In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respect...

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Autores principales:	Jae-bin Lee, Gökhan Tansel Tayyar, Joonmo Choung
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Tension angle Interlink angle OPB/IPB moment Multi-link analysis Three-link analysis Ocean engineering TC1501-1800 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/027c8c87bf864b4598690f402153c018
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id	oai:doaj.org-article:027c8c87bf864b4598690f402153c018
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spelling	oai:doaj.org-article:027c8c87bf864b4598690f402153c0182021-11-26T04:27:30ZIn-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique2092-678210.1016/j.ijnaoe.2021.11.003https://doaj.org/article/027c8c87bf864b4598690f402153c0182021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2092678221000625https://doaj.org/toc/2092-6782This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.Jae-bin LeeGökhan Tansel TayyarJoonmo ChoungElsevierarticleTension angleInterlink angleOPB/IPB momentMulti-link analysisThree-link analysisOcean engineeringTC1501-1800Naval architecture. Shipbuilding. Marine engineeringVM1-989ENInternational Journal of Naval Architecture and Ocean Engineering, Vol 13, Iss , Pp 848-857 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Tension angle Interlink angle OPB/IPB moment Multi-link analysis Three-link analysis Ocean engineering TC1501-1800 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	Tension angle Interlink angle OPB/IPB moment Multi-link analysis Three-link analysis Ocean engineering TC1501-1800 Naval architecture. Shipbuilding. Marine engineering VM1-989 Jae-bin Lee Gökhan Tansel Tayyar Joonmo Choung In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
description	This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.
format	article
author	Jae-bin Lee Gökhan Tansel Tayyar Joonmo Choung
author_facet	Jae-bin Lee Gökhan Tansel Tayyar Joonmo Choung
author_sort	Jae-bin Lee
title	In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
title_short	In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
title_full	In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
title_fullStr	In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
title_full_unstemmed	In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
title_sort	in-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/027c8c87bf864b4598690f402153c018
work_keys_str_mv	AT jaebinlee inplaneandoutofplanebendingmomentsandlocalstressesinmooringchainlinksusingmachinelearningtechnique AT gokhantanseltayyar inplaneandoutofplanebendingmomentsandlocalstressesinmooringchainlinksusingmachinelearningtechnique AT joonmochoung inplaneandoutofplanebendingmomentsandlocalstressesinmooringchainlinksusingmachinelearningtechnique
_version_	1718409881366560768

In-plane and out-of-plane bending moments and local stresses in mooring chain links using machine learning technique

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