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

Author

Listed:
  • 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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    5. Zhang, Zhiwei & Li, Songling & Wang, Huajie & Qian, Hongliang & Gong, Changqing & Wu, Qiongyao & Fan, Feng, 2025. "A study of neural network-based evaluation methods for pipelines with multiple corrosive regions," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    6. Li, Yan & Chen, Zhanfeng & Wang, Wen & Han, Ke & Shuai, Yi & Wang, Ganxun, 2025. "A novel assessment method for residual strength of CO2 pipelines with multiple defects based on RF-MLP," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    7. Li, Pengju & Li, Bin & Fang, Hongyuan & Du, Xueming & Wang, Niannian & Zang, Quansheng & Di, Danyang, 2025. "3D fractal modeling of non-uniform corrosion in steel pipes: Failure behavior analysis and structural integrity assessment," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    8. Yao, Zhenghong & Hao, Jin & Li, Changyou & Jiang, Zhiyuan & Zhao, Jinsong, 2025. "Reliability-based design optimization of fluid-conveying pipeline structure subjected to in-service loadings," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    9. Miao, Xingyuan & Ma, Yinghan & Sun, Xianglong & Zhao, Hong, 2025. "Residual strength prediction of hydrogen-blended natural gas pipelines based on incremental knowledge distillation," Energy, Elsevier, vol. 341(C).
    10. 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).
    11. 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).
    12. 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).
    13. Li, Shulin & Yang, Yan & Huang, Bensheng & Jia, Yanlin, 2025. "Residual strength hybrid prediction of hydrogen-blended natural gas pipelines based on FEM-FC-BP model," Energy, Elsevier, vol. 321(C).
    14. Xiao, Rui & Sushama, Laxmi & Meguid, Mohamed A. & Zayed, Tarek, 2026. "Evaluating the influence of climate change on the deterioration and bearing capacity of corroded gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).

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