Advancing artificial intelligence in ocean and maritime engineering: Trends, progress, and future directions
In: Ocean Engineering. Pergamon: Elmsford. ISSN 0029-8018; e-ISSN 1873-5258, meer
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| Trefwoord |
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| Author keywords |
Machine learning; Structural applications; Hybrid AI models; Ethics; Reinforcement learning; Maritime engineering |
| Auteurs | | Top |
- Portillo Juan, N.
- Negro Valdecantos, V.
- Troch, P., meer
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| Abstract |
This review article presents an analysis of Artificial intelligence (AI) applications in ocean and maritime engineering, examining the evolution, current trends, and future directions of AI in the field.
A key finding is the transformative impact of Reinforcement Learning (RL), which enables real-time adaptation in dynamic and uncertain marine environments, essential for applications such as autonomous navigation, vessel control, and route optimization. Additionally, the study identifies the promising potential of hybrid AI models, which combine optimization algorithms, fuzzy logic, and deep learning to address the complex, nonlinear challenges inherent in maritime structures and fluid-structure interactions.
Looking ahead, the review highlights several promising directions: the expansion of RL into new domains such as coastal erosion modelling and flood prediction; the adoption of transformer architectures for time-series forecasting; and the growing importance of Explainable AI (XAI) and digital twins for transparent and trustworthy deployment in safety-critical systems. As the industry moves towards Artificial General Intelligence (AGI), the article stresses the need for robust regulatory frameworks, ethical safeguards, and the preservation of human oversight to ensure responsible and effective integration of AI technologies in maritime applications. |
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