PlumX Metrics
Embed PlumX Metrics

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

Metals, ISSN: 2075-4701, Vol: 11, Issue: 2, Page: 1-25
2021
  • 36
    Citations
  • 0
    Usage
  • 25
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    36
    • Citation Indexes
      36
  • Captures
    25

Article Description

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 (R ) 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.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know