Effects of introducing dynamic constraints for buckling to truss sizing optimization problems

Petrović, Nenad; Marjanović, Nenad; Kostić, Nikola; Blagojević, M; Matejić, Miloš; Troha, Sanjin

doi:10.5937/fmet1801117p

Cited by 7 publications

(9 citation statements)

References 15 publications

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“…Some of the most frequently used sizing optimization problems in the literature are 10, 17, and 25-bar truss problems. These examples were previously analysed in [12], where the authors, using the same models with continuous cross-section variables, showed the influence of adding the Euler buckling constraints. The addition of the Euler buckling constraints to truss structural optimization problems ensures that the optimal truss configurations can stay in the elastic zone and maintain stability.…”

Section: Optimizationmentioning

confidence: 99%

“…A few studies which consider dynamic buckling constraints have been published in recent years [10][11][12][13][14][15]. In [12], the authors made a comparison between using and not using buckling constraints for truss sizing problems with continuous variables. In their research, they also tested existing results from the literature which do not consider this constraint and found that all models have some bar elements which would not meet buckling criteria.…”

Section: Introductionmentioning

confidence: 99%

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Means and Effects оf Constraining the Number of Used Cross-Sections in Truss Sizing Optimization

Petrović

Marjanović

Kostić

et al. 2020

TFAMENA

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This paper looks at sizing optimization results, and attempts to show the practical implications of using a novel constraint. Most truss structural optimization problems, which consider sizing in order to minimize weight, do not consider the number of different crosssections that the optimal solution can have. It was observed that all, or almost all, crosssections were different when conducting the sizing optimization. In practice, truss structures have a small, manageable number of different cross-sections. The constraint of the number of different cross-sections, proposed here, drastically increases the complexity of solving the problem. In this paper, the number of different cross-sections is limited, and optimization is done for four different sizing optimization problems. This is done for every number of different cross-section profiles which is smaller than the number of cross-sections in the optimal solution, and for a few numbers greater than that number. All examples are optimized using dynamic constraints for Euler buckling and discrete sets of cross-section variables. Results are compared to the optimal solution without a constrained number of different crosssections and to an optimal model with just a single cross-section for all elements. The results show a small difference between optimal solutions and the optimal solutions with a limited number of different profiles which are more readily applicable in practice.

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Section: Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Means and Effects оf Constraining the Number of Used Cross-Sections in Truss Sizing Optimization

Petrović

Marjanović

Kostić

et al. 2020

TFAMENA

View full text Add to dashboard Cite

show abstract

“…Optimization is the process of finding solutions from a group of alternative possible solutions. These solutions necessitate better characteristics of the construction, while at the same time decreasing invested effort and expended costs [6]. Parametric truss structural optimization is an iterative process which is used to improve desired characteristics of a truss structure.…”

Section: Structural Optimization Problemmentioning

confidence: 99%

“…Authors in [6] used continuous variables for truss sizing optimization on examples with 10, 17 and 25 bars with the added dynamic buckling constraint, showing the influence of the added constraint on optimal weight compared to work in [7], which did not consider buckling. In [8] researchers used continuous cross-sectional parameters for truss optimization of sizing, shape, topology and all their combinations.…”

Section: Introductionmentioning

confidence: 99%

Influence of Using Discrete Cross-Section Variables for All Types of Truss Structural Optimization with Dynamic Constraints for Buckling

Petrović¹,

Kostić²,

Marjanović³

et al. 2018

Applied Engineering Letters

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The use of continuous variables for cross-sectional dimensions in truss structural optimization gives solutions with a large number of different cross sections with specific dimensions which in practice would be expensive, or impossible to create. Even slight variations from optimal sizes can result in unstable structures which do not meet constraint criteria. This paper shows the influence of the use of discrete cross section sizes in optimization and compares results to continuous variable counterparts. In order to achieve the most practically applicable design solutions, Euler buckling dynamic constraints are added to all models. A typical space truss model from literature, which use continuous variables, is compared to the discrete variable models under the same conditions. The example model is optimized for minimal weight using sizing and all possible combinations of shape and topology optimizations with sizing.

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“…Optimization of trusses without buckling constraints gives low masses like in [9][10][11][12], but unstable constructions. In [13,14] the differences in optimal mass of models with and without using buckling constraints were compared. The use of buckling constraints can also be found in [15] where researchers compared both mass and overall outer area of different topological cases of a roof truss to show that the minimal mass solution does not always result in savings in all other areas.…”

Section: Introductionmentioning

confidence: 99%

Sizing and shape optimization material use in 10 bar trusses

et al. 2021

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Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

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Effects of introducing dynamic constraints for buckling to truss sizing optimization problems

Abstract: In this paper the effects of adding buckling constraints to truss

Cited by 7 publications

References 15 publications

Means and Effects оf Constraining the Number of Used Cross-Sections in Truss Sizing Optimization

Means and Effects оf Constraining the Number of Used Cross-Sections in Truss Sizing Optimization

Influence of Using Discrete Cross-Section Variables for All Types of Truss Structural Optimization with Dynamic Constraints for Buckling

Sizing and shape optimization material use in 10 bar trusses

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