Design of nonlinear framed structures using genetic optimization


In this paper we present a genetic algorithm (GA)-based optimization procedure for the design of 2D, geometrical, nonlinear steel-framed structures. The approach presented uses GAs as a tool to achieve discrete nonlinear optimal or near-optimal designs. Frames are designed in accordance with the requirements of the AISC-LRFD specification. In this paper, we employ a group selection mechanism, discuss an improved adapting crossover operator, and provide recommendations on the penalty function selection. We compare the differences between optimized designs obtained by linear and geometrically nonlinear analyses. Through two examples, we will illustrate that the optimal designs are not affected significantly by the P-Δ effects. However, in some cases we may achieve a better design by performing nonlinear analysis instead of linear analysis.

Publication Title

Journal of structural engineering New York, N.Y.