Publisher DOI: 10.1016/j.coldregions.2019.02.007
arXiv ID: 1812.03994v2
Title: Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior
Language: English
Authors: Kellner, Leon  
Stender, Merten  
von Bock und Polach, Rüdiger U. Franz 
Herrnring, Hauke 
Ehlers, Sören 
Hoffmann, Norbert  
Høyland, Knut V. 
Keywords: Physics - Data Analysis; Statistics and Probability;Physics - Data Analysis; Statistics and Probability
Issue Date: Jun-2019
Source: Cold Regions Science and Technology (162): 56-73 (2019-06)
Journal or Series Name: Cold regions science and technology 
Abstract (english): Ice material models often limit the accuracy of ice related simulations. The reasons for this are manifold, e.g. complex ice properties. One issue is linking experimental data to ice material modeling, where the aim is to identify patterns in the data that can be used by the models. However, numerous parameters that influence ice behavior lead to large, high dimensional data sets which are often fragmented. Handling the data manually becomes impractical. Machine learning and statistical tools are applied to identify how parameters, such as temperature, influence peak stress and ice behavior. To enable the analysis, a common and small scale experimental database is established. © 2019 Elsevier B.V.
URI: http://hdl.handle.net/11420/2365
ISSN: 0165-232X
Institute: Strukturdynamik M-14 
Konstruktion und Festigkeit von Schiffen M-10 
Type: (wissenschaftlicher) Artikel
Funded by: DFG: EH485/1–1, Bundesministerium für Wirtschaft und Energie 0324022B
Project: Impact of SEA Ice Loads on Global Dynamics of Offshore Wind Turbines 
Räumliche und zeitliche Lastschwankungen bei Eis-Struktur Interaktionen im Großmaßstab 
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