Optimising cost and carbon emissions in the conceptual building design with genetic algorithms

Book chapter, 2025

To reduce the climate impact of construction projects, it is crucial to understand how structural choices in the conceptual design phase influence both cost and carbon emissions. However, incorporating carbon calculations in the conceptual design introduces an additional optimisation objective, increasing the complexity of the design process and requiring a comprehensive evaluation method. Therefore, this study aims to leverage genetic algorithms, an artificial intelligence technique inspired by evolutionary strategies, to opti-mise cost and carbon emissions for apartment buildings. First, building part databases with associated struc-tural resistance, carbon emissions, costs, fire resistance and sound reduction were created. Then, the NSGA-II algorithm was applied to balance cost and carbon trade-offs for a reference building. The results show that the proposed genetic algorithm-based method provides diverse solutions quickly and supports data-informed decision-making. For the reference building, the results indicate that a 9% cost increase can yield a 35.