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Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model

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  • Miao, Xingyuan
  • Zhao, Hong

Abstract

Residual strength prediction of defective pipelines is critical to pipeline reliability assessment, which can affect the remaining useful life of pipelines. In this paper, we propose a novel method for predicting residual strength of defective pipelines based on deep extreme learning machine (DELM). To obtain the high accuracy, the hybrid teaching-learning-based optimization (HTLBO) algorithm with multiple adjustment strategies is designed to improve the DELM model. The experimental data of pipeline burst pressure is selected for the training and validation of proposed method. The interactions of input parameters on residual strength are investigated using response surface method. After comparisons of key model parameters, the optimal model is established to ensure the prediction accuracy. Through the validation of benchmark functions, the HTLBO performs well in convergence and optimization performance. The prediction results show that the proposed method has higher precision than other models, and it can predict the residual strength within the relative error of 6%. This study can provide a basis for reliability engineering and transportation safety of defective pipelines.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002831
    DOI: 10.1016/j.ress.2023.109369
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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. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Li, Zai-Wei & Zhou, Yun-Lai & Liu, Xiao-Zhou & Abdel Wahab, Magd, 2023. "Service reliability assessment of ballastless track in high speed railway via improved response surface method," Reliability Engineering and System Safety, Elsevier, vol. 234(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. 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).
    7. Jiang, Shengyu & He, Rui & Chen, Guoming & Zhu, Yuan & Shi, Jiaming & Liu, Kang & Chang, Yuanjiang, 2023. "Semi-supervised health assessment of pipeline systems based on optical fiber monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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    Cited by:

    1. Jianxiong Gao & Yuanyuan Liu & Yiping Yuan & Fei Heng, 2023. "Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings," Mathematics, MDPI, vol. 11(18), pages 1-24, September.

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