j.oceaneng.2004.11.008
Bibliographic Citation
OCEAN ENGINEERING, v.32, no.11-12, pp.1332 - 1349, 2005
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
breakwater; stability; neural network; reliability based design; Monte Carlo simulation
Type
Article
Language
English
Document Type
Article
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Related Researcher
Research Interests

Development of harbor structures,Analysis of harbor structures,Design of harbor structures,항만구조물개발,항만구조물해석,항만구조물설계

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