#3497. Extreme motion prediction and early-warning assessment of semisubmersible platform based on deep learning method

October 2026publication date
Proposal available till 26-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Engineering
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №3497.1 Contract №3497.2 Contract №3497.3 Contract №3497.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Under extreme met-ocean environmental loads, floating platforms may exhibit complex dynamic states. A long-short-term memory network (LSTM) deep learning network was established between the ocean environmental loads and the extreme values of the 6 DoF response. The predicted results indicate that the present LSTM neural network method could provide higher accuracy. The present prediction method based on CNN was accurate, which can provide guidance for platform operations and early warnings in daily operations.
Keywords:
Early-warning index; Extreme motions; LSTM neural net-work

Contacts :
0