Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based...

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Autores principales:	Byungmo Kim, Chanyeong Kim, Seung-Hyun Ha
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	damage detection artificial neural network offshore helideck mode shape prediction structural integrity assessment Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/15e304b9681a4a1b930a9947c1fd2ff8
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spelling	oai:doaj.org-article:15e304b9681a4a1b930a9947c1fd2ff82021-11-11T19:17:47ZMultiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data10.3390/s212173571424-8220https://doaj.org/article/15e304b9681a4a1b930a9947c1fd2ff82021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7357https://doaj.org/toc/1424-8220A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.Byungmo KimChanyeong KimSeung-Hyun HaMDPI AGarticledamage detectionartificial neural networkoffshore helideckmode shape predictionstructural integrity assessmentChemical technologyTP1-1185ENSensors, Vol 21, Iss 7357, p 7357 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	damage detection artificial neural network offshore helideck mode shape prediction structural integrity assessment Chemical technology TP1-1185
spellingShingle	damage detection artificial neural network offshore helideck mode shape prediction structural integrity assessment Chemical technology TP1-1185 Byungmo Kim Chanyeong Kim Seung-Hyun Ha Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
description	A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.
format	article
author	Byungmo Kim Chanyeong Kim Seung-Hyun Ha
author_facet	Byungmo Kim Chanyeong Kim Seung-Hyun Ha
author_sort	Byungmo Kim
title	Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
title_short	Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
title_full	Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
title_fullStr	Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
title_full_unstemmed	Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
title_sort	multiple damage detection of an offshore helideck through the two-step artificial neural network based on the limited mode shape data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/15e304b9681a4a1b930a9947c1fd2ff8
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Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

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