When erosion occurs, sand beaches cannot maintain sufficient sand width, foreshore slopes become steeper due to frequent erosion effects, and beaches are trapped in a vicious cycle of vulnerability due to incident waves. Accordingly, beach nourishment can be used as a countermeasure to simultaneously minimize environmental impacts. However, beach nourishment is not a permanent solution and requires periodic renourishment after several years. To address this problem, minimizing the period of renourishment is an economical alternative. In the present study, using the Tuvaluan coast with its cross-sectional gravel nourishment site, four different test cases were selected for the hydraulic model experiment aimed at discovering an effective nourishment strategy to determine effective alternative methods. Numerical simulations were performed to reproduce gravel nourishment; however, none of these models simultaneously simulated the sediment transport of gravel and sand. Thus, an artificial neural network, a deep learning model, was developed using hydraulic model experiments as training datasets to analyze the possibility of simultaneously accomplishing the sediment transport of sand and gravel and supplement the shortcomings of the numerical models.
Electric field assisted amplification of magnetic fields in tilted Dirac cone systems