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Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network

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  • Chen, Zhanfeng
  • Li, Xuyao
  • Wang, Wen
  • Li, Yan
  • Shi, Lei
  • Li, Yuxing

Abstract

Corrosion defects occurring in natural gas pipelines are common and annoying. The residual strength prediction of corroded pipelines is usually carried out based on theoretical, numerical, and experimental methods. However, the results are hard to obtain when it comes to high nonlinear problems. In this paper, an artificial neural network (ANN) was used to predicting residual strength of corroded pipelines. Due to inadequate training data from previous experiments and extreme iterations which were needed to ensure precision of predicting results, the overfitting phenomenon occurred. To solve the overfitting phenomenon, the training accuracy was reduced artificially by using ReLU activation function and dropout method which was cutting down neurons during ANN training. The results showed that the multilayer perceptron (MLP) conducted dropout method had the highest precision for inadequate sample data compared with relatively simple feedforward neural network (FFNN) structure and FFNN optimized by particle swarm optimization (PSO).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:231:y:2023:i:c:s0951832022005956
    DOI: 10.1016/j.ress.2022.108980
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    References listed on IDEAS

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    1. 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).
    2. 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).
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    Cited by:

    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).
    2. 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).
    3. 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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