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ANALYZING FLEXURAL STRENGTH DATA OF ICE: HOW USEFUL IS EXPLAINABLE MACHINE LEARNING?

Buil, Patrik and Kellner, Leon and Ehlers, Sören and von Bock und Polach, Franz (2022) ANALYZING FLEXURAL STRENGTH DATA OF ICE: HOW USEFUL IS EXPLAINABLE MACHINE LEARNING? In: ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022, 6, V006T07A012. OMAE2022, 2022-06-05 - 2022-06-10, Hamburg. doi: 10.1115/OMAE2022-79211. ISBN 978-079188595-6.

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Official URL: https://doi.org/10.1115/OMAE2022-87434

Abstract

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. Due to the complexity of natural materials, whose properties are often determined by many influencing variables, the development of a suitable material model for ice remains a major challenge. Many experimental studies on flexural strength have been done whose results can be used to draw conclusions about the material properties. However, a major drawback is that these experiments differ significantly regarding, e.g., test method, ice conditions, measured variables and testing boundary conditions, which makes a comparison as well as the derivation of general laws for material properties of ice challenging. Moreover, most studies investigate univariate relationships whereas in reality the behavior of ice is influenced by numerous factors. In this paper an explainable machine learning approach to analyze data from various bending tests and to identify relevant features regarding the flexural strength of ice is presented. Using an approach similar to Kellner et al. [1,2], a database of flexural strength tests is established. The data is used to create machine learning models, whose predictions are interpreted with the explainable AI (XAI) method Shapley Additive exPlanaionts (SHAP). The goal is to show a new approach to investigate the flexural strength of ice and to get a better understanding of how suitable the use of XAI for this problem is

Item URL in elib:https://elib.dlr.de/187372/
Document Type:Conference or Workshop Item (Speech)
Title:ANALYZING FLEXURAL STRENGTH DATA OF ICE: HOW USEFUL IS EXPLAINABLE MACHINE LEARNING?
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Buil, PatrikUniversity DuisburgEssen, Duisburg & Hamburg Ship Model Basin, Hamburg, GermanyUNSPECIFIEDUNSPECIFIED
Kellner, LeonHamburg University of Technology Hamburg, GermanyUNSPECIFIEDUNSPECIFIED
Ehlers, SörenUNSPECIFIEDhttps://orcid.org/0000-0001-5698-9354UNSPECIFIED
von Bock und Polach, FranzHamburg University of Technology & Universität Hamburg Hamburg, GermanyUNSPECIFIEDUNSPECIFIED
Date:5 June 2022
Journal or Publication Title:ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:6
DOI:10.1115/OMAE2022-79211
Page Range:V006T07A012
ISBN:978-079188595-6
Status:Published
Keywords:flexural strength, ice mechanics, material modeling, data analysis, machine learning, explainable AI
Event Title:OMAE2022
Event Location:Hamburg
Event Type:international Conference
Event Start Date:5 June 2022
Event End Date:10 June 2022
Organizer:American Society of Mechanical Engineers (ASME)
HGF - Research field:Energy
HGF - Program:other
HGF - Program Themes:E - no assignment
DLR - Research area:Energy
DLR - Program:E - no assignment
DLR - Research theme (Project):E - no assignment
Location: Geesthacht
Institutes and Institutions:Institute of Maritime Energy Systems
Deposited By: Piazza, Hilke Charlotte
Deposited On:02 Dec 2022 09:19
Last Modified:24 Apr 2024 20:48

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