DOI: 10.1007/s11069-021-04544-9
论文题名: Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine
作者: Azimi H. ; Shiri H.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 106, 期: 3 起始页码: 2307
结束页码: 2335
语种: 英语
中文关键词: 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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168893
Appears in Collections: 气候变化与战略
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作者单位: Civil Engineering Department, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
Recommended Citation:
Azimi H.,Shiri H.. Sensitivity analysis of parameters influencing the ice–seabed interaction in sand by using extreme learning machine[J]. Natural Hazards,2021-01-01,106(3)