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AI-Based Optimization Techniques for Hydrodynamic and Structural Design in Ships: A Review

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Publicado en:Journal of Marine Science and Engineering vol. 13, no. 9 (2025), p. 1719-1742
Autor principal: Htein Nay Min
Otros Autores: Louvros Panagiotis, Stefanou Evangelos, Aung Myo, Hifi Nabile, Boulougouris Evangelos
Publicado:
MDPI AG
Materias:
Structural engineering
Comparative analysis
Artificial intelligence
Hydrodynamics
Modelling
Weight
Optimization techniques
Artificial neural networks
Naval engineering
Physics
Computer applications
Structural design
Machine learning
Design
Efficiency
Data reduction
Accuracy
Design optimization
Structural weight
Genetic algorithms
Maritime industry
Support vector machines
Decision making
Optimization
Ship design
Weight reduction
Methods
Optimization algorithms
Architects
Neural networks
Economic
Acceso en línea:Citation/Abstract
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Resumen:Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to <inline-formula>R2</inline-formula> = 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process.
ISSN:2077-1312
DOI:10.3390/jmse13091719
Fuente:Engineering Database