Generating prototypical residential building geometry models using a new hybrid approach

Building prototyping has regularly been used in building performance analyses with statistically feasible models. The novelty of this research involves a new hybrid approach combining stratified sampling and k-means clustering to establish building geometry prototypes. The research focuses on residential buildings in Ningbo, China. Seventeen small residential districts (SRDs) containing 367 residential buildings were systemically selected for survey and data collection. The stratified sampling used building construction year as the main parameter to generate stratification. Floor numbers, shape coefficients, floor areas, and window-to-wall ratios were used as the four observations for k-means clustering. Based on this new approach, nine building geometry prototypes were identified and modelled. These statistically representative prototypes provide building geometrical information and characteristic-based evaluations for subsequent building performance analysis.