An empirical equation for failure pressure prediction of high toughness pipeline with interacting corrosion defects subjected to combined loadings based on artificial neural network

Suria Devi Vijaya Kumar, Saravanan Karuppanan, Mark Ovinis

    Research output: Contribution to journalArticlepeer-review

    5 Citations (SciVal)

    Abstract

    Conventional pipeline corrosion assessment methods for failure pressure prediction do not account for interacting defects subjected to internal pressure and axial compressive stress. In any case, the failure pressure predictions are conservative. As such, numerical methods are required. This paper proposes an alternative to the computationally expensive numerical methods, specifically an empirical equation based on Finite Element Analysis (FEA). FEA was conducted to generate training data for an ANN after validating the method against full scale burst test results from past research. An ANN with four inputs and one output was developed. The equation was developed based on the weights and biases of an ANN model trained with failure pressure from the FEA of a high toughness pipeline for various defect spacings, defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials, with a R2 value of 0.99. Extensive parametric study was subsequently conducted to determine the effects of defect spacing, defect length, defect depth, and axial compressive stress on the failure pressure of the pipe. The results of the empirical equation are comparable to the results from numerical methods for the pipes and loadings considered in this study.
    Original languageEnglish
    Article number2582
    JournalMathematics
    Volume9
    Issue number20
    DOIs
    Publication statusPublished (VoR) - 14 Oct 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

      • interacting defects
      • artificial neural network
      • Finite Element Analysis
      • corrosion assessment method

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