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

Show simple item record

dc.contributor.author Bao, Xingxian
dc.contributor.author Wang, Zhichao
dc.contributor.author Iglesias, Gregorio
dc.date.accessioned 2021-08-16T11:10:22Z
dc.date.available 2021-08-16T11:10:22Z
dc.date.issued 2021-06-27
dc.identifier.citation Bao, 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.2021.109388 en
dc.identifier.volume 235 en
dc.identifier.startpage 1 en
dc.identifier.endpage 14 en
dc.identifier.issn 0029-8018
dc.identifier.uri http://hdl.handle.net/10468/11743
dc.identifier.doi 10.1016/j.oceaneng.2021.109388 en
dc.description.abstract A 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.sponsorship National 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier 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.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Damage detection en
dc.subject Long and short-term memory networks en
dc.subject Offshore structures en
dc.subject Random decrement technique en
dc.title Damage detection for offshore structures using long and short-term memory networks and random decrement technique en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Jose Gregorio Iglesias Rodriguez, Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: gregorio.iglesias@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 24 months after publication by request of the publisher. en
dc.check.date 2023-06-27
dc.date.updated 2021-08-16T10:30:08Z
dc.description.version Accepted Version en
dc.internal.rssid 578231827
dc.contributor.funder National Natural Science Foundation of China en
dc.contributor.funder Natural Science Foundation of Shandong Province en
dc.contributor.funder Fundamental Research Funds for the Central Universities en
dc.contributor.funder Key Technology Research and Development Program of Shandong en
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Ocean Engineering en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress gregorio.iglesias@ucc.ie en
dc.identifier.articleid 109388 en


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

© 2021, Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2021, Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license.
This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement