Marine infrastructure such as offshore wind platforms, coastal bridges, ports and deepwater pipelines forms the backbone of the world's ocean economy and energy security. However, unlike inland structures, offshore structures endure corrosive seawater, strong currents, dynamic load cycles, and unpredictable weather events, making them very costly to monitor and maintain. Traditional modeling and simulation approaches have difficulty capturing nonlinear behavior and failure mechanisms under such complex conditions. With the rise of artificial intelligence, machine learning and deep learning have become promising alternatives that can learn hidden patterns from large datasets and support damage detection, corrosion monitoring, and structural risk prediction. Due to these challenges, deeper research on machine learning (ML)-based ocean structure solutions is urgently needed.
Researchers from Southern University of Science and Technology, the Hong Kong University of Science and Technology, and the University of Western Australia conducted a comprehensive review of machine learning techniques for marine structural engineering, summarizing algorithmic advances, application domains, and development platforms. Their findings were published in Ocean (DOI: 10.26599/OCEAN.2025.9470005). In this study, we introduce a modeling framework based on structural mechanisms to increase the reliability and interpretability of predictions and address the issues of data scarcity and model transparency. Covering the entire lifecycle from design to maintenance, this research shows how artificial intelligence is reshaping offshore engineering and providing new tools for safer and more intelligent marine infrastructure systems.
This review begins by classifying the mainstream ML and deep learning algorithms used in marine engineering, including neural networks, support vector machines, decision trees, and convolutional architectures. These technologies are already being applied to structural material durability evaluation, crack recognition, corrosion detection, deformation analysis, and failure prediction. A visual overview (Figure 1 of the paper) shows the use of ML across the design, construction, and maintenance stages, from material optimization to offshore wind turbine monitoring.
The main contribution is the proposed structural mechanism-based modeling approach. This technique bridges data-driven learning and machine principles through parameter selection, database granulation, and hyperparameter tuning. This closes a major technical gap where ML models often work well but remain “black boxes” with limited interpretability. The authors emphasize that increased transparency is essential for safety-critical maritime deployments. The paper also identifies core directions for research, including expanding high-quality datasets under different sea conditions, improving generalization of the model in real-world environments, integrating SHAP/LIME interpretation tools, and developing a collaborative platform between AI experts and structural engineers. Most existing ML applications are in the experimental stage, but the path to real-world implementation is becoming clearer.
“The ocean is one of the most demanding environments for engineering,” the authors said. “Machine learning offers new ways to understand structural behavior beyond traditional simulation, but its adoption in practice depends on transparency and trust. By combining machine knowledge with data-driven algorithms, we believe that future marine infrastructure will have longer lifespans, higher safety, and smarter maintenance.”
This study suggests that ML-enhanced modeling will play an important role in offshore wind farm construction, early warning of marine hazards, structural deterioration prediction, and automated inspection robotics. As climate risks intensify and global demand for ocean energy increases, advanced AI tools have the potential to significantly reduce maintenance costs and downtime. This review provides a roadmap for implementing interpretable and data-efficient ML systems into real-world projects. AI-powered ocean engineering has the potential to accelerate sustainable development and utilization of ocean resources through interdisciplinary collaboration and improved capture of marine environmental data.
Funding information
This research is supported by Shenzhen Science and Technology Program (grant number RCYX20210706092044076).
About sea
sea is a peer-reviewed, open-access, international journal that provides an interdisciplinary platform for cutting-edge research and practical applications in the fields of marine science, ocean technology, and ocean engineering. The journal publishes articles, reviews, and perspectives aimed at advancing theoretical, numerical, field-based, and experimental developments to promote global sustainability.
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