ICAME 2021, Balıkesir, Türkiye, 1 - 03 Eylül 2021, sa.111, ss.1-6
Balancing
the offshore structures in the ocean or sea against the forces created by
seismic movements and waves is extremely important for the serviceability and
the safety of the structure. In order to achieve this, various approaches are
currently being considered [1-4]. Region-specific parameters should be recorded
and used when needed to assist these approaches. In addition, the use of
artificial intelligence methods, which have been in demand recently, to reduce
the oscillation in buildings, allows more effective results in the application
that can be done in this field [5-7]. Using the long-short-time memory (LSTM)
algorithm [5], one of the deep learning methods, time series prediction can be
performed. As a result of the prediction, better approaches can be developed
for the future. This study shows that the vibration control of offshore
platforms can be achieved against various types of loadings by the deep
learning techniques, which is a branch of artificial intelligence. For this
purpose, a long pile is analyzed by solving the equation of motion under
forcing described by the Morison equation [8-9]. Thus, a realistic wave load is
applied to analyze system behavior in a more realistic setting. The applied
wave loads are predicted using the LSTM deep learning network and applied to
the system as negative feedback. It is shown that a significant reduction in
the vibration amplitudes can be achieved by this approach. Our findings and
their possible applications are also discussed.