Analyzing flexural strength data of ice: how useful is explainable machine learning?

First published in
Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE  
Number in series
6
Article Number
V006T07A012
Citation
ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022
Contribution to Conference
ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022  
Publisher DOI
10.1115/OMAE2022-79211
Scopus ID
2-s2.0-85140894610
Publisher
American Society of Mechanical Engineers
ISBN
978-0-7918-8591-8
The climate crisis results in a rapid sea ice decline, making shipping routes accessible for longer durations throughout the year and therefore increasing maritime traffic. At the same time, ice-structure interaction is known to cause damage to ships and structures. In this context, the flexural strength is a key property of the ice. It is also an important factor in the process of the formation of ice ridges which then act as obstacles to marine transit.
Subjects
Data analysis
Explainable ai
Flexural strength
Ice mechanics
Machine learning
Material modeling
DDC Class
600: Technology