Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN)

Suria Devi Vijaya Kumar, Saravanan Karuppanan, Mark Ovinis

    Research output: Contribution to journalArticlepeer-review

    29 Citations (SciVal)

    Abstract

    Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (R2) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress.
    Original languageEnglish
    Article number373
    Pages (from-to)1-25
    Number of pages25
    JournalMetals
    Volume11
    Issue number2
    DOIs
    Publication statusPublished (VoR) - 23 Feb 2021

    Funding

    Funding: This research and the APC was funded by Yayasan University Teknology PETRONAS, Malaysia, grant number 015LC0‐110.

    FundersFunder number
    Yayasan UTP015LC0‐110

      Keywords

      • artificial neural network
      • finite element analysis
      • pipeline corrosion assessment method

      Fingerprint

      Dive into the research topics of 'Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN)'. Together they form a unique fingerprint.

      Cite this