Machine learning based mapping of the wave attenuation mechanism of an inclined thin plate

The surface piercing and floating coastal defense structures can be applied as an alternative to conventional rubble mound structures in some specific circumstances. A partially submerged steeply inclined thin plate (ITP) is also one of the candidate alternative structures. Knowledge about the wave attenuation mechanism of ITP improves the engineer's ability to make more cost-effective design. From this motivation, the mechanism of ITP was modeled by artificial neural networks based on experimental data. It is particularly aimed to reveal some fundamental facts about the attenuation mechanism of ITP, which could not be previously attained solely by the conventional analysis of the relevant experimental data. Surface plots, which depict the relationships between the governing design variables were generated from the developed model. In this way, the influence of each individual parameter on the performance was decomposed in a more precise way. Based on the data-driven model outputs, it was inferred that the most dominant design variable is the wavelength. The ITP performance is enhanced with increasing submergence degree, an effect that becomes even more pronounced in severe wave climate conditions. In such wave conditions, decreasing inclination angles also improve the functionality of the structure. However, the generated data-driven model indicated that the combination of the examined variables can have a more complicated effect on the ITP performance, especially for the longer wave lengths.