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Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

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International Journal of Minerals, Metallurgy and Materials Aims and scope Submit manuscript

Abstract

This work constructed a machine learning (ML) model to predict the atmospheric corrosion rate of low-alloy steels (LAS). The material properties of LAS, environmental factors, and exposure time were used as the input, while the corrosion rate as the output. 6 different ML algorithms were used to construct the proposed model. Through optimization and filtering, the eXtreme gradient boosting (XGBoost) model exhibited good corrosion rate prediction accuracy. The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach, and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination (RFE) as well as XGBoost methods. The established ML models exhibited better prediction performance and generalization ability via property transformation descriptors. In addition, the SHapley additive exPlanations (SHAP) method was applied to analyze the relationship between the descriptors and corrosion rate. The results showed that the property transformation model could effectively help with analyzing the corrosion behavior, thereby significantly improving the generalization ability of corrosion rate prediction models.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2021YFB3701705).

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  1. School of Civil Engineering, Xiangtan University, Xiangtan, 411105, China

    Jingou Kuang & Zhilin Long

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  1. Jingou Kuang
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  2. Zhilin Long
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Correspondence to Zhilin Long.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Kuang, J., Long, Z. Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms. Int J Miner Metall Mater 31, 337–350 (2024). https://doi.org/10.1007/s12613-023-2679-5

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  • DOI: https://doi.org/10.1007/s12613-023-2679-5

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