A novel strategy for tall building optimization via combination of asymmetric genetic algorithm and machine learning methods

The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evolutionary algorithms and machine learning methods, is developed for achieving the optimal design of steel high-rise buildings. The most time-consuming part of the procedure is the analysis of tall buildings and the control of design code constraints, requiring frequent re-analyses. The main idea here proposed is to use machine learning methods for this purpose. The optimization process will be performed by a novel evolutionary algorithm, named asymmetric genetic algorithm (AGA), and in each iteration that requires checking the constraints for a large number of different structural states, machine learning methods, including MLP, GMDH and ANFIS-PSO are facilitators. By coupling ETABS and MATLAB software, various combinations of sections for structural elements are assigned and analyzed automatically, thus creating a database for training the neural networks. Accordingly, a practical methodology for the optimal design of steel tall buildings, allowing for the constraints imposed by typical building codes, is introduced. The applicability of the suggested procedure is described through the determination of the optimal seismic design for some case studies. Results testify that the present method not only supports the precision of the results but also remarkably diminishes the computational time and memory needed, in comparison with the existing classical methods; the optimization process time is also significantly decreased.