Accurate and rapid acquisition of crosswind loads is crucial for the preliminary structural design of tall buildings. Existing studies on predicting the crosswind loads on tall buildings commonly face limited applicability and low accuracy, thus hindering the widespread practical application. To address the issue, this study establishes a comprehensive database of generalized crosswind loads based on high-fidelity data from wind tunnel tests and large-eddy simulation (LES). Furthermore, an aerodynamic model for predicting the crosswind loads is proposed using the artificial neural network (ANN). This model accurately predicts the generalized crosswind loads on tall buildings incorporating a diverse range of building dimensions and three distinct boundary layer wind fields. The applicability and effectiveness of the proposed model are validated thoroughly by comparison with the experimental data from this study and other literature. In addition, the parametric analysis shows that certain variations in the building height have a slight effect on the crosswind loads. When the side ratio SR = 1, the crosswind base moment spectra are insensitive to the variations in building width. While for SR > 1, both the spectral values and the root mean square (RMS) base moment coefficients decrease greatly with the increase in building width. The proposed model provides a comprehensive perspective to understand the variation patterns of the crosswind loads on tall buildings under the combined effects of various influencing factors. Moreover, it provides a solution for the rapid and accurate evaluation of the crosswind responses in current wind engineering practice.
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