Analysis, design and optimization of structures using force method and genetic algorithm (original) (raw)

Nonlinear analysis and optimal design of structures via force method and genetic algorithm

Computers & Structures, 2006

In this paper analysis, design and optimization of structures are performed considering material and geometric nonlinearity. For this purpose, the force method, energy concepts and genetic algorithm are employed. The first part of this paper contains the formulation of the problem based on the force method and energy principles using linear analysis. In this method, the material nonlinearity is also included. The formulations are examined by simple illustrative examples. Reduction of the complementary energy is efficiently incorporated in this approach. The second part of the article combines the process of the analysis and design to achieve specified stress ratios for the members of the structure. This problem is especially important in the seismic deign of structures. Geometric nonlinearity is then formulated, in the third part by employing two approaches. Considering the energy term next to the weight of the structure, optimal dimensions of the structures are selected. In each part, the efficiency of the methods is illustrated by means simple examples.

Genetic algorithm for discrete-sizing optimal design of trusses using the force method

International Journal for Numerical Methods in Engineering, 2002

In the process of discrete-sizing optimal design of truss structures by Genetic Algorithm (GA), analysis should be performed several times. In this article, the force method is employed for the analysis. The advantage of using this method lies in the fact that the matrices corresponding to particular and complementary solutions are formed independently of the mechanical properties of members. These matrices are used several times in the process of the sequential analyses, increasing the speed of optimization. The second feature of the present method is the automatic nature of the prediction of the useful range of sections for a member from a list of profiles with a large number of cross-sections. The third feature consists of a contraction process developed to increase the efficiency of the GA by which an optimal design for the first sub-string associated with member cross-sections is obtained. Improved designs are achieved in subsequent cycles by reducing the length of sub-strings. Copyright © 2002 John Wiley & Sons, Ltd.

AN OPTIMUM STRUCTURAL COMPUTER-AIDED DESIGN USING HYBRID GENETIC ALGORITHM

Proceeding of the International Conference “Progress in Steel, Composite and Aluminium Structures”, 2006

This paper presents a computer-aided technique for design and optimization of metal structures. The optimum design problem is formulated as the non-linear programming task. The body of mathematics combines finite element method for linear static structural analysis and hybrid genetic algorithm with update gradient method for non-linear task solution. Proposed technique is realized with elaborated software oriented to solve wide range problems for different types of metal structures. Numerical examples demonstrate the effectiveness of the proposed optimization methodology.

Study of evolutionary structural optimization and applications

The fully stressed design (FSD) concept was developed to resolve the unproductiveness in structural optimization. In FSD case, all of the members of the structure are exposed to the allowed maximum or minimum limits. The FSD is achieved when the stress in all members become approximately equal to each other. The FSD case is also named as equal stress case in

Optimum design of structures by an improved genetic algorithm using neural networks

Advances in Engineering Software, 2005

Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.

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...

Design of structures for optimal static strength using ESO

Engineering Failure Analysis, 2005

This paper presents a modified evolutionary structural optimisation (ESO) algorithm for optimal design of damage tolerant structures. The proposed ESO algorithm uses fracture strength as the design objective. The formulation outlined here can be used for shape optimisation of structures and allows for cracks to be located along the entire structural boundary. In this work we use an approximate method for evaluating the stress intensity factors associated with the cracks. This extended ESO algorithm is illustrated using the problem of the optimal shape design of a 'cutout' in a rectangular plate under biaxial loading and the design of a shoulder fillet under uniaxial tension. It is found that this method reduced the maximum stress intensity factor for the optimum shape and produced a near uniform level of fracture criticality around the boundary. It is also shown that the shapes optimised for stress and fracture strength may differ. This highlights the need to explicitly include fracture parameters in the design objective function. The results agreed well with those reported in the literature using a biological method.

Application of Evolutionary Optimization in Structural Engineering

IFIP Advances in Information and Communication Technology, 2009

Practical optimization methods including genetic algorithms are introduced, based on evolutionary computing or soft computing. Several application examples are presented to demonstrate and discuss the efficiency and applicability of the described methods.

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.

A Modern Methodology of Design of Three-Dimensional Structures by a Genetic Algorithms Approach

2016

The computer aided design is realized today by the significant development of computational tools. These computer codes are often intended for advanced design phase of projects. However, there is to our knowledge very few design support tools in preliminary design phase. Indeed, in the life cycle of a construction project, the design phase is often the place of conflicting situations that prevent the overall optimization of the said projects production costs. During this phase, various technical treatments should be held to verify the feasibility of the works in relation to the structural constraints, neighborhood, implementation, etc. In this work, we propose a formulation of the optimization problem of the overall design of a simple metallic structure and a methodology of resolution based on the approach of Genetic Algorithms. The aim is to minimize the overall execution cost.