Ibrahim Aydogdu

Ant colony optimization of irregular steel frames including elemental warping effect

The effect of warping in the design of steel space frames having members of thin walled steel sections is significant. In this paper the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction) in which the effect of warping is also taken into account. Ant colony optimization technique is used to obtain the solution of the design problem. A number of space frame examples are designed by the algorithm developed in order to demonstrate the effect of warping in the optimum design.

Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution

Abstract Optimum design of real world steel space frames under design code provisions is a complicated optimization problem due to the presence of large numbers of highly nonlinear constraints and discrete design variables. The use of gradient based optimization techniques in finding the optimum solution of such large design problems is cumbersome due to the selection of initial design points and convergence difficulties while metaheuristic algorithms do not suffer such problems. Artificial bee colony (ABC) algorithm is one of the recent additions to the swarm intelligence based meta-heuristic search techniques that mimic natural foraging behavior of honey bees. In this study optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC and its solution is obtained by using enhanced artificial bee colony algorithm. The performance of artificial bee colony algorithm is improved by adding Levy flight distribution in the search of scout bees. Real world steel space frames are designed with the new algorithm developed in this study to demonstrate its robustness and efficiency.

Harmony Search Algorithms in Structural Engineering

Harmony search method is widely applied in structural design optimization since its emergence. These applications have shown that harmony search algorithm is robust, effective and reliable optimization method. Within recent years several enhancements are suggested to improve the performance of the algorithm. Among these Mahdavi has presented two versions of harmony search methods. He named these as improved harmony search method and global best harmony search method. Saka and Hasancebi (2009) have suggested adaptive harmony search where the harmony search parameters are adjusted automatically during design iterations. Coelho has proposed improved harmony search method. He suggested an expression for one of the parameters of standard harmony search method. In this chapter, the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Corporation). The weight of the steel frame is taken as the objective function to be minimized. Seven different structural optimization algorithms are developed each of which are based on one of the above mentioned versions of harmony search method. Three real size steel frames are designed using each of these algorithms. The optimum designs obtained by these techniques are compared and performance of each version is evaluated.

Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures

Sustainable construction aims at reducing the environmental impact of buildings on human health and natural environment by efficiently using energy, resources and reducing waste and pollution. Building construction has the capacity to make a major contribution to a more sustainable future of our World because this industry is one of the largest contributors to global warming. The use of cold-formed steel framing in construction industry provides sustainable construction which requires less material to carry the same load compare to other materials and reduces amount of waste mimum design algorithms are developed for cold-formed steel frames made of thin-walled sections using the recent metaheuristic techniques. The algorithms considered are firefly, cuckoo search, artificial bee colony with levy flight, biogeography-based optimization and teaching-learning-based optimization algorithms. The design algorithms select the cold-formed thin-walled C-sections listed in AISI-LRFD (American Iron and Steel Institution, Load and Resistance Factor Design) in such a way that the design constraints specified by the code are satisfied and the weight of the steel frame is the minimum. A real size cold-formed steel building is optimized by using each of these algorithms and their performance in attaining the optimum designs is compared.

Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights

ABSTRACT In this article, a new version of a biogeography-based optimization algorithm with Levy flight distribution (LFBBO) is introduced and used for the optimum design of reinforced concrete cantilever retaining walls under seismic loading. The cost of the wall is taken as an objective function, which is minimized under the constraints implemented by the American Concrete Institute (ACI 318-05) design code and geometric limitations. The influence of peak ground acceleration (PGA) on optimal cost is also investigated. The solution of the problem is attained by the LFBBO algorithm, which is developed by adding Levy flight distribution to the mutation part of the biogeography-based optimization (BBO) algorithm. Five design examples, of which two are used in literature studies, are optimized in the study. The results are compared to test the performance of the LFBBO and BBO algorithms, to determine the influence of the seismic load and PGA on the optimal cost of the wall.

Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization

Swarm intelligence refers to collective intelligence. Biologists and natural scientist have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. In spite of the fact that these insects are unsophisticated individually, they make wonders as a swarm by interaction with each other and their environment. In last two decades, the behaviors of various swarms that are used in finding preys or mating are simulated into a numerical optimization technique. In this chapter, eight different swarm intelligence–based algorithms are summarized and their working steps are listed. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. Two optimization problems taken from the literature are solved by all these eight algorithms and their performance are compared. It is noticed that most of the swarm intelligence–based algorithms are simple and robust techniques that determine the optimum solution of optimization problems efficiently without requiring much of a mathematical struggling.

Use of Swarm Intelligence in Structural Steel Design Optimization

In this chapter, the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Corporation). The weight of the steel frame is taken as the objective function to be minimized. The design optimization problem necessitates selection of steel sections for the members of the steel frame from the available steel profiles lists. This turns the design optimization problem into discrete programming problem. Obtaining the optimum solution of such programming problems is cumbersome with mathematical programming techniques. On the other hand with the use of recently developed metaheuristic techniques that are based on swarm intelligence, the solution of the same problem becomes straightforward. Five different structural optimization algorithms are developed which are based on ant colony optimization, particle swarm optimizer, artificial bee colony algorithm, firefly algorithm, and cuckoo search algorithm, respectively. Two real size steel space frames; one rigidly connected and the other pin jointed are designed using each of these algorithms. The optimum designs obtained by these techniques are compared and performance of each version is evaluated. It is noticed that most of swarm intelligence-based algorithms are simple and robust techniques that determine the optimum solution of structural design optimization problems efficiently without requiring much of a mathematical struggle.

Teaching and Learning-Based Optimization Algorithm for Optimum Design of Steel Buildings

In this study, an algorithm is developed for the optimum design of the steel buildings. The optimum design problem is formulated according to LRFD-AISC. Design constraints include the displacement limitations, inter-story drift restrictions of multi-story frames, strength requirements for beams and beam-columns and geometric constraints. Teaching and learning based optimization (TLBO) technique, inspired by the interaction and outcome of teacher and learners, is employed to determine its optimum solution. The design algorithm developed selects optimum W sections for beams and columns of three dimensional steel frames so that aforementioned constraints are satisfied and the frame has the minimum weight. A large scale steel building has been designed by TLBO algorithm developed in order to test performance of the algorithm.

Optimum design of purlin systems used in steel roofs

In this study, one existing purlin system which is used in steel roof is optimized by taking into account less cost and bearing maximum load via developed software. This software runs with firefly algorithm which is one of the recent stochastic search techniques. One of the metaheuristic techniques, so-called firefly algorithm imitates behaviors of natural phenomena. Behaviors and communications of firefly are inspired by this algorithm. In optimization algorithm, steel sections, distance between purlins, tensional diagonal braces are determined as design variables. Design loads are taken into account by considering TS498-1997 (Turkish Code) in point of place where structure will be built, outside factors and used materials. Profile list in TS910 is used in selection stage of cross sections of profile. Constraints of optimization are identified in accordance with bending stress, deformation and shear stress in TS648. Design variables of optimization are selected as discrete variables so as to obtain applicable results. Developed software is tested on existing real sample so; it is evaluated with regard to design and performance of algorithm. A R T I C L E I N F O

Utilization of Harmony Search Algorithm in Optimal Structural Design of Cold-Formed Steel Structures

The most important concern for structural design engineers is, nowadays, how to design and build a structure which is really sustainable. The course to design and construct of buildings has to be urgently changed if the overall carbon dioxide emission would like to be reduced. Otherwise, the increase in global warming arising out of building construction will continue in a great majority. The application of cold-formed steel skeleton frames increasingly in building trade makes possible sustainable structures. In this study a harmony search algorithm (HSA) and an improved version, called as adaptive harmony search (AHSA) algorithm to obtain optimum design of cold-formed steel frames. These algorithms choose the cold-formed thin-walled C-sections treated as design variables from a list in AISI-LRFD (American Iron and Steel Institution, Load and Resistance Factor Design). This selection minimize the weight of the cold-formed steel frame while the design constraints specified by the code are satisfied.