Structural optimization can lead to efficient designs for high-rise concrete buildings. However, the high computational cost of the structural analysis step hinders its application in practical cases. With regards to this problem, a novel hybrid optimization framework is developed in this study. Discrete variables, code-stipulated constraints and other construction requirements under multiple loading scenarios are considered. The hybrid optimization framework contains two steps: in the first step, a computationally efficient approximation of the optimization formulation is constructed using a combined response surface methodology (RSM). Combining all the design constraints, three sub-optimization problems are proposed based on the relaxation model of the original problem to update the response surface model in the near-optimum region. When the response surface model has sufficient accuracy in the near-optimum region, in the second step, a discrete Particle Swarm Optimization (PSO) technique is applied to search for the optimal solution utilizing the response surface model. By integrating the commercially available ETABS, a dedicated optimization software with an independent interface is developed and details for practical software development are included in this paper. Three examples of high-rise concrete building design are presented to demonstrate the effectiveness of the proposed optimization framework, and the results are compared to those of a general PSO algorithm. It is shown that, compared to the conventional design approach, a 14.3% reduction in material usage is achieved by the hybrid optimization framework. Additionally, the proposed framework significantly improves the computational efficiency compared to PSO. This methodology can therefore be used by engineers to optimize member sizes for high-rise buildings so that stable and sustainable designs can be achieved.

结构优化可以导致高层混凝土建筑的高效设计。然而，结构分析步骤的高计算成本阻碍了其在实际案例中的应用。针对这个问题，本研究开发了一种新的混合优化框架。考虑了多种加载场景下的离散变量、规范规定的约束和其他构造要求。混合优化框架包含两个步骤：在第一步中，使用组合响应面方法 (RSM) 构建优化公式的计算有效近似。结合所有设计约束，在原问题松弛模型的基础上，提出三个子优化问题，以更新接近最优区域的响应面模型。当响应面模型在接近最优区域具有足够的精度时，在第二步中，应用离散粒子群优化（PSO）技术利用响应面模型搜索最优解。通过整合市售的ETABS，开发了具有独立接口的专用优化软件，并在本文中包含了实际软件开发的细节。给出了三个高层混凝土建筑设计的例子来证明所提出的优化框架的有效性，并将结果与​​一般 PSO 算法的结果进行了比较。结果表明，与传统设计方法相比，混合优化框架可减少 14.3% 的材料使用量。此外，与 PSO 相比，所提出的框架显着提高了计算效率。因此，工程师可以使用这种方法来优化高层建筑的构件尺寸，从而实现稳定和可持续的设计。