Automated Fatigue Life Prediction and Optimization of Semi-Submersible Wind Turbine Mooring Systems using Bayesian Optimized Neural Networks (BONNs)

**Abstract:** This paper introduces a novel methodology for enhanced fatigue life prediction and optimization of semi-submersible wind turbine mooring systems. Traditional finite element analysis (FEA) and fatigue models suffer from computational expense and reliance on simplified assumptions. This research proposes a Bayesian Optimized Neural Network (BONN) architecture trained on a combination of FEA simulation data and environmental sensor readings to provide accurate, efficient, and adaptable fatigue life predictions. Furthermore, a multi-objective optimization framework is embedded within the BONN to identify mooring system configurations that minimize fatigue damage while considering cost and stability constraints. The resulting system promises significant advancements in design optimization and reduced operational risk for floating offshore wind farms.