A new approach is presented for the optimization of steel lattice towers by combining genetic algorithms and an object-oriented approach. The purpose of this approach is to eliminate the difficulties in the handling of large size problems such as lattice towers. Improved search and rapid convergence are obtained by considering the lattice tower as a set of small objects and combining these objects into a system. This is possible with serial cantilever structures such as lattice towers. A tower consists of panel objects, which can be classified as separate objects, as they possess an independent property as well as inherent properties. This can considerably reduce the design space of the problem and enhance the result. An optimization approach for the steel lattice tower problem using objects and genetic algorithms is presented here. The paper also describes the algorithm with practical design considerations used for this approach. To demonstrate the approach, a typical tower configuration with practical constraints has been considered for discrete optimization with the new approach and compared with the results of a normal approach in which the full tower is considered. ©ASCE.

Anand Rajaraman

Algorithms

Cantilever beams

Genetic algorithms

Object oriented programming

Topology

TOWERS

Trusses

LATTICE TOWERS

SPACE TRUSSES

STEEL LATTICE

STEEL STRUCTURE

Steel

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Object-Oriented Optimization Approach Using Genetic Algorithms for Lattice Towers

Journal of Computing in Civil Engineering

A number of authors have previously used genetic algorithms to optimize structural systems. This paper illustrates use of simple genetic algorithms for optimum design of large scale steel truss bridge superstructure for railway loadings. Object-Oriented Methodology is used to simplify' the design software development process. Optimal design of practical structures is illustrated, considering system topology, configuration and member sizes represented by mixed design variables (continuous and discrete). Complex design domain requirements conforming to Indian Railways design standard consisting of constraints corresponding to material strength, buckling strength as well as fatigue strength limit have been considered and the objective function to be minimized represents the total cost of the superstructure including cost of fabrication. The paper also demonstrates the efficient integration of genetic algorithms for optimization with the design software, both implemented in an object-oriented environment. Copyright ASCE 2006.

Proceedings of the Structures Congress and Exposition

Integrated system for steel truss bridge

Genetic algorithms have been shown to be very effective optimization tools for a number of engineering problems. Since the genetic processes typically operate independent of the actual problem, a core genetic algorithm library consisting of all the genetic operators having an interface to a generic objective function can serve as a very useful tool for learning as well as for solving a number of practical optimization problems. This paper discusses the object-oriented design and implementation of such a core library. Object-oriented design, apart from giving a more natural representation of information, also facilitates better memory management and code reusability. Next, it is shown how classes derived from the implemented libraries can be used for the practical size optimization of large space trusses, where several constructibility aspects have been incorporated to simulate real-world design constraints. Strategies are discussed to model the chromosome and to code genetic operators to handle such constraints. Strategies are also suggested for member grouping for reducing the problem size and implementing move-limit concepts for reducing the search space adaptively in a phased manner. The implemented libraries are tested on a number of large previously fabricated space trusses, and the results are compared with previously reported values. It is concluded that genetic algorithms implemented using efficient and flexible data structures can serve as a very useful tool in engineering design and optimization.

Object-oriented framework for genetic algorithms with application to space truss optimization

With increasing competition, the engineering industry is in need of optimization of designs that would lead to minimum cost or weight. Recent developments in Genetic Algorithms (GAs) makes it possible to model and obtain optimal solutions in structural design that can be put to use in industry. The main objective of this paper is to illustrate typical applications of GAs to practical design of structural systems such as steel trusses, towers, bridges, reinforced concrete frames, bridge decks, shells and layout planning of buildings. Hence, instead of details of GA process, which can be found in the reported literature, attention is focussed on the description of the various applications and the practical aspects that are considered in Genetic Modeling. The paper highlights scope and future directions for wide applications of GA based methodologies for optimal design in practice.

Structural Engineering and Mechanics

Structural optimization in practice: potential applications of genetic algorithms

Optimizing most structural systems used in practice requires considering design variables as discrete quantities. The paper presents a simple genetic algorithm for optimizing structural systems with discrete design variables. As genetic algorithms (GAs) are best suited for unconstrained optimization problems, it is necessary to transform the constrained problem into an unconstrained one. A penalty-based transformation method is used in the present work. The penalty parameter depends on the degree of constraint violation, which is found to be wellsuited for a parallel search using genetic algorithms. The concept of optimization using the genetic algorithm is presented in detail using a three-bar truss problem. All the computations for three successive generations are presented in the form of tables for easy understanding of the algorithm. Two standard problems from literature are solved and results compared. The application of the genetic algorithm to design optimization of a larger problem is illustrated using a 160-bar transmission tower. © ASCE.

Journal of Structural Engineering (United States)

Discrete optimization of structures using genetic algorithms

ULTIMATE STRENGTH OF COLD-FORMED STEEL BUILT-UP COLUMNS: EFFECT OF LACING SLENDERNESS

Choice Reviews Online

Genetic algorithms in search, optimization, and machine learning

Object-oriented optimization approach using genetic algorithms for lattice towers

Journal	Journal of Computing in Civil Engineering
ISSN	08873801
Open Access	No