Abstract
Abstract The objective of this study is to present the formulation and applications of the optimization problem of steel-concrete composite alveolar beams. In addition to presenting the formulation, a comparative analysis of the predominant collapse modes is performed numerically via the finite element method. The optimization program was developed with the GUI platform available in Matlab 2016. Since this is a discrete problem, a Genetic Algorithm (GA) was used to solve the optimization model, implemented with the Matlab optimization toolbox. Numerical examples using finite elements model are presented to validate the solution and analyze its effectiveness, along with an assesment of the predominant collapse modes for a group of 12 beams. The results show that more efficient designs are obtained when optimization tools are applied.
Highlights
Steel beams with uniformly distributed web openings, referred to as alveolar or castellated beams, have several constructive advantages
The collapse modes of the alveolar beams were used as restrictions for the implementation of the optimization routine
The objective of this study was to present the formulae for design optimization of steel-concrete composite alveolar beams and their applications, as well as the results of a numerical analysis, performed to demonstrate one of the predominant collapse modes in the optimization problem
Summary
Steel beams with uniformly distributed web openings, referred to as alveolar or castellated beams, have several constructive advantages. Alves and Lubke (2019) presented the optimization problem and an analysis of the predominant collapse modes for castellated steel-concrete composite beams and their different failure patterns. Min and Lee (2001) used genetic algorithms to conduct an optimization study on the life cycle cost of bridges with orthotropic steel deck systems consisting of steel slabs with ribbed formwork In this life cycle analysis, building and maintenance costs were considered, with adjustments in structural resistance, deflection and fatigue of the structure. Kociecki and Adeli (2015) and Prendes-Gero et al (2018) implemented GA for optimization of different steel structural systems in 2015 and 2018, respectively, and observed gains of around 10% when compared to non-optimized designs Both studies highlighted the ability to work with discrete variables when using GA as an important characteristic for obtaining good results. A software was developed on Matlab 2016 using the embedded GUI and optimization toolboxes
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