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

Author

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

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.

Suggested Citation

  • Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022006056
    DOI: 10.1016/j.ress.2022.108990
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    References listed on IDEAS

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    2. Zhou, Jun & Zhu, Jiaxing & Liang, Guangchuan & Ma, Junjie & He, Jiayi & Du, Penghua & Ye, Zhanpeng, 2024. "Three-layer and robust planning models to evaluate the strategies of defense layer, attack layer, and operation layer for optimal protection in natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    3. Wang, Jian & Liu, Huiyuan & Gao, Shibin & Yu, Long & Liu, Xingyang & Zhang, Dongkai & Kou, Lei, 2024. "Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Zerouali, Bilal & Sahraoui, Yacine & Nahal, Mourad & Chateauneuf, Alaa, 2024. "Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    5. Xie, Mingjiang & Wang, Yifei & Zhao, Jianli & Pei, Xianjun & Zhang, Tairui, 2024. "Prediction of pipeline fatigue crack propagation under rockfall impact based on multilayer perceptron," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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