Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network
Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on fini...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/6/764 |
| _version_ | 1797485738415095808 |
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| author | Suria Devi Vijaya Kumar Michael Lo Saravanan Karuppanan Mark Ovinis |
| author_facet | Suria Devi Vijaya Kumar Michael Lo Saravanan Karuppanan Mark Ovinis |
| author_sort | Suria Devi Vijaya Kumar |
| collection | DOAJ |
| description | Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes 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 the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R<sup>2</sup>) value of 0.9930 and an error range of −10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80. |
| first_indexed | 2024-03-09T23:23:12Z |
| format | Article |
| id | doaj.art-2c7fa0d3cb414cba8d0fef8114aad6b6 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T23:23:12Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2c7fa0d3cb414cba8d0fef8114aad6b62023-11-23T17:22:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-05-0110676410.3390/jmse10060764Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural NetworkSuria Devi Vijaya Kumar0Michael Lo1Saravanan Karuppanan2Mark Ovinis3Mechanical Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, MalaysiaMechanical Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, MalaysiaMechanical Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, MalaysiaMechanical Engineering Department, Universiti Teknologi Petronas, Seri Iskandar 32610, Perak, MalaysiaConventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes 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 the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R<sup>2</sup>) value of 0.9930 and an error range of −10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80.https://www.mdpi.com/2077-1312/10/6/764artificial neural networkfailure pressure predictionpipeline corrosion |
| spellingShingle | Suria Devi Vijaya Kumar Michael Lo Saravanan Karuppanan Mark Ovinis Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network Journal of Marine Science and Engineering artificial neural network failure pressure prediction pipeline corrosion |
| title | Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network |
| title_full | Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network |
| title_fullStr | Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network |
| title_full_unstemmed | Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network |
| title_short | Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network |
| title_sort | empirical failure pressure prediction equations for pipelines with longitudinal interacting corrosion defects based on artificial neural network |
| topic | artificial neural network failure pressure prediction pipeline corrosion |
| url | https://www.mdpi.com/2077-1312/10/6/764 |
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