Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior

Kellner, L; Stender, M; von Bock und Polach, RUF; Herrnring, H; Ehlers, S; Hoffmann, N; Høyland, KV

Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior

Kellner, L Stender, M

von Bock und Polach, RUF Herrnring, H Ehlers, S Hoffmann, N Høyland, KV

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Publication Type:: Journal Article
Citation:: Cold Regions Science and Technology, 2019, 162 pp. 56 - 73
Issue Date:: 2019-06-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Kellner, L	en_US
dc.contributor.author	Stender, M https://orcid.org/0000-0002-0888-8206	en_US
dc.contributor.author	von Bock und Polach, RUF	en_US
dc.contributor.author	Herrnring, H	en_US
dc.contributor.author	Ehlers, S	en_US
dc.contributor.author	Hoffmann, N	en_US
dc.contributor.author	Høyland, KV	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	Cold Regions Science and Technology, 2019, 162 pp. 56 - 73	en_US
dc.identifier.issn	0165-232X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139457
dc.description.abstract	© 2019 Elsevier B.V. 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.	en_US
dc.relation.ispartof	Cold Regions Science and Technology	en_US
dc.relation.isbasedon	10.1016/j.coldregions.2019.02.007	en_US
dc.subject.classification	Meteorology & Atmospheric Sciences	en_US
dc.title	Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior	en_US
dc.type	Journal Article
utslib.citation.volume	162	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0911 Maritime Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	162	en_US

Abstract:

© 2019 Elsevier B.V. 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.

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http://hdl.handle.net/10453/139457