Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN
Lo, Michael and Karuppanan, Saravanan and Ovinis, Mark (2021) Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN. Journal of Marine Science and Engineering, 9 (3). p. 281. ISSN 2077-1312
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Abstract
Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging from
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/jmse9030281 | ||||||
| Dates: |
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| Uncontrolled Keywords: | corroded pipeline, interacting corrosion defects, combined loadings, failure pressure, finite element analysis, artificial neural network | ||||||
| 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: | 27 Jun 2023 15:14 | ||||||
| Last Modified: | 27 Jun 2023 15:14 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14504 |
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