Cold formed steel (CFS) has many advantages over other construction materials.
CFS members are lightweight. They weigh up to 30-35% less than their wood
counterparts.. This makes CFS members economical and the same time very easy to erect
and inst
all. They may be shaped (cold-bent) to nearly any open cross section. This allows
for the use of optimization technique’s to find optimal shapes for the members’ cross
sections.
The research aims to show the genetic algorithm's ability in determining the optimum
dimensions cold formed C section. To do so, the optimum design mathematical
formulation was formulated by adding the manufacturing constraints that reflect the
section folding operations in addition the geometrical and structural constraints.
The research found that the genetic algorithm is effective tool in finding the best
solution to this issue, as it showed its ability to deal with asymmetric section through
reaching solutions conform to the basic principles of mechanics of material.
The algorithm is adjustable, so that it can implement the design restrictions which are
compatible with any codes or any manufacturing requirements imposed by modulation
techniques.
Thin walled Steel products are very much used in the construction industry, where it
is cold formed from uniform thickness steel plates. This study aims at determining the
optimal section of cold formed thin walled lipped C compressed member under
the effect
of several levels of axial force using Genetic Algorithm.
The research found that the genetic algorithm is able to resolve the issue of the
optimal design of studied column with high efficiency, accuracy. Also it found that the
torsional flexural buckling constraint and the overall buckling constraint in x-direction are
the effective constraints in case of long height.
The study recommends restudying the same issue as a multi objective optimization
problem by adding additional objective functions which are the overall buckling in x&y
directions.
