Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network

Tieyao Zhang, Jian Shuai, Yi Shuai, Luoyi Hua, Kui Xu, Dong Xie and Yuan Mei

Reliability Engineering and System Safety, 2023, vol. 231, issue C

Abstract: With the complexity of geological conditions and corrosive environments, the evaluation of failure pressure for defective pipelines under external loads has gradually become an important part of pipeline integrity management and reliability assessment. In this study, validated, 3D, nonlinear finite element (FE) models are established. Considering the safety margin in the evaluation process of corroded pipelines, the evaluation system of triple failure pressure is proposed. Subsequently, many simulations have been carried out with validated FE models. The obtained dataset is selected for training the backpropagation neural network (BPNN) model. After hyperparameter analysis and comparison, the triple failure pressure prediction BPNN model, which is based on a 9-dimensional input layer, a 7-dimensional 180-node hidden layer and a 3-dimensional output layer, is established. Through double verification, it is considered that the established BPNN model has high accuracy in predicting the ultimate burst pressure of corroded pipelines. While rapidly predicting the ultimate burst pressure, the model can synchronously output the flow failure pressure and yield failure pressure. With the established deep learning model, the multiple failure assessment curves of defective pipelines are automatically generated within seconds, which provides a convenient evaluation reference for the defect problems encountered in practical engineering.

Keywords: Corroded oil/gas pipeline; Finite element modelling; Triple failure pressure; Back propagation neural network; Failure assessment diagram (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006056

DOI: 10.1016/j.ress.2022.108990

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