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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1522279
This article is part of the Research Topic Failure Analysis and Risk Assessment of Natural Disasters Through Machine Learning and Numerical Simulation: Volume IV View all 17 articles

Envelope and Intelligent Prediction of Horizontal Bearing Capacity for Offshore Wind Monopiles in Sandy Seabed under HM Combined Loading

Provisionally accepted
Xin-Yu You Xin-Yu You 1Shi-Yi Qian Shi-Yi Qian 2Bin Li Bin Li 2Jun Wang Jun Wang 3Ling-Yu Xu Ling-Yu Xu 2*
  • 1 Nanjing Urban Construction Tunnel & Bridge Intelligent Management Co., Ltd., Nanjing, China
  • 2 Institute of Geotechnical Engineering, Nanjing Tech University, Nanjing, China
  • 3 School of Civil Engineering, Nanjing Tech University, Nanjing, Liaoning Province, China

The final, formatted version of the article will be published soon.

    This study presents a practical finite element model for evaluating laterally loaded monopiles embedded in sandy seabed, verified through comparison with field test data from the PISA project. The classical Mohr-Coulomb model, used for soil plasticity in this study, provides reliable predictions and required parameters that are straightforward to determine, enhancing its utility in engineering practice. The numerical model, combines with an artificial neural network (ANN), provides a feasible approach to predict the bearing capacity of monopiles in offshore wind applications, even under different seabed conditions and combined horizontal (H) and moment (M) loads. Results reveal that the horizontal bearing capacity significantly varies depending on slope direction, with increased capacity in the slope upward direction and decreased capacity in the slope downward direction. An elliptical equation is developed to represent the horizontal bearing capacity envelope in the HM plane, accurately predicting ultimate horizontal force (Hu) and bending moment (Mu) across different lengthto-diameter (L/D) ratios and seabed slopes. To further enhance predictive capability, an ANN surrogate model is developed, trained on 288 scenarios. Using L/D ratio, seabed slope, horizontal displacement and rotation angle at the monopile head as inputs, the ANN successfully predicts the horizontal bearing capacity with error margins within ±10%. This research offers a practical, validated finite element and ANN-based approach for modeling and predicting the lateral bearing capacities of monopiles in complex offshore environments, making it a valuable tool for the construction and measurement of offshore wind turbine foundations under HM loading conditions.

    Keywords: Finite Element Analysis, artificial neural network, offshore wind monopile, Horizontal bearing capacity, Failure envelope

    Received: 04 Nov 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 You, Qian, Li, Wang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Ling-Yu Xu, Institute of Geotechnical Engineering, Nanjing Tech University, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.