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

Wang, Zhenzhou, Sobey, Adam and Wang, Yikun (2021) Corrosion prediction for bulk carrier 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.

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Accepted/In Press date: 16 June 2021

e-pub ahead of print date: 19 June 2021

Published date: October 2021

Additional Information: Funding Information: The authors would like to thank Mr Joseph Morelos and Mr Leonidas Koukos from Lloyd’s Register for their technical guidance and support. The authors also acknowledge the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton, in the completion of this work. The authors acknowledge the funding from EPSRC Impact Acceleration Account, Lloyd’s Register Group and the Lloyd’s Register Foundation. Funding Information: The authors would like to thank Mr Joseph Morelos and Mr Leonidas Koukos from Lloyd's Register for their technical guidance and support. The authors also acknowledge the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton, in the completion of this work. The authors acknowledge the funding from EPSRC Impact Acceleration Account, Lloyd's Register Group and the Lloyd's Register Foundation. Publisher Copyright: © 2021 The Author(s)

Keywords: Artificial neural network, Bulk carrier ballast tanks, Carbon steels, Immersion and atmospheric corrosion, Interpretable machine learning, Marine environments