Failure Pressure Prediction of Medium to High Toughness Pipe with Circumferential Interacting Corrosion Defects Subjected to Combined Loadings Using Artificial Neural Network

Vijaya Kumar, Suria Devi and Lo, Michael and Karuppanan, Saravanan and Ovinis, Mark (2022) Failure Pressure Prediction of Medium to High Toughness Pipe with Circumferential Interacting Corrosion Defects Subjected to Combined Loadings Using Artificial Neural Network. Applied Sciences, 12 (9). p. 4120. ISSN 2076-3417

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Abstract

Assessment of a corroded pipe is crucial to determine when it must be repaired or replaced. However, the conventional corrosion assessment codes for the failure pressure predictions of corroded pipes with circumferentially aligned interacting defects are conservative (underestimations of more than 40%), resulting in premature repair or replacements of pipelines. Alternatively, numerical approaches may be used, but they are time consuming and computationally expensive. In this study, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with circumferentially aligned interacting corrosion defects subjected to combined loadings is proposed. An artificial neural network trained with failure pressure obtained from the finite element analysis of the three pipe grades for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of these parameters on the failure pressure of a corroded pipe with circumferential-interacting defects was conducted using the equation to determine the correlation between the defect geometries and failure pressure of the pipe. The new equations predicted failure pressures for these pipe grades with an R2 value of 0.99 and an error range of −9.92% to 0.98% for normalised defect spacings of 0.00 to 3.00, normalised effective defect lengths of 0.00 to 2.95, normalised effective defect depths of 0.00 to 0.80, and normalised axial compressive stress of 0.00 to 0.60.

Item Type:	Article
Identification Number:	10.3390/app12094120
Dates:	Date Event 29 March 2022 Accepted 19 April 2022 Published Online
Uncontrolled Keywords:	artificial neural network; finite element analysis; corrosion assessment method; interacting defects; combined loading
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:	27 Sep 2022 15:28
Last Modified:	20 Jun 2024 11:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/13611

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