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

Preview

Text materials-15-02259.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB)

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
Identification Number:	10.3390/ma15062259
Dates:	Date Event 11 February 2022 Accepted 18 March 2022 Published Online
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 > College of Engineering
Depositing User:	Mark Ovinis
Date Deposited:	26 Jun 2023 15:08
Last Modified:	20 Jun 2024 11:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14497

Actions (login required)

View Item