Extracting the Solution of Three-Dimensional Wave Diffraction Problem from Two-Dimensional Analysis by Introducing an Artificial Neural Network for Floating Objects

Abstract The diffraction of the waves from the two ends of floating breakwaters (FBWs) that have limited length, are practically a three-dimensional (3D). In order to perform a two-dimensional vertical (2DV) analysis to solve the wave diffraction problem, some “correcting factors” are required to modify the 2DV results and make them comparable and verifiable against 3D solutions. The main objective of the current study is to propose a method to obtain these correcting factors and demonstrate its usefulness through some example cases. An Artificial Neural Network (ANN) is trained by three main non-dimensional independent variables to predict the mentioned factors. In order to set up the ANN, a database including both 2DV and 3D results is required. The 2DV results are obtained by employing a semi-analytical method, namely the Scaled Boundary Finite Element Method (SBFEM). A basic change in the location of the scaling center is implemented. The 3D results are obtained via ANSYS AQWA software. Eighty-one cases are simulated on a floating object with rectangular cross-sections. The correlation factor R = 0.9607 for a group of new samples shows that the predicted results are closely matched to the target values. The correcting factor applies the 3D effects of diffracted waves around the structures on 2DV results and produces a more accurate prediction.