Corrosion prediction for bulk carrier 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