Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading
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...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2024.1522279/full |
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| author | Xin-Yu You Shi-Yi Qian Bin Li Jun Wang Ling-Yu Xu |
| author_facet | Xin-Yu You Shi-Yi Qian Bin Li Jun Wang Ling-Yu Xu |
| author_sort | Xin-Yu You |
| collection | DOAJ |
| description | 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 length-to-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. |
| format | Article |
| id | doaj-art-28d69e14a91a4fc988ca35127fb093f6 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Earth Science |
| spelling | doaj-art-28d69e14a91a4fc988ca35127fb093f62025-01-17T11:05:13ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-01-011210.3389/feart.2024.15222791522279Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loadingXin-Yu You0Shi-Yi Qian1Bin Li2Jun Wang3Ling-Yu Xu4Nanjing Urban Construction Tunnel and Bridge Intelligent Management Co., Ltd., Nanjing, ChinaInstitute of Geotechnical Engineering, Nanjing Tech University, Nanjing, ChinaInstitute of Geotechnical Engineering, Nanjing Tech University, Nanjing, ChinaSchool of Civil Engineering, Nanjing Tech University, Nanjing, ChinaInstitute of Geotechnical Engineering, Nanjing Tech University, Nanjing, ChinaThis 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 length-to-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.https://www.frontiersin.org/articles/10.3389/feart.2024.1522279/fullfinite element analysisartificial neural networkoffshore wind monopilehorizontal bearing capacityfailure envelope |
| spellingShingle | Xin-Yu You Shi-Yi Qian Bin Li Jun Wang Ling-Yu Xu Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading Frontiers in Earth Science finite element analysis artificial neural network offshore wind monopile horizontal bearing capacity failure envelope |
| title | Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading |
| title_full | Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading |
| title_fullStr | Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading |
| title_full_unstemmed | Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading |
| title_short | Envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under HM combined loading |
| title_sort | envelope and intelligent prediction of horizontal bearing capacity for offshore wind monopiles in sandy seabed under hm combined loading |
| topic | finite element analysis artificial neural network offshore wind monopile horizontal bearing capacity failure envelope |
| url | https://www.frontiersin.org/articles/10.3389/feart.2024.1522279/full |
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