Life Cycle Sustainable Design Optimization of Building Structural Components: A Hybrid Approach Incorporating Genetic Algorithm and Machine Learning

Optimization design is an effective strategy for reducing carbon emissions in building structures. Various exhaustive and metaheuristic methods have been proposed to optimize the carbon emissions of structural components, which has primarily focused on sustainable design during the construction phase. This study proposes a hybrid approach for the life cycle sustainable design of reinforced concrete components, encompassing the material production, construction, carbonization, and end-of-life phases. The resistance of structural components was evaluated through time-dependent reliability indices, and surrogate models were developed using machine learning techniques. The surrogate models were subsequently integrated into a dual-objective genetic algorithm for life cycle sustainable design. Based on the proposed approach, numerical examples including a singly reinforced beam and a biaxially eccentric compressed column were analyzed. The minimum carbon emissions were optimized to 486.2 kg CO2e and 307.8 kg CO2e, respectively, representing a reduction of more than 10% compared to the original design. Moreover, parametric and comparative analyses were conducted to identify the key factors influencing life cycle sustainable design. The findings underlined the impact of design methods, system boundaries, and specific design variables such as material strengths and concrete cover depth. Overall, this study enhances the efficiency and applicability of sustainable design for structural components while considering life cycle impacts.