Failure risk analysis of pipelines using data-driven machine learning algorithms

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.