Multi-Objective Optimization Based on Kriging Surrogate Model and Genetic Algorithm for Stiffened Panel Collapse Assessment
A hyperparameter-optimized Kriging surrogate model was developed for the structural collapse behavior framework presented in this paper. The assessment is conducted on a stiffened panel subject to axial load and lateral pressure, typical of the deck structure of a bulk carrier ship. This behavior is...
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| Published in | Applied Mechanics Vol. 6; no. 2; p. 34 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Zwijnaarde
MDPI AG
30.04.2025
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| Online Access | Get full text |
| ISSN | 2673-3161 2673-3161 |
| DOI | 10.3390/applmech6020034 |
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| Abstract | A hyperparameter-optimized Kriging surrogate model was developed for the structural collapse behavior framework presented in this paper. The assessment is conducted on a stiffened panel subject to axial load and lateral pressure, typical of the deck structure of a bulk carrier ship. This behavior is characterized using nonlinear finite element analysis to determine the collapse response. The surrogate model’s hyperparameters were optimized using a Genetic Algorithm to achieve the best performance, and the trained framework can predict ultimate strength. By following this approach, the problem can be reformulated as a multi-objective optimization task. This framework involves associating the Kriging surrogate model with a multi-objective evolutionary optimization algorithm based on Genetic Algorithms to balance the trade-off between the weight and ultimate strength of the stiffened panel. The results confirm the applicability of the Kriging surrogate framework to predict the ultimate strength and assess the collapse analysis of the stiffened panels, ensuring accuracy through GA-based hyperparameter optimization. |
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| AbstractList | A hyperparameter-optimized Kriging surrogate model was developed for the structural collapse behavior framework presented in this paper. The assessment is conducted on a stiffened panel subject to axial load and lateral pressure, typical of the deck structure of a bulk carrier ship. This behavior is characterized using nonlinear finite element analysis to determine the collapse response. The surrogate model’s hyperparameters were optimized using a Genetic Algorithm to achieve the best performance, and the trained framework can predict ultimate strength. By following this approach, the problem can be reformulated as a multi-objective optimization task. This framework involves associating the Kriging surrogate model with a multi-objective evolutionary optimization algorithm based on Genetic Algorithms to balance the trade-off between the weight and ultimate strength of the stiffened panel. The results confirm the applicability of the Kriging surrogate framework to predict the ultimate strength and assess the collapse analysis of the stiffened panels, ensuring accuracy through GA-based hyperparameter optimization. |
| Author | Isoldi, Liércio André Lima, João Paulo Silva Rocha, Luiz Alberto Oliveira Vieira, Raí Lima dos Santos, Elizaldo Domingues |
| Author_xml | – sequence: 1 givenname: João Paulo Silva orcidid: 0000-0002-6002-0076 surname: Lima fullname: Lima, João Paulo Silva – sequence: 2 givenname: Raí Lima orcidid: 0000-0002-0784-3217 surname: Vieira fullname: Vieira, Raí Lima – sequence: 3 givenname: Elizaldo Domingues surname: dos Santos fullname: dos Santos, Elizaldo Domingues – sequence: 4 givenname: Luiz Alberto Oliveira orcidid: 0000-0003-2409-3152 surname: Rocha fullname: Rocha, Luiz Alberto Oliveira – sequence: 5 givenname: Liércio André orcidid: 0000-0002-9337-3169 surname: Isoldi fullname: Isoldi, Liércio André |
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| SubjectTerms | Accuracy Boundary conditions Efficiency genetic algorithm Genetic algorithms Kriging multi-objective optimization nonlinear finite element analysis Optimization Residual stress Simulation stiffened panels Strain hardening Support vector machines |
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| Title | Multi-Objective Optimization Based on Kriging Surrogate Model and Genetic Algorithm for Stiffened Panel Collapse Assessment |
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