Optimum Design of Steel Space Frames via Bat Inspired Algorithm (original) (raw)
Related papers
2013
Steel buildings are preferred in residential as well as commercial buildings due to their high strength and ductility particularly in regions where earthquakes mostly happen. In the past, steel buildings were designed by using trial and error strategy or designer experience. However, these strategies were not economical. After improvements in computer technology, many optimization methods have been widely used in structural design problems to obtain economical solutions while satisfying design requirements. Traditional optimization methods are inadequate to find a satisfactory solution to structural optimization problems due to complicated nature and discrete design variables of these problems. Metaheuristic techniques have become efficient tools for structural optimization problems since their emergence. These techniques try to improve the solution by using certain strategies that are generally inspired by natural phenomena. Genetic algorithms, evolutionary strategies, simulating a...
Bat inspired algorithm for discrete size optimization of steel frames
Bat inspired (BI) algorithm is a recently developed metaheuristic optimization technique inspired by echolocation behavior of bats. In this study, the BI algorithm is examined in the context of discrete size optimization of steel frames designed for minimum weight. In the optimum design problem frame members are selected from available set of steel sections for producing practically acceptable designs subject to strength and displacement provisions of American Institute of Steel Construction-Allowable Stress Design (AISC-ASD) specification. The performance of the technique is quantified using three real-size large steel frames under actual load and design considerations. The results obtained provide a sufficient evidence for successful performance of the BI algorithm in comparison to other metaheuristics employed in structural optimization.
Optimum Design of Reinforced Concrete Frames Using Bat Meta-Heuristic Algorithm
2013
The main aim of the present study is to achieve optimum design of reinforced concrete (RC) plane moment frames using bat algorithm (BA) which is a newly developed meta-heuristic optimization algorithm based on the echolocation behaviour of bats. The objective function is the total cost of the frame and the design constraints are checked during the optimization process based on ACI 318-08 code. Design variables are the cross-sectional assignments of the structural members and are selected from a data set containing a finite number of sectional properties of beams and columns in a practical range. Three design examples including four, eight and twelve story RC frames are presented and the results are compared with those of other algorithms. The numerical results demonstrate the superiority of the BA to the other meta-heuristic algorithms in terms of the frame optimal cost and the convergence rate.
Civil Engineering Dimension
Determining the topology, layout, and size of structural elements is one of the most important aspects in designing steel seismic-resistant structures. Optimization of these parameters is beneficial to find the lightest weight of the structure, thus reducing construction cost. This study compares the performance of three metaheuristic algorithms, namely, Particle Swarm Optimization (PSO), Symbiotic Organisms Search (SOS), and Differential Evolution (DE). Three study cases are used in order to find the lightest structural weight without violating constraints based on SNI 1726:2019, SNI 1729:2020, and SNI 7860:2020. The results of this study show that SOS has better performance than other algorithms.
Iran University of Science & Technology, 2012
Different kinds of meta-heuristi c algorithms have been recently utilized to overcome the complex nature of optimum design of structures. In this paper, an integrated optimization procedure with the objective of minimizing the self-weight of real size structures is simply performed interfacing SAP2000 and MATLAB ® softwares in the form of parallel computing. The meta-heuristic algorithm chosen here is Cuckoo Search (CS) recently developed as a type of population based algorithm inspired by the behavior of some Cuckoo species in combination with the Levy flight behavior. The CS algorithm performs suitable selection of sections from the American Institute of Steel Construction (AISC) wide-flange (W) shapes list. Strength constraints of the AISC load and resistance factor design specification, geometric limitations and displacement constraints are imposed on frames. Effective time-saving procedure using simple parallel computing, as well as utilizing reliable analysis and design tool a...
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 material at a site. In this study five optimum 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.
A Comparative Study of Three Metaheuristics for Optimum Design of Engineering Structures
A comparative study is carried out on the optimum design of a real-size steel frame by considering three different metaheuristic search techniques. The techniques are selected as the Firefly Algorithm (FFA), Artificial Bee Colony (ABC), and Cuckoo Search (CS) algorithms. Metaheuristic search techniques of optimization are non-deterministic methods and they rely on heuristics in finding the better solutions in the search space. They use random or probabilistic parameters, while they search for the optimum solution, rather than deterministic quantities. The source of random variables may be several depending on the nature and the type of problem. The heuristics behind these innovative techniques is borrowed from the nature or physics. In the design example considered, the design constraints include the displacement limitations, inter-story drift restrictions, strength requirements for beams and beam-columns which are formulated according to provisions of LRFD-AISC (Load and Resistance Factor Design of American Institute of Steel Institution).