Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures (original) (raw)
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The construction industry is one of the largest contributors to global warming. This indicates the importance of adopting sustainable buildings in order to accommodate the urban population increasing by one million people every week. 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. The use of cold-formed steel frames by the construction industry provides sustainable construction which requires less material to carry the same load compared to other materials and reduces the amount of waste material at a site. In this study an optimum design algorithm is developed for cold-formed steel frames made out of thin-walled sections. The design algorithm selects the coldformed thin-walled C-sections listed in AISI-LRFD in such a way that the design constraints specified by the code are satisfied and that the weight of the steel frame is a minimum. This is achieved by making use of the artificial bee colony algorithm which is a recent addition to metaheuristic algorithms. Two cold-formed steel frames are designed by the algorithm presented in order to demonstrate its efficiency.
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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.
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).
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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...
Optimum Design of Steel Space Frames via Bat Inspired Algorithm
Design optimization of steel space frames is a very popular topic in structural engineering due to economy saved in cost of the structures by optimization process. Although the final cost of a steel frame is affected by many factors, such as material, manufacturing, erection and transportation costs, the material cost of steel comprises a great deal of overall cost of the structure. Hence, the design optimization of steel frames is focused on weight minimization in the literature based on the assumption that the use of least material leads to an economical design as well in terms of final cost of a structure. This study focuses on design optimization of steel space frames that are sized for minimum weight subject to stress, stability and nodal displacement and drift constraints according to Allowable Stress Design-American Institute of Steel Construction (ASD-AISC) specification. Bat inspired optimization (BIO) algorithm, which is a recently developed metaheuristic technique that exploits echolocation behavior of bats in searching a design space, is employed to deal with the optimization problem at hand. It is shown that BIO produces improved results with respect to other methods of metaheuristics.
Structural Optimization of Cold-Formed Steel Frames to AISI-LRFD
The construction industry, from harvesting raw materials, transport, manufacturing, to the actual construction of buildings, has a significant and negative impact on the environment. The construction of buildings not only produces more waste, but also requires more transport and electricity (emissions), and often results in landscape damage, ecological disruption, habitat destruction, and/or deforestation. Utilizing cold-formed steel frame systems in construction supplies sustainability since this kind of framing are made out of thin-walled sections. Nowadays, there are a variety of metaheuristics developed for minimum weight design of cold-formed steel frames.In this study, the biogeography-based optimization (BBO) algorithm is used to 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 cold-formed steel frame is the minimum. It is shown that BBO algorithm out performs other metaheuristic technique in the design example considered.
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.
Sustainable development in construction industry is emerging as a major issue among cities and communities in the current century. As global climate change becomes an increasingly serious concern for the future and construction industry dependence on fossil fuels for energy creates greater adverse influence on human health and natural environment, an interest in high-efficient, low environmental impact buildings has begun to transform the notion of building design, construction, and operation. As it stands, the most of the standard buildings in the world consume an extraordinary amount of resources while taking an enormous toll on the environment. The utilization of cold-formed thin-walled open steel sections in structural sites supplies green structural opportunities demanding less material and cost while providing high strength. The developed algorithm for this study obtains the optimum geometric dimensions of cold-formed thin-walled open steel sections under various external loading. Moreover, this design algorithm takes into account of the effect of geometric nonlinearity as well as effect of warping. Also the displacement and stress constraints are included in the formulation of the design problem. The optimum design problem obtained turn out to be mixed integer and discrete programming problem. Artificial Bee Colony (ABC) algorithm is used to obtain its solution. This technique is a recent numerical optimization technique which mimics the intelligent behavior of honey bee swarm. The recent studies with the ABC method have shown its effectiveness and robustness in finding the optimum solution of combinatorial optimization problems. A design example is included to demonstrate the efficiency of the optimum design algorithm developed.