CO2 Emission Optimization of Concrete-Filled Steel Tubular Rectangular Stub Columns Using Metaheuristic Algorithms

Abstract

Concrete-filled steel tubular (CFST) columns have been assiduously investigated experi-mentally and numerically due to the superior structural performance they exhibit. To obtain the best possible performance from CFST columns while reducing the environmental impact, the use of optimization algorithms is indispensable. Metaheuristic optimization techniques provide the de-signers of CFST members with a very efficient set of tools to obtain design combinations that perform well under external loading and have a low carbon footprint at the same time. That is why metaheuristic algorithms are more applicable in civil engineering due to their high efficiency. A large number of formulas for the prediction of the axial ultimate load-carrying capacity (Nu) of CFST columns are available in design codes. However, a limitation of the usage of these design formulas is that most of these formulas are only applicable for narrow ranges of design variables. In this study a newly developed set of equations with a wide range of applicability that calculates Nu in case of rectangular cross-sections is applied. In order to optimize the cross-sectional dimen-sions, two different metaheuristic algorithms are used, and their performances are compared. The reduction in CO2 emission is demonstrated as a function of cross-sectional dimensions while considering certain structural performance requirements. The outcome of the more recently developed social spider algorithm is compared to the outcome of the well-established harmony search tech-nique. The objective of optimization was to minimize CO2 emissions associated with the fabrication of CFST stub columns. The effects of varying the wall thickness as well as the concrete compressive strength on CO2 emissions are visualized by using two different optimization techniques. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.