An efficient Kriging-based framework for computationally demanding constrained structural optimization problems

Juliani, Marcela Alejandra; Gomes, Wellison José de Santana

doi:10.1007/s00158-021-03095-8

Cited by 13 publications

(3 citation statements)

References 50 publications

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“…The 1-bay 8-story problem is one of the benchmark structural engineering problems (Gandomi and Yang 2011) that has been widely used in the literature (e.g., Juliani and Gomes (2022)). The configuration of this frame structure, including the applied loads, is shown in Fig.…”

Section: Design Of a 1-bay 8-story Framementioning

confidence: 99%

Variable functioning and its application to large scale steel frame design optimization

Gandomi

Deb

Averill

et al. 2022

Struct Multidisc Optim

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To solve complex real-world problems, heuristics and concept-based approaches can be used to incorporate information into the problem. In this study, a concept-based approach called variable functioning (Fx) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the problem structure analysis technique and engineering expert knowledge, the Fx method is used to enhance the steel frame design optimization process as a complex real-world problem. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that Fx can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.

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Section: Design Of a 1-bay 8-story Framementioning

confidence: 99%

Variable functioning and its application to large scale steel frame design optimization

Gandomi

Deb

Averill

et al. 2022

Struct Multidisc Optim

View full text Add to dashboard Cite

show abstract

“…The 1-bay 8-story problem is one of the benchmark structural engineering problems (Gandomi and Yang, 2011), and has been widely used in the literature (e.g. Juliani and Gomes (2022)). The configuration of this frame structure, including the applied loads, is shown in Figure 5.…”

Section: Design Of a 1-bay 8-story Framementioning

confidence: 99%

Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization

Gandomi¹,

Deb²,

Averill³

et al. 2022

Preprint

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To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based approach called variable functioning (F x) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subset of variables are defined with functions using information prior to optimization; thus, instead of modifying the variables in the search process, the function variables are optimized. By using problem structure analysis technique and engineering expert knowledge, the F x method is used to enhance the steel frame design optimization process as a complex real-world problem. The proposed approach is coupled with particle swarm optimization and differential evolution algorithms and used for three case studies. The algorithms are applied to optimize

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“…Zhang et al [10] proposed an active learning Kriging-assisted method to reduce the computational cost in RBDO associated with expensive and time-consuming constraints under distributional probability-box (p-box) model to quantify the uncertain variables and parameters. Marcela et al [11] proposed a global optimization framework based on the Kriging surrogate model to deal with structural problems that have expensive constraints. Yang et al [12] proposed an efficient local adaptive Kriging approximation method with single-loop strategy (LAKAM-SLS) to enhance the computational efficiency of the Kriging-based RBDO methods.…”

Section: Introductionmentioning

confidence: 99%

Quantile-based optimization under uncertainties for complex engineering structures using active learning basis-adaptive PC-Kriging model

Gong,

Zhang,

et al. 2023

Preprint

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The reliability-based design optimization (RBDO) of complex engineering structures considering uncertainty has problems of high-dimensional, highly nonlinear and time-consuming, which requires a significant amount of sampling simulation computation. In this paper, a basis-adaptive PC-Kriging surrogate model is proposed, in order to relieve the computational burden and enhance predictive accuracy of metamodel. The active learning basis-adaptive PC-Kriging model is combined with quantile-based RBDO framework. Finally, four engineering cases have been implemented, including a benchmark RBDO problem, two high dimensional explicit problems and a high dimensional implicit problem. Compared with SVM, Kriging and polynomial chaos expansion models, the results show that the proposed basis-adaptive PC-Kriging model is more robust and efficient for RBDO problems of complex engineering structures.

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An efficient Kriging-based framework for computationally demanding constrained structural optimization problems

Cited by 13 publications

References 50 publications

Variable functioning and its application to large scale steel frame design optimization

Variable functioning and its application to large scale steel frame design optimization

Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization

Quantile-based optimization under uncertainties for complex engineering structures using active learning basis-adaptive PC-Kriging model

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