Truss Structure Optimization for Two Design Variable Elements Using Genetic Algorithms with Stress and Failure Probability Constraints SUHARJANTO (original) (raw)

Optimization of Steel Truss Using Genetic Algorithm

Passer journal of basic and applied sciences, 2024

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.

Discrete Optimization of Truss Structure Using Genetic Algorithm

2014

During the last few years, several methods have been developed for the optimal design of structures. However, most of them, because of their calculus-based nature, treat the search space of the problem as continuous, when it is really discrete. Sometimes this leads to unrealistic solutions and therefore, they are not used in practice, which still prefers to rely on the more traditional iterative methods. This paper describes and uses genetic algorithm approach which explains that in a certain community, only the best organisms survive from all the adverse effects, i.e.,“ survival of the fittest ”. This paper describes the use of genetic algorithm (GA) in performing optimization of 2D truss structures to achieve minimum weight. The GA uses fixed length vector of the design variables which are the cross-sectional areas of the members. The objective considered here is minimizing the weight of the structure. The constraints in this problem are the stress and deflection in no member of t...

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min±max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-os and that the genetic algorithm can be used as a reliable numerical optimization tool. 7

Optimization of Plane and Space Trusses Using Genetic Algorithms

2014

weight optimization of trusses is so important due to economic and sustainability considerations. Geometry, topology and sizing optimization is extensively found in literature. Applications found in literature uses the traditional deign variables containing node coordinates, elements connectivity and member cross sections. This paper presents an approach based on the genetic algorithm for optimum design of plane and space trusses subjected to specified set of constraints. The proposed approach defined innovative design variables in terms of node coordinates and displacements. Such limited design variables lead to the reduction of genotype length resulting in less execution time. Topology and cross sections are estimated after using strength criteria. The proposed approach was applied on benchmark problems repeated in literature, the proposed approach resulted in more optimized results with less mathematical effort.

Weight optimization of truss structures by using genetic algorithms

Rakenteiden Mekaniikka, 2022

Lightweight structures, especially trusses, have attracted a tremendous attention due to their extensive applications in the construction of infrastructures. Optimizing the shape and crosssectional topology of truss members is essential since the truss systems are widely used in engineering routines. These systems form the framework of structures like bridges, steel halls for industry and trade, and towers. For the scope of this research, genetic algorithms (GAs) were used for weight optimization of truss structures. This paper aims to optimize truss structures for finding optimal crosssectional area. To optimize the cross-sectional area, all members were selected as design variables, with the structure's weight being the objective function. The restrictions related to the change of the location of the nodes and the tension in the members were the looked-upon problems, the permissible values of which were determined under the circumstances of the problem. In addition, the resulting optimized model which masses for sizing, shape, and topology or their combinations, were compared.

Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm

IPTEK The Journal for Technology and Science, 2011

During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA) into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on "survival of the fittest" strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing-configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing-configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally) and these results are very competitive compared to those obtained from other methods of truss optimization.

Sizing and Topology Optimization of Trusses Using Genetic Algorithm

Materials

Genetic algorithms are a robust method for a solution of wide variety optimization problems. It explores a big space of design variables in order to find the best solution. From the point of view of a user, the algorithm requires the encoding of design variables into the form of strings and the procedure of optimization uses them for optimization. Here, for the structural engineer, it is crucial to find the form of objective function including the constraints of the task and also to avoid critical states during the solution of structural responses. This paper presents the use of genetic algorithm for solving truss structures. The use of genetic algorithm approach is shown on three cases of truss structures.

Weight Optimization of Square Hollow Steel Trusses Using Genetic Algorithm

IOP Conference Series: Materials Science and Engineering

Conceptual design in structural engineering entails a large amount of trial and errors or extensive expertise to obtain the most economical and functional design solutions for large engineering projects. In this paper a modern optimization technique called Genetic algorithm, adopting its concept from genetic evolution is used to optimize the shape, size and topology of a plane truss structure with the aim of minimizing the total weight of the truss. A genetic algorithm developed in MATLAB was implemented in this paper to optimize the weight of plane truss structures. The objective function of the optimization problem is subjected to constraints such as stress limits, buckling constraints, tension and compression capacity according to British steel design code BS 5950. The plane trusses which were subject to point loads were tested in the genetic algorithm, the resulting optimized truss structures were then subject to real life loading to determine their feasibility to withstand real life loading. The optimized trusses presented by the algorithm were modelled in a structural analysis and design software called SAP 2000, where they were subjected to dead and live loads. After design the weight saving discovered between the original trusses and the optimized version was between 37-47%. The results show that the genetic algorithm implemented in this study is useful in optimizing the weight of a plane truss structure.

Weight optimization of large span steel truss structures with genetic algorithm

2015

A genetic algorithm for first principles global structure optimization of supported nano structures Abstract. The paper presents the weight optimization process of the main steel truss that supports the Slatina Sport Hall roof. The structure was loaded with self-weight, dead loads, live loads, snow, wind and temperature, grouped in eleven load cases. The optimization of the structure was made using genetic algorithms implemented in a Matlab code. A total number of four different cases were taken into consideration when trying to determine the lowest weight of the structure, depending on the types of connections with the concrete structure ( types of supports, bearing modes), and the possibility of the lower truss chord nodes to change their vertical position. A number of restrictions for tension, maximum displacement and buckling were enforced on the elements, and the cross sections are chosen by the program from a user data base. The results in each of the four cases were analyzed in terms of weight, element tension, element section and displacement. The paper presents the optimization process and the conclusions drawn.

Structural Analysis of a Genetic Algorithm Optimized Steel Truss Structure According to BS 5950

2018

A modern technique in structural optimization known as genetic algorithm was implemented in this paper to optimize a plane steel truss structure under point loadings and is subject to stress and displacement and buckling constraints. The genetic algorithm was developed in the MATLAB software. The genetic algorithm was run thrice on the plane truss structure and the run with the best result was picked as the final optimized truss structure. For each run a minimum of 500 initial population was set. The optimized truss structure gotten from the algorithm were analyzed and designed under dead and imposed loadings to compare and determine the percentage weight reduction and check the feasibility of the optimized truss structure. The software used to analyze and design according to British standard for steel design, BS 5950 was the SAP 2000 software. The results of the analysis and design in the SAP 2000 software showed the feasibility of the optimized truss as it passed all stress and di...