Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN

Michael Lo, Saravanan Karuppanan, Mark Ovinis

    Research output: Contribution to journalArticlepeer-review

    24 Citations (SciVal)

    Abstract

    Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from
    Original languageEnglish
    Article number281
    JournalJournal of Marine Science and Engineering
    Volume9
    Issue number3
    DOIs
    Publication statusPublished (VoR) - 8 Feb 2021

    Funding

    Funding: This research was funded by Ministry of Higher Education, Malaysia (FRGS/1/2018/TK03/ UTP/02/1).

    FundersFunder number
    Ministry of Higher Education, MalaysiaFRGS/1/2018/TK03/ UTP/02/1

      Keywords

      • corroded pipeline
      • interacting corrosion defects
      • combined loadings
      • failure pressure
      • finite element analysis
      • artificial neural network

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