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利用人工神经网络从已有的土壤腐蚀试验数据中通过训练求得土壤的理化性能与碳钢在土壤中的腐蚀速度之间的非线性关系，从而预测碳钢在土壤中的腐蚀速度采用的神经网络结构为５—８—１的形式，学习算法采用ＢＰ算法．结果表明，含水量和Ｃｌ－离子是影响碳钢土壤腐蚀的主要因素．
The artificial neural network is used to obtain the nonlinear relationship between the physical and chemical properties of soil and the corrosion rate of carbon steel in the soil from the existing soil erosion test data so as to predict the corrosion rate of carbon steel in the soil. The network structure is in the form of 5-8-1, and the learning algorithm uses BP algorithm. The results show that the water content and Cl- ions are the main factors affecting the corrosion of carbon steel.