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 used to reconstruct the measured acceleration data of the target structure. Further, the data reconstruction error was used as the input for the deep neural network algorithm trained in advance using the reconstruction error pattern in various tether damage cases. The proposed method was verified by conducting a well-validated simulation based on hydrodynamics. The damage-detection accuracy of the proposed method was directly compared with that of a conventional supervised learning algorithm-based approach. In addition, the case study results confirmed that the proposed approach was applicable to other submerged floating tunnel (SFT) structures by retraining the LSTM autoencoder and deep neural network algorithms with intact datasets only. Thus, this approach does not require a large amount of training data or simulation model updates for other SFT structures.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.119105
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801824024430
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Min, Seongi
  • Jeong, Kiwon
  • Kim, Seungjun
  • 0000-0001-8247-8451
• Publication Date: 2024-11-15

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 119105
• Serial:
  • Ocean Engineering
  • Volume: 312
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Detection and identification; Floating structures; Hydrodynamics; Neural networks; Underwater tunnels
• Subject Areas: Bridges and other structures; Marine Transportation;

Filing Info

• Accession Number: 01931410
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2024 9:17AM