气候变化领域集成服务门户(CCPortal): Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine

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DOI	10.1007/s11069-021-04544-9
	Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine
	Azimi H.; Shiri H.
发表日期	2021
ISSN	0921030X
起始页码	2307
结束页码	2335
卷号	106 期号:3
英文摘要	Ice gouging problem is a significant challenge threatening the integrity of subsea pipelines in the Arctic (e.g., Beaufort Sea) and even non-Arctic (e.g., Caspian Sea) offshore regions. Determining the seabed response to ice scour through the subgouge soil deformations and the keel reaction forces are important aspects for a safe and cost-effective design. In this study, the subgouge soil deformations and the keel reaction forces were simulated by the extreme learning machine (ELM) for the first time. Nine ELM models (ELM 1–ELM 9) were developed using the key parameters governing the ice–seabed interaction. The number of neurons in the hidden layer was optimized and the best activation function for the ELM network was identified. The premium ELM model, resulting in the lowest level of inaccuracy and complexity and the highest level of correlation with experimental values was identified by performing a sensitivity analysis. The gouge depth ratio and the shear strength of the seabed soil were found to be the most influential input parameters affecting the subgouge soil deformations and the keel reaction forces. A set of the ELM-based equations were proposed to approximate the ice gouging parameters. The uncertainty analysis showed that the premium ELM model slightly underestimated the subgouge soil deformation. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	Extreme learning machine Ice–seabed interaction Sandy seabed Sensitivity analysis Uncertainty analysis
英文关键词	deformation; design; extreme event; ice-structure interaction; machine learning; safety; sandy soil; scour; seafloor; sensitivity analysis; submarine pipeline; uncertainty analysis; Arctic Ocean
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206094
作者单位	Civil Engineering Department, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
推荐引用方式 GB/T 7714	Azimi H.,Shiri H.. Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine[J],2021,106(3).
APA	Azimi H.,&Shiri H..(2021).Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine.Natural Hazards,106(3).
MLA	Azimi H.,et al."Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine".Natural Hazards 106.3(2021).