Damage detection for offshore structures using long and short-term memory networks and random decrement technique

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dc.contributor.authorBao, Xingxian
dc.contributor.authorWang, Zhichao
dc.contributor.authorIglesias, Gregorio
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderNatural Science Foundation of Shandong Provinceen
dc.contributor.funderFundamental Research Funds for the Central Universitiesen
dc.contributor.funderKey Technology Research and Development Program of Shandongen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2021-08-16T11:10:22Z
dc.date.available2021-08-16T11:10:22Z
dc.date.issued2021-06-27
dc.date.updated2021-08-16T10:30:08Z
dc.description.abstractA damage detection method is presented which combines the random decrement technique (RDT) with long and short-term memory (LSTM) networks. The method uses the measured vibration response of offshore structures subjected to random excitation and is able to locate and assess the damage with accuracy, even in noisy conditions. The applicability of the proposed RDT-LSTM method is verified through a numerical example and laboratory tests. The numerical example consists of a jacket platform subjected to random wave excitation. The simulated damage cases encompass single and multiple damage locations not only on whole segments but also on local elements (one-fifth of the whole segment) of the numerical structure, with minor (1%–5%) severity, and different noise levels. RDT is applied first to process the noisy random data, and then the damage detection is carried out using LSTM. After the numerical example, the proposed method is applied to laboratory tests of a jacket platform model under random loading produced by a shaking table. Minor and major damages and their combination at different locations are discussed. Both the numerical simulation and laboratory test show that the proposed RDT-LSTM method has an outstanding performance in structural damage detection.en
dc.description.sponsorshipNational Natural Science Foundation of China (Grant No. 51979283); Natural Science Foundation of Shandong Province (Grant No. ZR2018MEE053); Fundamental Research Funds for the Central Universities (Grant No. 20CX02313A); Key Technology Research and Development Program of Shandong (Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment Grant No. 20CX02313A); Science Foundation Ireland (Grant SFI MAREI2_12/RC/2302/P2 Platform RA1b)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid109388en
dc.identifier.citationBao, X., Wang, Z. and Iglesias, G. (2021) 'Damage detection for offshore structures using long and short-term memory networks and random decrement technique', Ocean Engineering, 235, 109388 (14pp). doi: 10.1016/j.oceaneng.2021.109388en
dc.identifier.doi10.1016/j.oceaneng.2021.109388en
dc.identifier.endpage14en
dc.identifier.issn0029-8018
dc.identifier.journaltitleOcean Engineeringen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/11743
dc.identifier.volume235en
dc.language.isoenen
dc.publisherElsevier Ltd.en
dc.rights© 2021, Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDamage detectionen
dc.subjectLong and short-term memory networksen
dc.subjectOffshore structuresen
dc.subjectRandom decrement techniqueen
dc.titleDamage detection for offshore structures using long and short-term memory networks and random decrement techniqueen
dc.typeArticle (peer-reviewed)en
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