Comparative study on recent metaheuristic algorithms in design optimization of cold-formed steel structures

Abstract

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 coldformed 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. © 2015 Springer International Publishing Switzerland.