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Comparative Study on the Efficiency of Simulation and Meta-Model-Based Monte Carlo Techniques for Accurate Reliability Analysis of Corroded Pipelines


Mohamed El Amine Ben Seghier, Panagiotis Spyridis, Jafar Jafari-Asl, Sima Ohadi, Xinhong Li

Source title: 
Sustainability: 14(10): 5830, 2022 (ISI)
Academic year of acceptance: 

Estimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. In this paper, the reliability analysis of corroded pipelines is investigated using different simulation and meta-model methods. This includes five simulation approaches, i.e., Monte Carlo Simulation (MCS), Directional Simulation (DS), Line Sampling (LS), Subset Simulation (SS), and Importance Sampling (IS), and two meta-models based on MCS as Kriging-MCS and Artificial Neural Network based on MCS (ANN-MCS). To implement the proposed approaches, three limit state functions (LSFs) using probabilistic burst pressure models are established. These LSFs are designed for describing the collapse failure mode for pipelines constructed of low, mid, and high strength steels and are subjected to corrosion degradation. Illustrative examples that comprise three candidate pipelines made of X52, X65, and X100 steel grade are employed. The performance and efficiency of the proposed techniques for the estimation of the failure probability are compared from different aspects, which can be a useful implementation to indicate the complexity of handling the uncertainties provided by corroded pipelines.