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Impact factor analysis, prediction, and mapping of soil corrosion of carbon steel across China based on MIV-BP artificial neural network and GIS

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Q235 carbon steel is one of the most widely used carbon steels, and soil corrosion and failures of it caused accidents, casualties, and great financial losses. Corrosion of Q235 carbon steel differed in spatial because of spatial variation in soil environmental factors. However, the national scale spatial pattern of soil corrosion of Q235 carbon steel across China has not been explored.

Materials and methods

The values of 12 impact factors, corrosion rate, and pitting corrosion rate at 25 sites covered all soil types in China were collected. Mean impact value (MIV) algorithm and back propagation artificial neural network (BP ANN) were combined and applied in the impact factor analysis. Prediction models for corrosion rate and pitting corrosion were developed based on BP ANN. The proposed prediction models and information about soil properties with high resolution (1 km × 1 km) were used in the prediction of corrosion rate. Based on geographical information system (GIS), the national scale spatial pattern of soil corrosion of Q235 carbon steel across China were analyzed.

Results and discussion

The water content and pH were of the largest influence (|MIV| > 0.522) on both corrosion rate and pitting corrosion rate. For prediction models of corrosion rate and pitting corrosion rate, the predicted values were close to the measured values. Corrosion rates were of higher spatial differences, ranged from 0.632 to 5.181 g/(dm2·a). Pitting corrosion rates in the northeast were higher than other areas, which might be caused by higher values of total salt content, organic matter, and total voidage. Average corrosion rate in soil type 1 (1.161 mm/a) was nearly two times of that in soil type 2 (0.610 mm/a) and over three times of that in other types, indicating that corrosion rate varied largely among different soil types.

Conclusion

The importance of 12 impact factors of corrosion of Q235 carbon steel were evaluated, and the national scale corrosion rate and pitting corrosion rate in China were predicted and mapped for the first time. Both pitting corrosion rates and corrosion rates were of strong spatial variation.

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Acknowledgments

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions on this paper.

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Authors and Affiliations

  1. Beijing Spacecrafts, China Academy of Space Technology, Beijing, 100094, China

    Jingyang Li & Junfeng Qi

  2. Materials Service Safety Assessment Facilities, University of Science and Technology Beijing, Beijing, 100083, China

    Jingyang Li & Bin Yang

  3. State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China

    Cong Men & Ruimin Liu

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  1. Jingyang Li
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Correspondence to Cong Men.

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Li, J., Men, C., Qi, J. et al. Impact factor analysis, prediction, and mapping of soil corrosion of carbon steel across China based on MIV-BP artificial neural network and GIS. J Soils Sediments 20, 3204–3216 (2020). https://doi.org/10.1007/s11368-020-02649-5

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