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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Main Authors: Ben He, Mingbao Lin, Xinran Yu, Zhishuai Zhang, Song Dai
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Journal of Marine Science and Engineering
Subjects:
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
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institution Kabale University
issn 2077-1312
language English
publishDate 2024-12-01
publisher MDPI AG
record_format Article
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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AT xinranyu randomforestbasedpredictionmodelforstiffnessdegradationofoffshorewindfarmsubmarinesoil
AT zhishuaizhang randomforestbasedpredictionmodelforstiffnessdegradationofoffshorewindfarmsubmarinesoil
AT songdai randomforestbasedpredictionmodelforstiffnessdegradationofoffshorewindfarmsubmarinesoil