AI-Based Marine Biofouling Classification and Forecasting for Offshore Renewable Energy Infrastructure

Marine biofouling is the accumulation of organisms on submerged structures, affecting the operation, maintenance, and reliability of offshore renewable energy systems. Marine growth varies with site and environmental conditions (Picken, 1995) and begins almost immediately after installation (Teng and Nath, Proc. OTC, 1989). In offshore wind, waves, and current energy devices, biofouling increases hydrodynamic loading (Emamdoost et al., Proc. OTC, 2026; Marty et al., Mar. Sci. Eng., 2021), reduces fatigue life (Rashid et al., Eng. Appl. Artif. Intell, 2024) and increases inspection and maintenance requirements. Recent studies have applied deep convolutional neural networks (CNNs) for biofouling classification (Signor et al., Ocean Eng., 2023; Chin et al., Complexity, 2017) and machine learning for biofilm growth prediction (Tanudjaja et al., npj Clean Water, 2025; Rashid et al., Expert Syst. Appl. 2025; Lang et al., Transp. Res. Part D, 2026). However, integrated AI frameworks combining image-based biofouling classification with probabilistic thickness prediction remain limited. This study presents an integrated AI framework combining deep-learning image classification with Bayesian probabilistic thickness prediction to support offshore inspection and engineering decision-making.