Modeling and predicting the rate of scour depth below pipelines using kernel-based methods for steady flows

Pipelines are used all around the world to transport fluids from one location to another. When these pipelines meet rivers, seas and oceans pipes are laid on the solid bed and it causes changes in the flow pattern around the pipes. In result of these changes, the shear stress of bed under pipelines and turbulence of current will be increased, and scour will occur under pipelines and the scour hole will form and develop. These holes cause damage and failure to the pipe due to the pipe weight. In case of failure of the pipe, irreparable damages will incur to the environment and there will be huge financial costs. Therefore, it is very important to study the scour depth and effective variables to reduce scour and prevent damages. Researchers have conducted experimental and numerical studies on scour phenomenon and have provided relations over the years.
In this research the effect of various factors on this phenomenon in steady current are investigated using Gaussian process regression (GPR) and support vector machine (SVM) and it is compared with the previous presented relations. To this end several laboratory data were used and after defining several non-dimensional parameters the performance of these methods was evaluated. The result of this research demonstrated that these methods are better than experimental relations and have promising outcomes. This study have shown that an SVM model with ℎ/D, D/d, Re and S0 variables in steady current have the best results. It is worth mentioning that ℎ and variables in steady current have the most significant effect on the scour below pipelines.