Abstract Failure risk analysis of pipeline networks is essential for their effective management. Since pipeline networks are often large and complex, analyzing a large number of pipelines is often intensive and time-consuming. This study utilizes recent advances in machine learning to develop a viable alternative to computationally intensive analytical approaches to determine the failure risk of steel oil and gas pipelines. Burst failure risk of pipelines under active corrosion defects is evaluated considering the remaining strength of pipelines with corrosion pit. In the first part of the study, a comprehensive database of pipelines is generated based on information extracted from literature, and true failure pressure is predicted considering variability between predicted burst failure pressure and experimental burst test results. True burst failure pressure is estimated using various design codes in a probabilistic manner. Among the various burst failure prediction models, the DNV RP-F101 model provides the lowest variability in the failure pressure prediction. Therefore, the model is used to generate the probability of failure of pipelines at a burst limit state. Pipelines are classified based on their probability of failures, from low to severe failure risk groups. Then, eight machine learning algorithms are evaluated based on the generated dataset to identify the best failure prediction model. The performance of machine learning algorithms is evaluated on the basis of a confusion matrix and computational efficiency. It shows that XGBoost is the optimal algorithm in predicting the failure and is recommended for future analysis. The computational efficiency of the machine learning algorithm is also compared to the physics-based model. It is revealed that machine learning algorithms can perform a failure risk analysis of pipelines with greater computational efficiency than the physics-based approach. Even the slowest machine learning algorithm is about 12 times faster than the physics-based model.

中文翻译：

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使用数据驱动的机器学习算法对管道进行故障风险分析

摘要 管网失效风险分析对其有效管理至关重要。由于管道网络通常庞大而复杂，因此分析大量管道通常既费时又费力。这项研究利用机器学习的最新进展来开发一种可行的替代计算密集型分析方法，以确定钢制石油和天然气管道的故障风险。考虑带蚀坑管道的剩余强度，评价活性腐蚀缺陷下管道的爆裂失效风险。在研究的第一部分，基于从文献中提取的信息生成了一个综合的管道数据库，并考虑了预测爆裂破坏压力和实验爆破测试结果之间的可变性来预测真实的破坏压力。使用各种设计规范以概率方式估计真实爆裂失效压力。在各种爆裂故障预测模型中，DNV RP-F101 模型在故障压力预测中提供了最低的可变性。因此，该模型用于生成管道在突发极限状态下的故障概率。管道根据其故障概率进行分类，从低到严重的故障风险组。然后，根据生成的数据集评估八种机器学习算法，以确定最佳故障预测模型。机器学习算法的性能是根据混淆矩阵和计算效率来评估的。这表明 XGBoost 是预测故障的最佳算法，推荐用于未来的分析。机器学习算法的计算效率也与基于物理的模型进行了比较。结果表明，与基于物理的方法相比，机器学习算法可以以更高的计算效率对管道进行故障风险分析。即使是最慢的机器学习算法也比基于物理的模型快 12 倍左右。