Marine infrastructure, including offshore wind platforms, coastal bridges, and deep-sea pipelines, is critical for the global maritime economy and energy security. However, these ocean structures face severe challenges such as corrosion and unpredictable climate events, making maintenance costly. Traditional modeling fails to address these complexities, prompting a shift to artificial intelligence (AI) techniques like machine learning (ML) and deep learning. Researchers from Southern University of Science and Technology and others reviewed ML applications in marine engineering, showcasing advancements in algorithmic techniques and their impact on structural monitoring and risk prediction. They introduced a structural-mechanism-based modeling framework to improve transparency and reliability, tackling issues of data scarcity. Key findings include the successful application of ML for durability evaluation and failure prediction across the marine infrastructure lifecycle. This work emphasizes the need for interdisciplinary collaboration to harness AI for safer, more efficient marine engineering, ultimately aiding sustainable ocean resource management.
For further details, refer to the research published in Ocean (DOI: 10.26599/OCEAN.2025.9470005).
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