Otimização via algoritmos meta-heurísticos de perfis de aço U enrijecidos formados a frio submetidos à compressão axial

Abstract

This study assumes that the usual geometry of cold formed steel (CFS) can be improved by computational processes to increase their capacity, leading to more efficient and economical systems. This dissertation aims to provide a methodology that allows the development of lipped channel columns with maximum capacity for practical applications. The algorithms developed in this work match the geometric requirements suggested by NBR 14762, practical and fabrication restrictions suggested by researches. The compressive strength of the CFS was determined by the Direct Strength Method (DSM) adopted in the Brazilian standard and the critical buckling loads required by the procedure that was calculated by the Finite Strip Method (FSM) and by Machine Learning (ML) techniques. The performance of four optimization processes based on different meta-heuristic algorithms were compared. Five lipped channel profiles (U) available in the manufacturers catalog have been taken as a reference in the optimization studies.