Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings
Vijaya Kumar, Suria Devi and Karuppanan, Saravanan and Ovinis, Mark (2022) Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings. Materials. ISSN 1996-1944
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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.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/ma15062259 | ||||||
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| Uncontrolled Keywords: | artificial neural network, finite element analysis, corrosion assessment method | ||||||
| Subjects: | CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-02 - mechanical engineering | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment > Dept. of Engineering | ||||||
| Depositing User: | Mark Ovinis | ||||||
| Date Deposited: | 26 Jun 2023 15:08 | ||||||
| Last Modified: | 26 Jun 2023 15:08 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14497 |
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