The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Corrosion prediction for bulk carriers via data fusion of survey and experimental measurements

Corrosion prediction for bulk carriers via data fusion of survey and experimental measurements
Corrosion prediction for bulk carriers via data fusion of survey and experimental measurements
Accurate corrosion predictions are vital to safe and optimised designs of marine assets. Traditional approaches, including those used to develop rule requirements, seek to use empirical regressions to model corrosion, but most are solely time-dependent. This may lead to conservative damage estimates and hence heavy and inefficient ships. To provide more accurate predictions, this paper presents an interpretable machine learning algorithm based on data fusion of ship survey and experimental measurements. The corrosion behaviour in bulk carrier ballast tanks is interpreted through a sensitivity analysis which quantifies the relationships between operational/environmental factors and the corrosion rate. The prediction accuracy is improved by a minimum of 82% when compared to the two representative empirical models, with a mean absolute error down to 0.10 mm.
Artificial neural network, Bulk carrier ballast tanks, Carbon steels, Immersion and atmospheric corrosion, Interpretable machine learning, Marine environments
0264-1275
Wang, Zhenzhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Wang, Yikun
2729f2f1-36d7-4daa-8589-b61fcc99a313
Wang, Zhenzhou
794c41fe-f5da-4da4-8f1c-c7beb06f87eb
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Wang, Yikun
2729f2f1-36d7-4daa-8589-b61fcc99a313

Wang, Zhenzhou, Sobey, Adam and Wang, Yikun (2021) Corrosion prediction for bulk carriers via data fusion of survey and experimental measurements. Materials & Design, 208 (109910), [109910]. (doi:10.1016/j.matdes.2021.109910).

Record type: Article

Abstract

Accurate corrosion predictions are vital to safe and optimised designs of marine assets. Traditional approaches, including those used to develop rule requirements, seek to use empirical regressions to model corrosion, but most are solely time-dependent. This may lead to conservative damage estimates and hence heavy and inefficient ships. To provide more accurate predictions, this paper presents an interpretable machine learning algorithm based on data fusion of ship survey and experimental measurements. The corrosion behaviour in bulk carrier ballast tanks is interpreted through a sensitivity analysis which quantifies the relationships between operational/environmental factors and the corrosion rate. The prediction accuracy is improved by a minimum of 82% when compared to the two representative empirical models, with a mean absolute error down to 0.10 mm.

Text
Revised manuscript - final - clean version - Accepted Manuscript
Download (2MB)
Text
1-s2.0-S0264127521004639-main - Version of Record
Download (4MB)

More information

Accepted/In Press date: 16 June 2021
e-pub ahead of print date: 19 June 2021
Published date: 23 June 2021
Keywords: Artificial neural network, Bulk carrier ballast tanks, Carbon steels, Immersion and atmospheric corrosion, Interpretable machine learning, Marine environments

Identifiers

Local EPrints ID: 450003
URI: http://eprints.soton.ac.uk/id/eprint/450003
ISSN: 0264-1275
PURE UUID: ece6c2bb-de32-41a4-950e-4642288b085a
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338
ORCID for Yikun Wang: ORCID iD orcid.org/0000-0001-5619-7795

Catalogue record

Date deposited: 02 Jul 2021 16:31
Last modified: 26 Nov 2021 03:01

Export record

Altmetrics

Contributors

Author: Zhenzhou Wang
Author: Adam Sobey ORCID iD
Author: Yikun Wang ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×