Based on the open-source code DualSPHysics, a numerical model was conducted to simulate the regular wave transformation on the slope breakwater with artificial block, and the simulation results were verified according to the measured data from the physical experiment. The deviation between the numerical model and the measured data was less than 6% and 9% in wave run-up and overtopping, respectively, which demonstrated the model can reliably capture the wave evolution on the breakwater with an artificial block. Based on this verified model, the size of the artificial block was adjusted to obtain optimal wave-damping effects. Once obtained, the hydrodynamic characteristics of the optimized new artificial block TB-CUBE were further studied, and the effects of the breakwater slope, water depth in front of the breakwater, incident wave period, and the height on wave run-up were all analyzed. Finally, an empirical formula for wave run-up on this type of article block was suggested through data-fitting, for which the correlation coefficient is 0.981.

Aiming at the problem of calculating the overtopping of single-slope breakwaters, a mean impact value-backpropagation (MIV-BP) estimation model for predicting overtopping was established. Experimental data from the Tianjin Research Institute of Water Transport Engineering (TIWTE) were utilized to further enrich the dataset of the CLASH project for single-slope wave overtopping discharge. This paper established a comprehensive prediction model based on an ensemble learning average method combination strategy. There are 10 input parameters in the model, including the offshore effective wave height, average wave period, offshore water depth, toe submergence, toe width, slope tangent, armor rock surface roughness factor, crest height with respect to the static water level, wall height with respect to the static water level, and crest width; the output parameter is the mean overtopping discharge. Subsequently, a comparative analysis was conducted between this estimation model, the Chinese standard formula calculation model, and the European Van der Meer formula calculation model. Compared with the two formulas mentioned above, this estimation model’s coefficient of correlation increased by 0.23 and 0.26, respectively. Finally, a weight evaluation analysis of the 10 main factors affecting overtopping was carried out based on a MIV-BP neural network model. In the analysis, a positive correlation was found for factors, such as the wave height, average wave period, and water depth at the structure toe; a negative correlation was found for factors, such as the slope, crest height with respect to the static water level, wall height with respect to the static water level, and crest width. Overall, the results provide a significant basis and reference for optimizing the design of the wave overtopping control.