Abstract The work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

中文翻译：

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一种埋地管道外部点蚀损伤数值预测方法

摘要 本文介绍了一种埋地管道外部损伤数值预测方法。油气管道的外部点蚀萌生和腐蚀速率受管道老化、土壤理化性质和阴极保护性能以及涂层条件的影响。在建立损伤预测模型之前，通过灰色关联分析权衡影响因素，然后通过人工神经网络（ANN）建立点蚀深度与外部腐蚀影响因素之间的关系，进行腐蚀损伤预测。随后，将建立的人工神经网络应用于一些选定案例的腐蚀损伤和腐蚀速率预测，并对神经网络预测模型进行分析，并与其他腐蚀速率预测模型进行比较。