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

• Published in: Journal of soils and sediments Vol. 20; no. 8; pp. 3204 - 3216
• Main Authors: Li, Jingyang, Men, Cong, Qi, Junfeng, Yang, Bin, Liu, Ruimin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; carbon; Carbon steel; Carbon steels; Casualties; China; Corrosion; Corrosion rate; Earth and Environmental Science; Environment; Environmental factors; Environmental Physics; Factor analysis; Geographic information systems; Geographical information systems; Impact analysis; Impact factors; Information systems; Killed steels; Mapping; Moisture content; Neural networks; Organic matter; Pitting (corrosion); prediction; Prediction models; Predictions; salt content; Sec 5 • Soil and Landscape Ecology • Research Article; Soil properties; Soil Science & Conservation; Soil types; Soils; Spatial variations; Steel; Underground corrosion; Water content; China; Q235 carbon steel; Pitting corrosion; Mean impact value; Back propagation artificial neural network; Geographical information system
• ISSN: 1439-0108; 1614-7480
• DOI: 10.1007/s11368-020-02649-5

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/(dm 2 ·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.