中文摘要：

通过测量大庆地区区域土壤的理化性质以及碳钢的短期腐蚀数据，分析土壤传质过程的逻辑关系，构建了碳钢短期土壤腐蚀预测模型．通过用该模型在BP人工神经网络中进行学习、训练及模拟，并与现场碳钢埋片腐蚀实验结果对比。进一步验证了腐蚀模型的合理性．结果表明：含水量、空气容量、pH、Cl^-含量、SO4^2-含量和可溶盐总量六种土壤环境参数为影响区域土壤中碳钢腐蚀的主要因素；运用基于Matlab平台的人工神经网络，通过不断地积累土壤腐蚀信息，多次训练后可以建立起稳定性好、泛化能力强的土壤腐蚀预测模型，能较好地预测了大庆地区碳钢在土壤中的腐蚀速率．

英文摘要：

A short-term prediction model for soil corrosion of carbon steel in the regional soil environment of Daqing area was established by measuring the physical and chemical properties of soil in this area, the short-term corrosion data of carbon steel and analyzing the logical relationship among mass transfer processes. The reasonableness of the corrosion model was verified by using BP artificial neural network to learn, train, simulate and compare to the corrosion test results of buried carbon steel samples. The results show that water content, air content, pH, Cl^- content, SO4^2- content and total dissolved salts are the six key factors on soil corrosion of carbon steel in the local soil environment. It is indicated that a stable forecasting model with good generalization ability can be built based on BP artificial neural network through Matlab platform software, by continuous accumulation of soil corrosion information and after adequate training. The model predicts the corrosion rates of carbon steel in Daqing soil accurately.