基于低碳指标的混凝土框架柱截面优化设计

To investigate the method and influencing factors for low-carbon design of reinforced concrete (RC) members, the carbon emission and cost were taken as indicators, and sectional optimization design of RC frame columns was realized by a double-objective genetic algorithm based on discrete variables. Moreover, a parametric analysis was employed to explore the influences of material strength, axial compression ratio, eccentricity, and seismic grade on the carbon emissions of frame columns. The results indicate that high-strength longitudinal reinforcements show considerable low-carbon and economic benefits, whereas the concrete strength has little impact on the optimized carbon emissions, and higher strength concrete can be appropriately used to save costs. If the axial compression ratio is constant, the optimal results of carbon emission and cost increase with the eccentricity of axial force. And if the eccentricity is constant, low-carbon and economical designs can be achieved when the axial compression ratio is within the range of 0. 4 to 0. 6. Besides, the optimized carbon emissions and costs of the frame columns increase significantly with the increase of seismic grade.