IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v237y2023ics0951832023002831.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023002831
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109369?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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).
    3. 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).
    4. 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).
    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. 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).
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. 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.
    3. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. 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).
    5. Li, Pengyu & Wang, Xiufang & Jiang, Chunlei & Bi, Hongbo & Liu, Yongzhi & Yan, Wendi & Zhang, Cong & Dong, Taiji & Sun, Yu, 2024. "Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    8. 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).
    9. Chen, Yinuo & Xie, Shuyi & Tian, Zhigang, 2022. "Risk assessment of buried gas pipelines based on improved cloud-variable weight theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    10. Xiaoyu Cheng & Guangyu Qian & Wei He & Guohui Zhou, 2022. "A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
    11. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).
    12. Li, Gang & Hu, Jiayao & Ding, Yaping & Tang, Aimin & Ao, Jiaxing & Hu, Dalong & Liu, Yang, 2024. "A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    13. Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    14. Li, Haoqian & Wang, Yong & Zeng, Jing & Li, Fansong & Yang, Zhenhuan & Mei, Guiming & Ye, Yunguang, 2024. "Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    15. 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).
    16. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    17. 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).
    18. Yue Su & Jingfa Li & Wangyi Guo & Yanlin Zhao & Jianli Li & Jie Zhao & Yusheng Wang, 2022. "Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model," Energies, MDPI, vol. 15(22), pages 1-19, November.
    19. Lingyun, Guo & Markus, Niffenegger & Jing, Zhou, 2022. "A novel procedure to evaluate the performance of failure assessment models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.