Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Structural and Multidisciplinary Optimization
A genetic algorithm for design of moment-resisting steel frames
Structural and Multidisciplinary Optimization
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In this paper, an optimum design method is presented for non-linear steel frames with semi-rigid connections and semi-rigid column bases using a genetic algorithm. The design algorithm obtains the minimum total cost which comprises total member plus connection costs by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. A genetic algorithm is employed as optimization method which utilizes reproduction, crossover and mutation operators. Displacement and stress constraints of AISC-Load and Resistance Factor Design (LRFD) specification and also size constraints for beams and columns are imposed on the frame. The Frye and Morris polynomial model and also a linear spring model are used for semi-rigid connections and column bases respectively. Three design examples with various type of connections are presented. The designs obtained using AISC-LRFD code are compared to those where AISC-Allowable Stress Design (ASD) is considered. The comparisons show that the former code yields frames with less costs. Moreover, the semi-rigid connection and column base modelling is compared to rigid connection modelling.