Exploration of computational formulations for wind-induced interference effects on high-rise buildings via Kolmogorov–Arnold networks

作者：Kun Wang, Tianhao Shen, Jinlong Liu, Shiqi Wang, Xu Bao, Jingyu Wei, Weicheng Hu, Lei Xu · 发表于：Developments in the Built Environment · 年份：2025 · DOI：10.1016/j.dibe.2025.100770 · 被引用次数：35 · 研究领域：Wind and Air Flow Studies、Seismic and Structural Analysis of Tall Buildings、Probabilistic and Robust Engineering Design

In dense urban environments, wind-induced interference effects introduce significant uncertainties in predicting aerodynamic forces on high-rise buildings. Conventional methods such as wind tunnel tests and computational fluid dynamics (CFD) suffer from high cost and long runtime, while multivariate regression analysis (MRA) lacks the ability to capture nonlinear couplings, and black-box models (e.g., CatBoost) fail to ensure physical consistency. To overcome these limitations, this study proposes a KM-KAN-SR framework that integrates Kolmogorov–Arnold Networks (KAN) with K-means clustering (KM) and symbolic regression (SR) to derive explicit aerodynamic force formulas. Benchmarking results highlight the superior performance of KM-KAN-SR. Specifically, KM-KAN-SR achieved R 2 values of 0.931 and 0.961 for C Fx_mean and C Fy_mean , respectively, significantly higher than those of CFD (0.830 and 0.795) and MRA (0.849 and 0.532). Moreover, the expressions derived by KM-KAN-SR are on average 50 % less complex than those of conventional KAN-SR and remain concise and interpretable. In terms of efficiency, KM-KAN-SR generates predictions within milliseconds, whereas CFD requires millions of grid cells and hours of computation under large-eddy simulation. Sensitivity analyses further reveal that KM-KAN-SR preserves smooth, physically consistent aerodynamic trends, unlike CatBoost which exhibits step-like discontinuities. Overall, the KM-KAN-SR framework demonstrates high predictive ac...