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 |
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| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Htein Nay Min |u NAOME, University of Strathclyde, Glasgow G4 0LZ, UK; panagiotis.louvros@strath.ac.uk (P.L.); myo.aung@strath.ac.uk (M.A.); evangelos.boulougouris@strath.ac.uk (E.B.) | |
| 245 | 1 | |a AI-Based Optimization Techniques for Hydrodynamic and Structural Design in Ships: A Review | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Structural engineering | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Hydrodynamics | ||
| 653 | |a Modelling | ||
| 653 | |a Weight | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Naval engineering | ||
| 653 | |a Physics | ||
| 653 | |a Computer applications | ||
| 653 | |a Structural design | ||
| 653 | |a Machine learning | ||
| 653 | |a Design | ||
| 653 | |a Efficiency | ||
| 653 | |a Data reduction | ||
| 653 | |a Accuracy | ||
| 653 | |a Design optimization | ||
| 653 | |a Structural weight | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Maritime industry | ||
| 653 | |a Support vector machines | ||
| 653 | |a Decision making | ||
| 653 | |a Optimization | ||
| 653 | |a Ship design | ||
| 653 | |a Weight reduction | ||
| 653 | |a Methods | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Architects | ||
| 653 | |a Neural networks | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Louvros Panagiotis |u NAOME, University of Strathclyde, Glasgow G4 0LZ, UK; panagiotis.louvros@strath.ac.uk (P.L.); myo.aung@strath.ac.uk (M.A.); evangelos.boulougouris@strath.ac.uk (E.B.) | |
| 700 | 1 | |a Stefanou Evangelos |u NAOME, University of Strathclyde, Glasgow G4 0LZ, UK; panagiotis.louvros@strath.ac.uk (P.L.); myo.aung@strath.ac.uk (M.A.); evangelos.boulougouris@strath.ac.uk (E.B.) | |
| 700 | 1 | |a Aung Myo |u NAOME, University of Strathclyde, Glasgow G4 0LZ, UK; panagiotis.louvros@strath.ac.uk (P.L.); myo.aung@strath.ac.uk (M.A.); evangelos.boulougouris@strath.ac.uk (E.B.) | |
| 700 | 1 | |a Hifi Nabile |u BAE Systems Maritime—Naval Ships, South Street, Scotstoun, Glasgow G14 OXN, UK; nabile.hifi@baesystems.com | |
| 700 | 1 | |a Boulougouris Evangelos |u NAOME, University of Strathclyde, Glasgow G4 0LZ, UK; panagiotis.louvros@strath.ac.uk (P.L.); myo.aung@strath.ac.uk (M.A.); evangelos.boulougouris@strath.ac.uk (E.B.) | |
| 773 | 0 | |t Journal of Marine Science and Engineering |g vol. 13, no. 9 (2025), p. 1719-1742 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
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