Detecting failed tethers in submerged floating tunnels using an LSTM autoencoder and DNN algorithms

This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (LSTM) autoencoder model trained using response datasets under intact conditions was...

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Vydané v:	Ocean engineering Ročník 312; s. 119105
Hlavní autori:	Min, Seongi, Jeong, Kiwon, Kim, Seungjun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 15.11.2024
Predmet:	Damage detection and localization Deep neural networks LSTM autoencoder Measured motion response data Submerged floating tunnel Tether Submerged floating tunnel Deep neural networks Measured motion response data Damage detection and localization Tether LSTM autoencoder
ISSN:	0029-8018
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