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- Publisher
- Emerald Publishing
- Copyright
- © Emerald Publishing Limited
- ISSN
- 0003-5599
- DOI
- 10.1108/acmm-11-2017-1858
- Publisher site
- See Article on Publisher Site
Abstract
This study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.Design/methodology/approachThis paper presents a new method, used to the collected corrosion data of carbon steel provided by the China Gateway to Corrosion and Protection, that combines non-linear gray Bernoulli model (NGBM(1,1) with genetic algorithm to attain the purpose of this study.FindingsResults of the experiments showed that the present study’s method is more accurate than other algorithms. In particular, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method in data sets are 9.15 per cent and 1.23 µm/a, respectively. Furthermore, this study illustrates that model parameter can be used to evaluate the similarity of curve tendency between two carbon steel data sets.Originality/valueCorrosion data are part of a typical small-sample data set, and these also belong to a gray system because corrosion has a clear outcome and an uncertainly occurrence mechanism. In this work, a new gray forecast model was proposed to achieve the goal of long-term prediction of carbon steel in China.
Journal
Anti-Corrosion Methods and Materials – Emerald Publishing
Published: Aug 9, 2019
Keywords: Genetic algorithm; Atmospheric corrosion; Carbon steel; Long-term prediction