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PurposeIn this article, a practical design methodology is proposed for discrete sizing optimization of high-rise concrete buildings with a focus on large-scale and real-life structures.Design/methodology/approachThis framework relies on a computationally efficient approximation of the constraint and objective functions using a radial basis function model with a linear tail, also called the combined response surface methodology (RSM) in this article. Considering both the code-stipulated constraints and other construction requirements, three sub-optimization problems were constructed based on the relaxation model of the original problem, and then the structural weight could be automatically minimized under multiple constraints and loading scenarios. After modulization, the obtained results could meet the discretization requirements. By integrating the commercially available ETABS, a dedicated optimization software program with an independent interface was developed and details for practical software development were also presented in this paper.FindingsThe proposed framework was used to optimize different high-rise concrete buildings, and case studies showed that material usage could be saved by up to 12.8% compared to the conventional design, and the over-limit constraints could be adjusted, which proved the feasibility and effectiveness.Originality/valueThis methodology can therefore be applied by engineers to explore the optimal distribution of dimensions for high-rise buildings and to reduce material usage for a more sustainable design.

The scope of this paper is to present a novel design methodology that relies on the tabu search (TS) algorithm and aims to optimize the shear wall layout of high-rise buildings. By discretizing the distributable area of a shear wall into wall elements, different possible shear wall layouts can be described by binary code where ‘0’ and ‘1’ indicate the absence or presence of a wall element respectively. Furthermore, the conceptual design is combined with the optimization algorithm to play a guiding role in the optimization, and the surrogate model is built through support vector machine (SVM) to drive the optimization process with historical data and reduce the cost of the real model’s evaluation. Then, the structural weight can be automatically minimized with constraints on the story drift and period ratio and an optimization program with an independent interface is compiled in this paper. This method was used to optimize the layouts of different shear wall structures, which proved the feasibility and effectiveness.