Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings

Abstract

Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.