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

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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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    1. Su, Yue & Li, Jingfa & Yu, Bo & Zhao, Yanlin & Yao, Jun, 2021. "Fast and accurate prediction of failure pressure of oil and gas defective pipelines using the deep learning model," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    3. Dann, Markus R. & Maes, Marc A., 2018. "Stochastic corrosion growth modeling for pipelines using mass inspection data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 245-254.
    4. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Amaya-Gómez, Rafael & Schoefs, Franck & Sánchez-Silva, Mauricio & Muñoz, Felipe & Bastidas-Arteaga, Emilio, 2022. "Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Frank Cheng, 2016. "Monitor safety of aged fuel pipelines," Nature, Nature, vol. 529(7585), pages 156-156, January.
    8. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Dann, Markus R. & Dann, Christoph, 2017. "Automated matching of pipeline corrosion features from in-line inspection data," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 40-50.
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    1. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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