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Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data

文献类型：期刊论文

作者Du, Xing2,3; Song, Yupeng2; Wang, Dong3; He, Kunpeng1; Chi, Wanqing2; Xiu, Zongxiang2; Zhao, Xiaolong2
刊名FRONTIERS IN MARINE SCIENCE
出版日期2024-11-15
卷号11页码:15
关键词submarine pipelines gradient boosting support vector machine machine learning wave-current coupling
DOI10.3389/fmars.2024.1491899
英文摘要Predicting wave-induced liquefaction around submarine pipelines is crucial for marine engineering safety. However, the complex of interactions between ocean dynamics and seabed sediments makes rapid and accurate assessments challenging with traditional numerical methods. Although machine learning approaches are increasingly applied to wave-induced liquefaction problems, the comparative accuracy of different models remains under-explored. We evaluate the predictive accuracy of four classical machine learning models: Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF). The results indicate that the GB model exhibits high stability and accuracy in predicting wave-induced liquefaction, due to its strong ability to handle complex nonlinear geological data. Prediction accuracy varies across output parameters, with higher accuracy for seabed predictions than for pipeline surroundings. The combination of different input parameters significantly influences model predictive accuracy. Compared to traditional finite element numerical methods, employing machine learning models significantly reduces computation time, offering an effective tool for rapid disaster assessment and early warning in marine engineering. This research contributes to the safety of marine pipeline protections and provides new insights into the intersection of marine geological engineering and artificial intelligence.
资助项目National Natural Science Foundation of China[41876066] ; National Natural Science Foundation of China[42102326] ; Basic Scientific Fund for National Public Research Institutes of China[2022Q05] ; National Key R&D Program of China[2022YFC2803800]
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种英语
WOS记录号WOS:001365724300001
出版者FRONTIERS MEDIA SA
源URL[http://119.78.100.198/handle/2S6PX9GI/43284]  
专题中科院武汉岩土力学所
通讯作者Song, Yupeng; Wang, Dong
作者单位1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan, Peoples R China
2.Minist Nat Resources MNR, Inst Oceanog 1, Engn Ctr, Qingdao, Peoples R China
3.Ocean Univ China, Coll Environm Sci & Engn, Qingdao, Peoples R China
推荐引用方式
GB/T 7714		 Du, Xing,Song, Yupeng,Wang, Dong,et al. Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data[J]. FRONTIERS IN MARINE SCIENCE,2024,11:15.
APA Du, Xing.,Song, Yupeng.,Wang, Dong.,He, Kunpeng.,Chi, Wanqing.,...&Zhao, Xiaolong.(2024).Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data.FRONTIERS IN MARINE SCIENCE,11,15.
MLA Du, Xing,et al."Comparative evaluation of machine learning models for assessment of seabed liquefaction using finite element data".FRONTIERS IN MARINE SCIENCE 11(2024):15.

入库方式： OAI收割

来源：武汉岩土力学研究所

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