AI meets the ocean: A new era for safer, smarter marine structures

Marine infrastructure—including offshore wind platforms, coastal bridges, ports, and deep-sea pipelines—forms the backbone of the global maritime economy and energy security. However, unlike inland structures, those in the ocean endure corrosive seawater, strong currents, dynamic load cycles, and unpredictable climate events, making monitoring and maintenance extremely costly. Traditional modeling and simulation approaches struggle to capture 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 marine structural 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 progress, application domains, and development platforms. Their findings were published (DOI: 10.26599/OCEAN.2025.9470005) in Ocean in 2025. The work introduces a structural-mechanism-based modeling framework to enhance prediction reliability and interpretability, addressing data scarcity and model transparency issues. Covering the full lifecycle from design to maintenance, the study illustrates how artificial intelligence is reshaping offshore engineering and providing new tools for safer, more intelligent marine infrastructure systems.

The review first categorizes mainstream ML and deep learning algorithms used in marine engineering, including neural networks, support vector machines, decision trees, and convolutional architectures. These techniques are already applied to structural material durability evaluation, crack recognition, corrosion detection, deformation analysis, and failure prediction. A visual summary (Fig. 1 in the paper) shows ML utilization across design, construction, and maintenance stages, with applications ranging from material optimization to offshore wind turbine monitoring.

A key contribution is the proposed structural-mechanism-based modeling method, which bridges data-driven learning with mechanical principles through parameter selection, database subdivision, and hyperparameter tuning. This addresses a major technical gap—ML models often perform well but remain “black boxes” with limited interpretability. The authors emphasize that improving transparency is essential for safety-critical marine deployment. The paper also identifies core research directions: expanding high-quality datasets under varying sea states, improving model generalization in real environments, integrating SHAP/LIME interpretability tools, and developing collaborative platforms between AI experts and structural engineers. While most existing ML applications remain experimental, the path toward real-world implementation is becoming clearer.

“The ocean is one of the most demanding environments for engineering,” the authors note. “Machine learning offers us a new way to understand structural behavior beyond traditional simulations, but practical adoption depends on transparency and reliability. By combining mechanical knowledge with data-driven  algorithms, we believe future marine infrastructures will achieve longer lifespan, higher safety, and smarter maintenance.”

This work suggests that ML-enhanced modeling will play a vital role in offshore wind construction, marine disaster early-warning, structural degradation prediction, and automated inspection robotics. As climate risks intensify and global demand for marine energy grows, advanced AI tools could significantly reduce maintenance costs and downtime. The review offers a roadmap for deploying interpretable and data-efficient ML systems in real projects. With interdisciplinary collaboration and better ocean-environment data acquisition, AI-powered marine engineering could accelerate sustainable development and ocean resource utilization.

Funding information

This work is supported by Shenzhen Science and Technology Program (Grant No. RCYX20210706092044076).

About Ocean

Ocean is an international, peer-reviewed, open-access journal that provides a multidisciplinary platform for cutting-edge research and practical applications in the fields of ocean science, marine technology, and marine engineering. The journal publishes articles, reviews, and perspectives aimed at advancing theoretical, numerical, site-based, and experimental developments to promote global sustainability.