Long-term prediction on atmospheric corrosion data series of carbon steel in China based on NGBM(1,1) model and genetic algorithm
Anti-Corrosion Methods and Materials
ISSN: 0003-5599
Article publication date: 30 April 2019
Issue publication date: 9 August 2019
Abstract
Purpose
This study aims to achieve long-term prediction on a specific monotonic data series of atmospheric corrosion rate vs time.
Design/methodology/approach
This 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.
Findings
Results 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/value
Corrosion 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.
Keywords
Acknowledgements
The authors would like to thank the National Natural Science Foundation of China (grant no. 51271032), the National Natural Science Foundation of China (grant no. 51131005) and the National Environmental Corrosion Platform for supporting this work.
Citation
Zhi, Y., Fu, D., Yang, T., Zhang, D., Li, X. and Pei, Z. (2019), "Long-term prediction on atmospheric corrosion data series of carbon steel in China based on NGBM(1,1) model and genetic algorithm", Anti-Corrosion Methods and Materials, Vol. 66 No. 4, pp. 403-411. https://doi.org/10.1108/ACMM-11-2017-1858
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited