There is a growing conflict between building density and the comfort of the external environment in residential construction, especially in high-density cities in China. To address this conflict, a sensible building layout has to take both aspects into account. However, it is difficult for traditional planning approaches to produce a sensible building layout. This is partly due to the fact that an architect's subjective experiences are unreliable. On the other hand, the wind environment simulations of professional software are often time-consuming so that they are difficult to apply efficiently in practice. This study therefore focuses on the automatic generation of optimized high-density residential building layouts as well as the fast and accurate calculation of the corresponding wind environments. By combining the automatic optimization function of a genetic algorithm and the prediction function of a fully convolutional neural network, an intelligent planning method is proposed for producing optimal high-density residential building layouts in consideration of the local wind environment. To further verify its practicality and significance, a case study was carried out in the Yangtze River Delta region, China, through the automatic generation of a residential building layout, wind environment simulation, and a scheme comparison for optimization.
Keywords: Automatic generation; Fast predict; High-density residential layout; Optimal solution set; Outdoor wind environment.
© 2023 The Authors.