Low-carbon design optimization of reinforced concrete building structures using genetic algorithm

Embodied carbon emissions are getting increased attention to realize low-carbon buildings. Low-carbon designs have been explored by conducting both member-level and structure-level analyses for concrete structures. However, methods for combining two-tiered research need to be further developed to reduce the cost of design optimization. This study aims to propose a hybrid iterative approach for the low-carbon optimization of concrete framed structures. Accordingly, the integrated structural analysis was applied to design the initial scheme, subproject-based emission assessment was applied to identify the carbon-intensive structural components, and a genetic algorithm was applied to optimize the desired components subjected to relevant design constraints. A case study was made on a residential building to verify the feasibility of the proposed approach. The optimization results indicated that a potential 18.3% and 4.2% reduction in the embodied emissions from the beams and the building main body were achieved, respectively. A further discussion also proposed that an optimized scheme for interior walls using lightweight panels resulted in a 12% reduction in carbon emissions compared to the initial scheme using masonry walls. The proposed method and results can facilitate understanding the low-carbon design optimization of reinforced concrete structures and, therefore, contribute to carbon reduction of the construction industry.