AI-Augmented FEA for Offshore Wind Turbine Equipment: A State-of-the-Art Methodology for Accelerated Fabrication Review

Offshore wind energy is projected to exceed 500 GW of installed capacity by 2050, driving unprecedented demand for efficient, high-quality fabrication of monopiles, jacket structures, and nacelle components. Current fabrication reviews—covering dimensional tolerances, weld integrity, and compliance with DNV-GL-ST-0126 and ISO 19902—are predominantly manual, multi-day processes that create costly project bottlenecks. This study presents an AI-augmented, FEA-driven fabrication review framework that integrates CAD models, FEA stress and fatigue simulations, pointcloud scans, weld imagery, and IoT sensor data into a cloud-native digital twin environment. The framework combines rule-based compliance checking, machinelearning defect detection, and FEA-informed criticality ranking to accelerate decisionmaking and focus inspection resources on structurally significant anomalies. Validation was conducted across three industrial-scale case studies: (1) Monopile weld seam inspection, achieving 96.3 % detection accuracy and reducing review time from 8 h to 1.2 h; (2) Jacket node dimensional compliance, detecting non-compliance with 94.8 % accuracy and cutting review from 3 days to 6 h; and (3) Tower section real-time digital twin review, attaining 97.2 % precision with end-to-end review in under 2 h. Weighted averages across all cases exceeded 96 % detection accuracy and delivered an 85.3 % reduction in review duration compared to conventional methods. Projected industrial impact for a 100-turbine campaign includes reductions of over 5,500 inspector hours, $2.3 M in crane downtime, and $1.1 M in re-fabrication costs. The proposed methodology offers a scalable pathway to predictive, structurally informed, and standards-compliant inspection, supporting the rapid, reliable expansion of offshore wind manufacturing. JEL Classification: Q42, L94, C63, O33, C55