Artificial Intelligence in Predicting Performance of Sustainable Marine Materials

The increasing demand for ecological materials in the marine sector has increased the quest for new materials which are environmental, economic, and durable. However, it is still challenging to predict long-term performances of those materials in complex ocean and corrosive environment based only on the conventional experimental methods. AI has become a revolutionizing factor in speeding up the material discovery and performance rating, life cycle optimization in marine applications. This review looks at the range of AI techniques—machine learning, deep learning, natural language processing and hybrid AI models—used to forecast the performance of sustainable marine materials: bio-composites, corrosion-resistant alloys, nanomaterials, and smart self-healing coatings. Supervised and unsupervised learning algorithms are employed to predict tensile strength and to study corrosion behavior for different environmental conditions. Deep learning models (e.g., CNNs, RNNs) are used to detect microstructural defects and used for time-series monitoring of degradation of protective coatings. Hybrid AI and physics models including FEA and CFD, combined with IoT sensors realize real-time structural health monitoring and the predictive maintenance for maritime assets. In addition, AI enables data-inspired decisions for material selection, design optimization, and environmental benefit. AI-based approaches and methods applied to the field of sustainable marine materials, have not only increased prediction efficiency, but also reduced development times; decreased testing costs, and are in line with the principles for a circular economy. In this review, state-of-the-art and limitations, e.g., data scarcity and interpretability, of AI-based technologies for accelerating marine material innovation are addressed and the future roadmap for exploiting the full potential of AI in marine material innovation and green maritime infrastructure is discussed. Major Findings: AI methodologies such as the development of ML and DL models, further improve the predictability of the corrosion immunity, mechanical strength, and long-term durability of sustainable marine materials. AI systems combining.