Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

Suria Devi Vijaya Kumar, Saravanan Karuppanan, Mark Ovinis

    Research output: Contribution to journalArticlepeer-review

    12 Citations (SciVal)

    Abstract

    Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.
    Original languageEnglish
    Article number2259
    JournalMaterials
    Volume15
    Issue number6
    DOIs
    Publication statusPublished (VoR) - 18 Mar 2022

    Funding

    Acknowledgments: This work was supported by Yayasan Universiti Teknologi PETRONAS, Malaysia [015LC0-110].

    FundersFunder number
    Yayasan UTP015LC0-110

      Keywords

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

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