Weight optimization of plane truss using genetic algorithm

Neeraja, D.; Kamireddy, Thejesh; Kumar, Potnuru Santosh; Reddy, Vijay Simha

doi:10.1088/1757-899x/263/3/032015

Cited by 9 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recent studies have shown that GA (Dede, Bekirolu, & Ayvaz, 2011), TLBO (Dede, 2014), DE (Vu, 2015), Simulated Annealing (SA) (Millan-Paramo, 2018), Ant Lion Optimizer (ALO) (Salar & Dizangian, 2019), Crow Search Algorithm (CSA) (Javidi, Salajegheh, & Salajegheh, 2019) and Artificial Coronary Circulation System (ACCS) algorithm (Kooshkbaghi & Kaveh, 2020) have been used for optimal dimensioning of plane truss structures with minimum weight. Also, optimization of layout and geometry/shape with topology besides dimensioning was done using GA (Chen, Shui, & Huang, 2017;Neeraja, Kamireddy, Kumar, & Reddy, 2017;Rahami, Kaveh, & Gholipour, 2008), Firefly Algorithm (FA) (Miguel, Lopez, & Miguel, 2013), PSO (Harsono et al, 2020;Mortazavi & Toğan, 2016), JA (Degertekin, Lamberti, & Ugur, 2019). In addition, in recent studies, Total Potential Optimization (S. Ç.…”

Section: Weight Optimization Of Plane Trussesmentioning

confidence: 99%

A generative design-to-BIM workflow for minimum weight plane truss design

Malgit¹,

Işıkdağ

Bekdaş

et al. 2022

rdlc

View full text Add to dashboard Cite

Structural design has a significant impact on the overall cost of truss structures. In order to reduce the cost of a structure it is important to support designers' decision making starting from the early design phase. In this study, an optimization workflow is proposed, developed and implemented using well known generative design and Building Information Modeling (BIM) tools to achieve a cost-optimal design of a truss structure in the early design phase. Generative design aims to develop products that are lighter, stronger, more efficient and tailored to the specific needs of end users. Generative design tools allow users to create efficient designs via optimizing factors such as cost, weight, energy efficiency and performance. The aim of this study was to develop an optimization workflow to find and model the minimum weight / minimum cost design alternative in the early design phase using generative design, structural analysis and BIM tools in an integrated manner. The goal for the optimization was determined as finding the minimum weight (thus minimum cost) structure among the generated design alternatives. The single span steel truss was selected as the structure to be optimized, and optimization scenarios were prepared and implemented to determine the structural components of the truss with minimum weight. The results demonstrated that through integrated use of structural analysis, generative design and BIM tools minimum-weight truss design can be realized easily and practically.

show abstract

Section: Weight Optimization Of Plane Trussesmentioning

confidence: 99%

A generative design-to-BIM workflow for minimum weight plane truss design

Malgit¹,

Işıkdağ

Bekdaş

et al. 2022

rdlc

View full text Add to dashboard Cite

show abstract

“…Ramkumar. P. et al [13]used differential evolution algorithm to optimize the plane truss member cross-section area, D. Neeraja et al [14] along with size; shape and topology optimization was implemented through MATLAB on plane truss using GA, and H Assimi et.al. [15] extended size and topology optimization of planar truss using GA to more benchmark problem and did a comparison.…”

Section: Genetic Algorithm Parameter Effect On 3d Truss Optimization mentioning

confidence: 99%

Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

Kumar¹,

Rangavittal²

2018

Adv. J. Grad. Res.

View full text Add to dashboard Cite

The Genetic Algorithm is one of the advanced optimization techniques frequently used for solving complex problems in the research field, and there are plenty of parameters which affect the outcome of the GA. In this study, a 25-bar truss with the nonlinear constraint is chosen with the objective to minimize the mass and variables being the discrete area. For the same, GA parameter like Selection Function, Population Size, Crossover Function, and Creation Function are varied to find the best combination with minimum function evaluation. It is found that the Uniform selection gives the best result irrespective of the creation function, population size or crossover functions. But this is at the cost of a large number of function evaluations, and the other selection function fails to reach the global optimum and has a smaller number of function evaluation count. If the analysis of selection function is done one at a time, it is seen that all Cases performs better in Roulette but, Case A which is non-integer type with 200 population size being computationally cheaper than Case B and C of population size 300. In the Tournament selection, Case A, B with smaller population size and Case C with higher population size performs better. Case C performs better at Remainder selection with smaller population size, and Case A and B for Stochastic Uniform with higher population size. And, it is clear that the function evaluation count increases with the population size in every Case from this study.

show abstract

“…Weight optimization of plane trusses has been achieved using genetic algorithms (Neeraja et al, 2017). Moreover, advanced optimization techniques, such as genetic algorithms, have been employed as auxiliary tools in structural design, leading to a considerable reduction in the volume of concrete used in foundations (Ede et al, 2018) (Lopes et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Steel Truss Using Genetic Algorithm

Mahmood,

Yousif,

Issa

2024

PJBAS

View full text Add to dashboard Cite

In this paper, an optimization study is presented, focusing on steel trusses. The main goal of this study is to reduce the weight of truss structures using a Genetic Algorithm (GA), which is a widely acknowledged evolutionary-based method known for its efficiency in solving intricate optimization problems. The design problem formulation takes into account various constraints, such as displacement, tensile stress, and minimum size requirements. These constraints are implemented in MATLAB, utilizing the ANSI/AISC 360-16 Specification as a guideline for designing tension and compression members. To determine the optimal design, the approach involves considering discrete design variables. This is achieved by selecting sections from a database containing all available steel sections specified in the AISC Steel Construction Manual, ensuring practical and feasible design solutions. The efficiency of the algorithm is validated through its application to several plane truss types. Through a comparison of the outcomes obtained from the proposed algorithm with the results generated by CSI-ETABS software, it is demonstrated that this approach consistently yields better weight optimization. Overall, the study showcases the effectiveness of the GA-based algorithm in optimizing the weight of steel trusses. The results and implications of the findings are thoroughly discussed in the paper; this study has the potential to make a substantial contribution to the field of structural optimization and design.

show abstract

Weight optimization of plane truss using genetic algorithm

Cited by 9 publications

References 5 publications

A generative design-to-BIM workflow for minimum weight plane truss design

A generative design-to-BIM workflow for minimum weight plane truss design

Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

Optimization of Steel Truss Using Genetic Algorithm

Contact Info

Product

Resources

About