Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network
Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neu...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/8/10/766 |
| _version_ | 1797552156541190144 |
|---|---|
| author | Dohan Oh Julia Race Selda Oterkus Bonguk Koo |
| author_facet | Dohan Oh Julia Race Selda Oterkus Bonguk Koo |
| author_sort | Dohan Oh |
| collection | DOAJ |
| description | Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines. |
| first_indexed | 2024-03-10T15:56:00Z |
| format | Article |
| id | doaj.art-8600ae8d50cc4350b8325d0208dbf458 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T15:56:00Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-8600ae8d50cc4350b8325d0208dbf4582023-11-20T15:41:07ZengMDPI AGJournal of Marine Science and Engineering2077-13122020-09-0181076610.3390/jmse8100766Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural NetworkDohan Oh0Julia Race1Selda Oterkus2Bonguk Koo3Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UKDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UKDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UKDepartment of Naval Architecture and Marine Engineering, Changwon National University, Changwon-si, Gyeongsangnam-do 51140, KoreaMechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.https://www.mdpi.com/2077-1312/8/10/766artificial neural networkdeep neural networkburst pressurepipelinedentocean and shore technology (OST) |
| spellingShingle | Dohan Oh Julia Race Selda Oterkus Bonguk Koo Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network Journal of Marine Science and Engineering artificial neural network deep neural network burst pressure pipeline dent ocean and shore technology (OST) |
| title | Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network |
| title_full | Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network |
| title_fullStr | Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network |
| title_full_unstemmed | Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network |
| title_short | Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network |
| title_sort | burst pressure prediction of api 5l x grade dented pipelines using deep neural network |
| topic | artificial neural network deep neural network burst pressure pipeline dent ocean and shore technology (OST) |
| url | https://www.mdpi.com/2077-1312/8/10/766 |
| work_keys_str_mv | AT dohanoh burstpressurepredictionofapi5lxgradedentedpipelinesusingdeepneuralnetwork AT juliarace burstpressurepredictionofapi5lxgradedentedpipelinesusingdeepneuralnetwork AT seldaoterkus burstpressurepredictionofapi5lxgradedentedpipelinesusingdeepneuralnetwork AT bongukkoo burstpressurepredictionofapi5lxgradedentedpipelinesusingdeepneuralnetwork |