Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Predictions of Bearing Capacities of OWT Monopile Foundations based on BP Neutral Network
Jinjin Zhai, Qilin Yin*

School of Naval Architecture & Ocean Engineering, Jiangsu University of Science and Technology
Zhenjiang, Jiangsu, China

ABSTRACTembedded in subsea soil to resist the moment load resulted from thewind, current and wave. As the demand for wind energy increases
rapidly in recent years, the OWTs are moving to deeper waters, whichmeans thicker and longer monopiles are needed to withstand greater
environmental loads. Because of the high investment and large costalong with foundation structure, it is necessary to do some researcheson the bearing capacities of OWT monopile foundations.

Offshore wind energy has become one of the fastest growing
sustainable energies. The bearing performances of foundation structuresof offshore wind turbines (OWTs) are closely associated with structural
security. In this paper, taking into account the coupling effects of wind,
wave and current on the OWTs, the three-dimensional non-linear finite
element analysis coupled with the BP neutral network model areapplied on prediction of the static bearing performances of OWTmonopile foundations. Meanwhile, the normal operating condition andthe braked condition of OWT structures are considered in the coupledmodel. The non-linear finite element analyses coupled with theprobabilistic statistical methodology can provide reference for the online monitoring of the structural safety state of the OWT, and can bepotentially applied to subsequent analyses including the failure risk
analysis, economic evaluation, and structural optimization.

In this paper, the non-linear finite element analysis coupled with theprobabilistic statistical methodology are applied on prediction of thestatic bearing performances of OWT monopiles. Considering thecoupling effects of wind, wave and current on the OWTs, threedimensional finite element models (FEMs) considering the soilstructure interaction (SSI) are established to calculate the static bearing
performances of monopiles. The results of FEMs are used as the sample
data to establish the BP neural network model, then the static bearing
performances of OWTs monopiles can be obtained. This coupled modelis used to rapidly calculate the static bearing performances of OWTmonopile foundations under different wind, wave and current loads,
which can provide reference for the on-line monitoring of the structurestate of OWTs, which can provide reference for the on-line monitoringof the structure state of OWTs. Besides, it could be potentially applied
to conduct subsequent analysis including the failure risk analysis,
economic evaluation, and structural optimization.

KEY WORDS: Offshore wind turbines; bearing capacities; threedimensional finite element model; BP neutral network; prediction.
INTRODUCTION
Offshore wind energy development has been the focus of marine
engineers and scientists during the past decades. The proportion ofoffshore wind energy in the total renewable energy is also growing
rapidly after the years of development. Offshore wind is expected togrow from 6GW in 2019 to nearly 80GW in 2024, bringing its market
share in global new installations from 10% in 2019 to 20% by 2024
(GWEC, 2019). However, compared to the onshore wind energy
development, the cost of offshore wind energy development is muchhigher. It is mainly due to the large cost of the foundation structure,
which may account for 20%~30% of the total cost of an offshore windturbine (OWT) (Yang et al., 2018). Monopiles are currently the mostwidely used foundation types for offshore wind turbines (OWTs), due
to its advantages of small self-weight, simple manufacturing andmature installation technology (Alamo et al, 2018). The monopile is*BP NEURAL NETWORKNeural network is a nonlinear dynamic system originated from theworking principle of artificial neurons, which is a rapidly developing
frontier interdisciplinary subject in recent years (Yin et al., 2021). The
system is very efficient at processing information according to thedynamic response of external input information. Neural network has
many characteristics of self-organization, self-learning, association,
fault tolerance, anti-interference and nonlinear dynamic processing.
The most prominent feature of neural network is that a reasonably
designed neural network with arbitrary accuracy can approach any

Corresponding author427