ANN- and FEA-Based Assessment Equation for a Corroded Pipeline with a Single Corrosion Defect
Lo, Michael and Karuppanan, Saravanan and Ovinis, Mark (2022) ANN- and FEA-Based Assessment Equation for a Corroded Pipeline with a Single Corrosion Defect. Journal of Marine Science and Engineering, 10 (4). p. 476. ISSN 2077-1312
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Abstract
Most of the standards available for the assessment of the failure pressure of corroded pipelines are limited in their ability to assess complex loadings, and their estimations are conservative. To overcome this research gap, this study employed an artificial neural network (ANN) model trained with data obtained using the finite element method (FEM) to develop an assessment equation to predict the failure pressure of a corroded pipeline with a single corrosion defect. A finite element analysis (FEA) of medium-toughness pipelines (API 5L X65) subjected to combined loads of internal pressure and longitudinal compressive stress was carried out. The results from the FEA with various corrosion geometric parameters and loads were used as the training dataset for the ANN. After the ANN was trained, its performance was evaluated, and its weights and biases were obtained for the development of a corrosion assessment equation. The prediction from the newly developed equation has a good correlation value, R2 of 0.9998, with percentage errors ranging from −1.16% to 1.78%, when compared with the FEA results. When compared with the failure pressure estimates based on the Det Norske Veritas (DNV-RP-F101) guidelines, the standard was more conservative in its prediction than the assessment equation developed in this study.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/jmse10040476 | ||||||
| Dates: |
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| Uncontrolled Keywords: | failure pressure prediction; artificial neural network; finite element method; single corrosion defect; combined loadings; corroded pipeline | ||||||
| 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 | ||||||
| Depositing User: | Mark Ovinis | ||||||
| Date Deposited: | 27 Sep 2022 15:30 | ||||||
| Last Modified: | 27 Sep 2022 15:30 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13612 |
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