Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil
Offshore wind power is a hot spot in the field of new energy, with foundation construction costs representing approximately 30% of the total investment in wind farm construction. Offshore wind turbines are subjected to long-term cyclic loads, and seabed materials are prone to causing stiffness degra...
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/8 |
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| author | Ben He Mingbao Lin Xinran Yu Zhishuai Zhang Song Dai |
| author_facet | Ben He Mingbao Lin Xinran Yu Zhishuai Zhang Song Dai |
| author_sort | Ben He |
| collection | DOAJ |
| description | Offshore wind power is a hot spot in the field of new energy, with foundation construction costs representing approximately 30% of the total investment in wind farm construction. Offshore wind turbines are subjected to long-term cyclic loads, and seabed materials are prone to causing stiffness degradation. The accurate disclosure of the mechanical properties of marine soil is critical to the safety and stability of the foundation structure of offshore wind turbines. The stiffness degradation laws of mucky clay and silt clay from offshore wind turbines were firstly investigated in the study. Experiments found that the variations in the elastic modulus presented “<i>L</i>-type” attenuation under small cyclic loads, and the degradation coefficient fleetingly decayed to the strength progressive line under large cyclic loads. Based on the experimental results, a random forest prediction model for the elastic modulus of the submarine soil was established, which had high prediction accuracy. The influence of testing the loading parameters of the submarine soil on the prediction results was greater than that of the soil’s physical property parameters. In criticality, the CSR had the greatest impact on the prediction results. This study provides a more efficient method for the stiffness degradation assessment of submarine soil materials in offshore wind farms. |
| format | Article |
| id | doaj-art-4e53c45aa83e4c6996d580440e61f13c |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-4e53c45aa83e4c6996d580440e61f13c2025-01-24T13:36:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-01131810.3390/jmse13010008Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine SoilBen He0Mingbao Lin1Xinran Yu2Zhishuai Zhang3Song Dai4Key Laboratory of Far Shore Wind Power Technology of Zhejiang Province, Power China Huadong Engineering Corporation Ltd. (HDEC), Hangzhou 311122, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaSchool of Civil Engineering, Shandong University, Jinan 250061, ChinaOffshore wind power is a hot spot in the field of new energy, with foundation construction costs representing approximately 30% of the total investment in wind farm construction. Offshore wind turbines are subjected to long-term cyclic loads, and seabed materials are prone to causing stiffness degradation. The accurate disclosure of the mechanical properties of marine soil is critical to the safety and stability of the foundation structure of offshore wind turbines. The stiffness degradation laws of mucky clay and silt clay from offshore wind turbines were firstly investigated in the study. Experiments found that the variations in the elastic modulus presented “<i>L</i>-type” attenuation under small cyclic loads, and the degradation coefficient fleetingly decayed to the strength progressive line under large cyclic loads. Based on the experimental results, a random forest prediction model for the elastic modulus of the submarine soil was established, which had high prediction accuracy. The influence of testing the loading parameters of the submarine soil on the prediction results was greater than that of the soil’s physical property parameters. In criticality, the CSR had the greatest impact on the prediction results. This study provides a more efficient method for the stiffness degradation assessment of submarine soil materials in offshore wind farms.https://www.mdpi.com/2077-1312/13/1/8offshore wind farmrandom forestsubmarine soilstiffness attenuationvariable importance analysis |
| spellingShingle | Ben He Mingbao Lin Xinran Yu Zhishuai Zhang Song Dai Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil Journal of Marine Science and Engineering offshore wind farm random forest submarine soil stiffness attenuation variable importance analysis |
| title | Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil |
| title_full | Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil |
| title_fullStr | Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil |
| title_full_unstemmed | Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil |
| title_short | Random Forest-Based Prediction Model for Stiffness Degradation of Offshore Wind Farm Submarine Soil |
| title_sort | random forest based prediction model for stiffness degradation of offshore wind farm submarine soil |
| topic | offshore wind farm random forest submarine soil stiffness attenuation variable importance analysis |
| url | https://www.mdpi.com/2077-1312/13/1/8 |
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