Active control method for the sinking of open caissons: A data-driven approach based on CNN and time series prediction

Open caissons are widely used in civil engineering as deep foundations and underground structures. It is necessary to control the sinking process of open caissons to achieve safe sinking. This paper proposes a key index of open caisson sinking, the sinking by earth excavation index (SEI). Then, a time series prediction model of SEI based on convolutional neural network (CNN) was established, and an active control method with a data-driven strategy was proposed based on this model. The proposed model was applied in the Changtai Yangtze River Bridge Project in China and was validated by field data. Finally, the effects of hyperparameters in the model were analysed, and a real-time SEI prediction was simulated to describe the practicability of the model. Results show that the proposed model is reliable and practicable in open caisson engineering, and the prediction accuracy is higher than that of other algorithms. Three hyperparameters have little effect on prediction accuracy, including the number of neurons in the fully connected layer, dropping probability of dropout and input length, while the prediction length has a strong influence. With strong objectivity and remarkable performance, the proposed method is conducive to ensuring the steady state and safety of open caissons.

• Record URL:
  https://doi.org/10.1016/j.oceaneng.2022.111683
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0029801822010393
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Dong, Xuechao
  • Guo, Mingwei
  • Wang, Shuilin
• Publication Date: 2022-8-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 111683
• Serial:
  • Ocean Engineering
  • Volume: 257
  • 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: Caissons; Control systems; Data analysis; Mathematical prediction; Neural networks; Time series
• Identifier Terms: Changtai Yangtze River Bridge (China)
• Subject Areas: Marine Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01850515
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2022 1:33PM