Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques
Subsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing pipeline suspension and leading...
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doaj-20dbf1ddf0344913858e89a60c9b81822021-04-22T23:04:08ZengMDPI AGApplied Sciences2076-34172021-04-01113792379210.3390/app11093792Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence TechniquesMohammad Najafzadeh0Giuseppe Oliveto1Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman 76315-117, IranSchool of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, I-85100 Potenza, ItalySubsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing pipeline suspension and leading to the risk of pipeline failure. The resulting morphological variations of the seabed propagate not only below and normally to the pipeline but also along the pipeline itself. Therefore, 3D scouring patterns need to be considered. Mainly based on the experimental works at laboratory scale by Cheng and coworkers, in this study, Artificial Intelligent (AI) techniques are employed to present new equations for predicting three dimensional current- and wave-induced scour rates around subsea pipelines. These equations are given in terms of key dimensionless parameters, among which are the Shields’ parameter, the Keulegan–Carpenter number, relative embedment depth, and wave/current angle of attach. Using various statistical benchmarks, the efficiency of AI-models-based regression equations is assessed. The proposed predictive models perform much better than the existing empirical equations from literature. Even more interestingly, they exhibit a clear physical consistence and allow for highlighting the relative importance of the key dimensionless variables governing the scouring patterns.https://www.mdpi.com/2076-3417/11/9/3792Evolutionary Polynomial Regression (EPR)Gene-Expression Programming (GEP)Keulegan–Carpenter numbermodel tree (MT)Multivariate Adaptive Regression Splines (MARS)scouring |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohammad Najafzadeh Giuseppe Oliveto |
spellingShingle |
Mohammad Najafzadeh Giuseppe Oliveto Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques Applied Sciences Evolutionary Polynomial Regression (EPR) Gene-Expression Programming (GEP) Keulegan–Carpenter number model tree (MT) Multivariate Adaptive Regression Splines (MARS) scouring |
author_facet |
Mohammad Najafzadeh Giuseppe Oliveto |
author_sort |
Mohammad Najafzadeh |
title |
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques |
title_short |
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques |
title_full |
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques |
title_fullStr |
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques |
title_full_unstemmed |
Exploring 3D Wave-Induced Scouring Patterns around Subsea Pipelines with Artificial Intelligence Techniques |
title_sort |
exploring 3d wave-induced scouring patterns around subsea pipelines with artificial intelligence techniques |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
description |
Subsea pipelines carry oil or natural gas over long distances of the seabed, but fluid leakage due to a failure of the pipeline can culminate in huge environmental disasters. Scouring process may take place beneath pipelines due to current and/or wave action, causing pipeline suspension and leading to the risk of pipeline failure. The resulting morphological variations of the seabed propagate not only below and normally to the pipeline but also along the pipeline itself. Therefore, 3D scouring patterns need to be considered. Mainly based on the experimental works at laboratory scale by Cheng and coworkers, in this study, Artificial Intelligent (AI) techniques are employed to present new equations for predicting three dimensional current- and wave-induced scour rates around subsea pipelines. These equations are given in terms of key dimensionless parameters, among which are the Shields’ parameter, the Keulegan–Carpenter number, relative embedment depth, and wave/current angle of attach. Using various statistical benchmarks, the efficiency of AI-models-based regression equations is assessed. The proposed predictive models perform much better than the existing empirical equations from literature. Even more interestingly, they exhibit a clear physical consistence and allow for highlighting the relative importance of the key dimensionless variables governing the scouring patterns. |
topic |
Evolutionary Polynomial Regression (EPR) Gene-Expression Programming (GEP) Keulegan–Carpenter number model tree (MT) Multivariate Adaptive Regression Splines (MARS) scouring |
url |
https://www.mdpi.com/2076-3417/11/9/3792 |
work_keys_str_mv |
AT mohammadnajafzadeh exploring3dwaveinducedscouringpatternsaroundsubseapipelineswithartificialintelligencetechniques AT giuseppeoliveto exploring3dwaveinducedscouringpatternsaroundsubseapipelineswithartificialintelligencetechniques |
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