Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network
There is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal pressure and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/7/1615 |
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| author | Suria Devi Vijaya Kumar Saravanan Karuppanan Veeradasan Perumal Mark Ovinis |
| author_facet | Suria Devi Vijaya Kumar Saravanan Karuppanan Veeradasan Perumal Mark Ovinis |
| author_sort | Suria Devi Vijaya Kumar |
| collection | DOAJ |
| description | There is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal pressure and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, based on Artificial Neural Networks, for the failure pressure prediction of pipe elbows subjected to combined loadings. The neural network was trained with data generated using the Finite Element Method. A parametric analysis was then carried out to study the failure behaviour of corroded high-strength steel subjected to combined loadings. It was found that defect depth, length, spacing (longitudinal), and axial compressive stress greatly influenced the failure pressure of a corroded pipe elbow, especially for defects located at the intrados, with reductions in failure pressure ranging from 12.56–78.3%. On the contrary, the effects of circumferential defect spacing were insignificant, with a maximum of 6.78% reduction in the failure pressure of the pipe elbow. This study enables the failure pressure prediction of corroded pipe elbows subjected to combined loadings using empirical equations. However, its application is limited to single, longitudinally interacting, and circumferentially interacting defects with the specified range of parameters mentioned in this study. |
| first_indexed | 2024-03-11T05:31:03Z |
| format | Article |
| id | doaj.art-b44e741af4f24f0f9495e9c5d03bb0d5 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T05:31:03Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-b44e741af4f24f0f9495e9c5d03bb0d52023-11-17T17:08:09ZengMDPI AGMathematics2227-73902023-03-01117161510.3390/math11071615Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural NetworkSuria Devi Vijaya Kumar0Saravanan Karuppanan1Veeradasan Perumal2Mark Ovinis3Mechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaMechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaMechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaSchool of Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, UKThere is no reliable failure pressure assessment method for pipe elbows, specifically those subjected to internal pressure and axial compressive stress, other than time-consuming numerical methods, which are impractical in time-critical situations. This paper proposes a set of empirical equations, based on Artificial Neural Networks, for the failure pressure prediction of pipe elbows subjected to combined loadings. The neural network was trained with data generated using the Finite Element Method. A parametric analysis was then carried out to study the failure behaviour of corroded high-strength steel subjected to combined loadings. It was found that defect depth, length, spacing (longitudinal), and axial compressive stress greatly influenced the failure pressure of a corroded pipe elbow, especially for defects located at the intrados, with reductions in failure pressure ranging from 12.56–78.3%. On the contrary, the effects of circumferential defect spacing were insignificant, with a maximum of 6.78% reduction in the failure pressure of the pipe elbow. This study enables the failure pressure prediction of corroded pipe elbows subjected to combined loadings using empirical equations. However, its application is limited to single, longitudinally interacting, and circumferentially interacting defects with the specified range of parameters mentioned in this study.https://www.mdpi.com/2227-7390/11/7/1615artificial neural networkcorrosion assessmentfinite element methodpipe elbow |
| spellingShingle | Suria Devi Vijaya Kumar Saravanan Karuppanan Veeradasan Perumal Mark Ovinis Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network Mathematics artificial neural network corrosion assessment finite element method pipe elbow |
| title | Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network |
| title_full | Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network |
| title_fullStr | Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network |
| title_full_unstemmed | Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network |
| title_short | Failure Pressure Prediction of Corroded High-Strength Steel Pipe Elbow Subjected to Combined Loadings Using Artificial Neural Network |
| title_sort | failure pressure prediction of corroded high strength steel pipe elbow subjected to combined loadings using artificial neural network |
| topic | artificial neural network corrosion assessment finite element method pipe elbow |
| url | https://www.mdpi.com/2227-7390/11/7/1615 |
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