A novel framework for predicting the burst pressure of energy pipelines with clustered corrosion defects

• Published in: Thin-walled structures 2024-12, Vol.205, p.112413, Article 112413
• Main Authors: Shuai, Yi, Zhang, Yi, Shuai, Jian, Xie, Dong, Zhu, Xueming, Zhang, Zhuwu
• Format: Article
• Language: English
• Subjects: Failure pressure; Finite element model; Interacting coefficient between defects; Machine learning methods; Multiple defects; Pipeline
• ISSN: 0263-8231
• DOI: 10.1016/j.tws.2024.112413

•The influence of multiple corrosion defects on energy pipelines reveals based on the finite element method.•A prediction model for interaction coefficients of combined defects was developed by integrating machine learning methods and finite element models, which can be used to characterize the interaction strength of cluster corrosion defects.•A failure pressure assessment framework for energy pipelines containing cluster corrosion defects was proposed on the concept of interaction coefficients between defects, which can be applied to a multitude of stochastic scenarios of pipeline corrosion.•The predictive methodology shows a higher accuracy in n evaluating the failure pressure of energy pipelines containing cluster corrosion defects than other publicly available criteria. The accurate prediction of failure pressures in pipelines containing multiple defects is important for assessing the integrity and reliability of corroded pipelines. First, the effect of the remaining defects on the failure pressure of the pipeline containing triple defects was investigated by developing the finite element (FE) model of pipelines containing triple defects. Then, to integrate machine learning method and finite element models to establish a prediction model for the interaction coefficients corresponding to combined corrosion defects. Subsequently, a framework for predicting the burst pressure of pipelines appliable to the scenario of group corrosion defects was proposed by integrating the interacting coefficient and the failure pressure of single defect. Finally, the accuracy of the model was demonstrated by the evaluation indicators and bursting experimental data of corroded pipeline containing cluster defects. The framework is expected to provide a maintenance foundation for the integrity assessment of pipelines with clustered corrosion defects.