Generative design for more economical and environmentally sustainable reinforced concrete structures

Abstract

With billion tons of carbon dioxide emitted by the construction and manufacturing sector every year, there is an urgent need for reducing construction materials consumption as the construction industry continues to grow. Current practices for designing structural elements, which are based on traditional design methods, result in building box-shaped elements and consuming more materials than what is structurally necessary. Excess materials can be removed without compromising the structural soundness of the elements while also providing added architectural value. This paper presents an approach based on generative design concepts, genetic algorithms, and the Lagrangian Multiplier Method to design concrete beams that use near-exact amounts of material only where they are needed. In this study, beam designs are optimized to reduce concrete and steel use in reinforced concrete beams. The effectiveness of the approach was demonstrated through its deployment to design a cantilever beam and a simply supported beam. The results of both applications were very promising as the reductions in material costs reached 63% and the related reductions in CO2 emissions reached 57% per beam. This lays strong foundations for expanding the effort to scale this approach into entire buildings. The optimized structures can be constructed using 3D concrete printing technology, which eliminates the barriers imposed by traditional form-based construction. The results of this study contribute to means for making construction more economical and environmentally sustainable.