An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

Suria Devi Vijaya Kumar, Saravanan Karuppanan, Mark Ovinis

    Research output: Contribution to journalArticlepeer-review

    3 Citations (SciVal)

    Abstract

    Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged from
    Original languageEnglish
    Article number1722
    JournalApplied Sciences (Switzerland)
    Volume12
    Issue number3
    DOIs
    Publication statusPublished (VoR) - 8 Feb 2022

    Keywords

    • artificial neural network
    • remaining strength equation
    • corroded pipeline
    • single defect
    • combined loadings
    • finite element analysis

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