Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning

The interference effects of taller high-rise buildings significantly impact the nearby shorter highrise buildings in dense urban areas, potentially leading to severe wind-induced disasters. This study aims to develop a universal intelligent assessment method to predict the aerodynamic forces and wind-induced responses of buildings, considering various interfering and coupling factors. A database containing 61440 samples is established through a series of synchronous pressure measurement wind tunnel tests. The aerodynamic forces and wind-induced responses were predicted using six machine learning models under varying wind fields, interfering building locations, interfering building sizes, and dynamic characteristics of the principal building. The performance of the models was enhanced through Bayesian hyperparameter optimization and evaluated using the R2 and NRMSE. Additionally, the SHapley Additive exPlanations (SHAP) method was applied to evaluate the impact of each factor on the aerodynamic force and windinduced response coefficients. Eight specific cases were examined using local SHAP explanations to explore the influence of seven input variables on maximum wind-induced response coefficients. Results indicated that the XGB model exhibited superior predictive performance, with R2 exceeding 0.99 in both training and testing sets. The SHAP analysis revealed significant impacts from wind direction angle, reduced wind speed, and damping ratio on wind-induced response. Consequently, irrelevant or minimally impactful input variables were removed from the XGB models. The R2 and NRMSE variation rate with reduced inputs for the optimized XGB model was less than 0.45 %. The graphical user interface (GUI) was designed can effectively guide intelligent urban planning and building design.