Cumulative strain intelligent evaluation of marine soil from offshore wind farms based on enhanced machine learning

IF 4.3 2区 工程技术 Q1 ENGINEERING, OCEAN Applied Ocean Research Pub Date : 2024-10-13 DOI:10.1016/j.apor.2024.104265
{"title":"Cumulative strain intelligent evaluation of marine soil from offshore wind farms based on enhanced machine learning","authors":"","doi":"10.1016/j.apor.2024.104265","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate evaluation of cumulative strains in marine soils under long-term cyclic loading is essential for the design and safe operation of offshore wind turbines. This study proposes an enhanced machine learning model to predict the cumulative strain in marine soils subjected to cyclic loading. Cumulative strains of marine soils from five offshore wind farms under long-term cyclic loading were tested. Four prediction models for cumulative strains were developed and evaluated based on test results using the Back Propagation Neural Network (BP-NN), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, each combined with the Particle Swarm Optimization (PSO) algorithm. The prediction model with the highest accuracy was further analyzed using the SHapley Additive exPlanations (SHAP) method. Results show that the RF and XGBoost algorithms have higher prediction accuracy, with R² values above 0.99, compared to the BP-NN and SVR models. Furthermore, dynamic triaxial test parameters significantly influence the cumulative strain predictions more than the soil properties. This study provides a more efficient method for cumulative strain assessment of marine soils under long-term cyclic loading.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Ocean Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141118724003869","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate evaluation of cumulative strains in marine soils under long-term cyclic loading is essential for the design and safe operation of offshore wind turbines. This study proposes an enhanced machine learning model to predict the cumulative strain in marine soils subjected to cyclic loading. Cumulative strains of marine soils from five offshore wind farms under long-term cyclic loading were tested. Four prediction models for cumulative strains were developed and evaluated based on test results using the Back Propagation Neural Network (BP-NN), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, each combined with the Particle Swarm Optimization (PSO) algorithm. The prediction model with the highest accuracy was further analyzed using the SHapley Additive exPlanations (SHAP) method. Results show that the RF and XGBoost algorithms have higher prediction accuracy, with R² values above 0.99, compared to the BP-NN and SVR models. Furthermore, dynamic triaxial test parameters significantly influence the cumulative strain predictions more than the soil properties. This study provides a more efficient method for cumulative strain assessment of marine soils under long-term cyclic loading.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于增强型机器学习的海上风电场海洋土壤累积应变智能评估
准确评估海洋土壤在长期循环荷载作用下的累积应变对于海上风力涡轮机的设计和安全运行至关重要。本研究提出了一种增强型机器学习模型,用于预测循环加载下海洋土壤的累积应变。对五个海上风电场的海洋土壤在长期循环荷载下的累积应变进行了测试。根据测试结果,使用反向传播神经网络 (BP-NN)、随机森林 (RF)、支持向量回归 (SVR) 和极梯度提升 (XGBoost) 模型,结合粒子群优化 (PSO) 算法,开发并评估了四种累积应变预测模型。使用 SHapley Additive exPlanations (SHAP) 方法对准确率最高的预测模型进行了进一步分析。结果表明,与 BP-NN 和 SVR 模型相比,RF 和 XGBoost 算法的预测精度更高,R² 值超过 0.99。此外,动态三轴试验参数对累积应变预测的影响比土壤特性更大。这项研究为海洋土壤在长期循环荷载下的累积应变评估提供了一种更有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Ocean Research
Applied Ocean Research 地学-工程:大洋
CiteScore
8.70
自引率
7.00%
发文量
316
审稿时长
59 days
期刊介绍: The aim of Applied Ocean Research is to encourage the submission of papers that advance the state of knowledge in a range of topics relevant to ocean engineering.
期刊最新文献
Investigation of morphodynamic response to the storm-induced currents and waves in the Bay of Bengal Wave Energy Potential and the Role of Extreme Events on South America's Coasts. A Regional Frequency Analysis Evaluation, sampling and testing methods for offshore disturbed sands with plastic fines: A case study Dynamic response of three different floating platform (OC4, BSS, GVA) using multi-segment mooring system Numerical study of underwater acoustic radiation and propagation induced by structural vibration in ocean environments using FEM-BMSBM
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1