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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Vydáno v:Applied Mechanics Ročník 6; číslo 2; s. 34
Hlavní autoři: Lima, João Paulo Silva, Vieira, Raí Lima, dos Santos, Elizaldo Domingues, Rocha, Luiz Alberto Oliveira, Isoldi, Liércio André
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zwijnaarde MDPI AG 30.04.2025
Témata:
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
ISSN:2673-3161, 2673-3161
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Shrnutí: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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ISSN:2673-3161
2673-3161
DOI:10.3390/applmech6020034