To reduce the losses caused by the atmospheric corrosion of carbon steels, it is important to establish a prediction model to determine the corrosion rate of carbon steels in natural environments. In this study, a prediction model of atmospheric corrosion of Q235 carbon steel (PMACC-Q235) in China was established by coupling the mean impact value algorithm and back propagation artificial neural network. Tempo-spatial patterns of corrosion rates in five long-exposure time categories across China were analyzed. Ten main factors affecting the atmospheric corrosion of Q235 were identified. The corrosion rates in a single year were similar (approximately 30 μm/a) and larger than those for 2 (25.30 μm/a) and 3 years (21.66 μm/a). The spatial corrosion rates in the northwestern areas were primarily lower than those in southeastern coastal areas. This could be influenced by climatic factors, such as temperature, humidity, and precipitation. All corrosion rates reached the C2 level (>1.3 μm/a), and there was some possibility that they reached higher corrosion levels. The largest probability for the C3 level in all periods was an average of 0.91, and that for the C4 level was 0.83. Spatially, higher probabilities were mainly located in the southern area, especially in Hainan, located in the south and surrounded by sea. Corrosion rates largely varied among climatic zones, and mean corrosion rates in the tropical monsoon climate zone were the largest (average of three periods 33.39 μm/a). SO2 and soluble-dust fall had the largest impact on the variations in the corrosion rates among different climatic zones.
Keywords: Atmospheric corrosion; Carbon steel; Impact factor; Prediction model; Spatial variation; Temporal variation.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.