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Optimum design of a composite floor system considering environmental and economic impacts

Otimização de sistema de piso misto de aço e concreto considerando o impacto ambiental e econômico

Abstract

The composite floor system, composed of steel deck and concrete slab, generates more efficient and economical structures. On the other hand, the design of this type of structure has a high complexity level due to the consideration of several variables. In this respect, the objective of this paper is to present the formulation of the optimization problem for a composite floor system (steel and concrete) considering such environmental as economic impacts. To formulate the optimization problem, the reduction of environmental impact was adopted as an objective function - assuming the CO2 emission and the finance cost as parameters. The restrictions were taken by the limiting states imposed in standard NBR 8800:2008. The computer program was developed via Matlab R2016a and the optimization process was carried out using the Genetic Algorithm toolbox existing in this platform. Two application examples of the formulation at hand are presented: the first from the literature and the second from an existing building - in both situations the influences of different concrete compressive characteristic strengths were analyzed. The results of the optimization problem show a reduction in geometry and, consequently, in its weight. The solution found by the program reduces by up to 17.70% of CO2 emissions and 17.47% of the finance cost. When was applying different concrete compressive strengths, the optimal solution for environmental impact did not get the lowest cost. In general, the steel deck formwork obtained the highest percentage of environmental impact, while the beams and girders, with the same shape configuration, had the highest finance cost. Therefore, it is shown that the optimal design solution to CO2 emissions is not always the better solution for the finance cost.

Keywords:
steel-concrete composite floor system; cost and environmental impact; genetic algorithm


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