Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN)
Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/2/373 |
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| author | Suria Devi Vijaya Kumar Saravanan Karuppanan Mark Ovinis |
| author_facet | Suria Devi Vijaya Kumar Saravanan Karuppanan Mark Ovinis |
| author_sort | Suria Devi Vijaya Kumar |
| collection | DOAJ |
| description | Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (<i>R</i><sup>2</sup>) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress. |
| first_indexed | 2024-03-09T00:35:26Z |
| format | Article |
| id | doaj.art-601cb4b20bc14b22ad31049c85d63dbc |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T00:35:26Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-601cb4b20bc14b22ad31049c85d63dbc2023-12-11T18:10:47ZengMDPI AGMetals2075-47012021-02-0111237310.3390/met11020373Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN)Suria Devi Vijaya Kumar0Saravanan Karuppanan1Mark Ovinis2Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaMechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaMechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaConventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (<i>R</i><sup>2</sup>) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress.https://www.mdpi.com/2075-4701/11/2/373artificial neural networkfinite element analysispipeline corrosion assessment method |
| spellingShingle | Suria Devi Vijaya Kumar Saravanan Karuppanan Mark Ovinis Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) Metals artificial neural network finite element analysis pipeline corrosion assessment method |
| title | Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) |
| title_full | Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) |
| title_fullStr | Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) |
| title_full_unstemmed | Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) |
| title_short | Failure Pressure Prediction of High Toughness Pipeline with a Single Corrosion Defect Subjected to Combined Loadings Using Artificial Neural Network (ANN) |
| title_sort | failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network ann |
| topic | artificial neural network finite element analysis pipeline corrosion assessment method |
| url | https://www.mdpi.com/2075-4701/11/2/373 |
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