AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions - Mohammed Loukili; Soufiane El Moumni; Kamila Kotrasova (Fluids)
Coastal defense structures play a crucial role in mitigating wave impacts; yet, existing breakwater designs often face challenges in balancing wave reflection, energy dissipation, and structural stability. This study leverages machine learning (ML) to predict the optimal 2D dimensions of rectangular breakwaters in two configurations: submerged at the bottom of a wave tank and positioned at the free surface. Further, the objective is to achieve controlled wave reflection allowing a specific wave run-up and optimized energy dissipation, while ensuring maritime stability. Thus, we used an analytical equation modeling the reflection coefficient versus relative water depth (KH), for different immersion ratios of obstacle (h/H), and relative length (l/H). Two datasets of 32,000 data points were generated for underwater and free-surface breakwaters, with an additional 10,000 data points for validation, totaling 42,000 data points per case. Five ML algorithms—Random Forest, Support Vector Regression, Artificial Neural Network, Decision Tree, and Gaussian Process—were applied and evaluated. Results demonstrated that Random Forest and Decision Tree balanced accuracy with computational efficiency, while the Gaussian Process closely matched analytical results but demanded higher computational resources. These findings support ML as a powerful tool to optimize breakwater design, complementing traditional methods and contributing to more sustainable and resilient coastal defense systems.