Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN
Onshore oil and gas pipelines are often buried beneath the river bed and channel banks. One of the primary reasons for the exposure of buried pipelines is the scouring mechanism that occurs when shear stress induced on riverbed by flowing water exceeds the resistance of channel bed material. Dependi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/8/11/840 |
| _version_ | 1797549783193223168 |
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| author | Adithyaa Karthikeyan Saadat Mirza Byul Hur Gregory Pearlstein Ronald Ledbetter |
| author_facet | Adithyaa Karthikeyan Saadat Mirza Byul Hur Gregory Pearlstein Ronald Ledbetter |
| author_sort | Adithyaa Karthikeyan |
| collection | DOAJ |
| description | Onshore oil and gas pipelines are often buried beneath the river bed and channel banks. One of the primary reasons for the exposure of buried pipelines is the scouring mechanism that occurs when shear stress induced on riverbed by flowing water exceeds the resistance of channel bed material. Depending on the free spanning length and watercourse flow velocity, the vortex shedding phenomena may cause interactions resulting in a catastrophic pipeline failure. Accurate estimation of parameters that influence critical span length and scour depth become extremely important to maintain the integrity of the pipeline system and optimize its effective service life. This study is aimed at quantifying the relative importance of input variables used in predicting critical span length and scour depth based on the weights obtained from an Artificial Neural Network (ANN). The Artificial Neural Network model is developed by collecting pipeline accident reports from Pipeline and Hazardous Material Safety Administration (PHMSA) database for accidents that occurred due to Vortex Induced Vibration (VIV) loading during flooding in the last 35 years. It is seen that factors such as internal fluid pressure, dynamic lateral and vertical soil stiffness, reduced velocity and age of pipeline have a significant contribution in terms of model weights and help in accurately assessing the pipeline’s vulnerability to failure. |
| first_indexed | 2024-03-10T15:20:20Z |
| format | Article |
| id | doaj.art-01246a4728f14cd1870b3d870ac27fba |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T15:20:20Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-01246a4728f14cd1870b3d870ac27fba2023-11-20T18:31:06ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-10-0181184010.3390/jmse8110840Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANNAdithyaa Karthikeyan0Saadat Mirza1Byul Hur2Gregory Pearlstein3Ronald Ledbetter4Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843, USADepartment of Mathematics, Texas A&M University, College Station, TX 77843, USADepartment of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USAOnshore oil and gas pipelines are often buried beneath the river bed and channel banks. One of the primary reasons for the exposure of buried pipelines is the scouring mechanism that occurs when shear stress induced on riverbed by flowing water exceeds the resistance of channel bed material. Depending on the free spanning length and watercourse flow velocity, the vortex shedding phenomena may cause interactions resulting in a catastrophic pipeline failure. Accurate estimation of parameters that influence critical span length and scour depth become extremely important to maintain the integrity of the pipeline system and optimize its effective service life. This study is aimed at quantifying the relative importance of input variables used in predicting critical span length and scour depth based on the weights obtained from an Artificial Neural Network (ANN). The Artificial Neural Network model is developed by collecting pipeline accident reports from Pipeline and Hazardous Material Safety Administration (PHMSA) database for accidents that occurred due to Vortex Induced Vibration (VIV) loading during flooding in the last 35 years. It is seen that factors such as internal fluid pressure, dynamic lateral and vertical soil stiffness, reduced velocity and age of pipeline have a significant contribution in terms of model weights and help in accurately assessing the pipeline’s vulnerability to failure.https://www.mdpi.com/2077-1312/8/11/840river crossing pipelinesvortex induced vibrationsArtificial Neural Networkscritical span lengthscour depthpipeline integrity management |
| spellingShingle | Adithyaa Karthikeyan Saadat Mirza Byul Hur Gregory Pearlstein Ronald Ledbetter Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN Journal of Marine Science and Engineering river crossing pipelines vortex induced vibrations Artificial Neural Networks critical span length scour depth pipeline integrity management |
| title | Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN |
| title_full | Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN |
| title_fullStr | Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN |
| title_full_unstemmed | Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN |
| title_short | Quantifying Variable Importance in Predicting Critical Span Length and Scour Depth for Failure of Onshore River Crossing Pipelines Using ANN |
| title_sort | quantifying variable importance in predicting critical span length and scour depth for failure of onshore river crossing pipelines using ann |
| topic | river crossing pipelines vortex induced vibrations Artificial Neural Networks critical span length scour depth pipeline integrity management |
| url | https://www.mdpi.com/2077-1312/8/11/840 |
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