Data-driven machine learning surrogate models for estimating the wind response of tall buildings

ABSTRACT As the demand for high-performance structures continues to rise, the need for performance-based wind design (PBWD) methodologies becomes increasingly apparent. The utmost outputs of PBWD can be achieved when adopted with an optimization framework. However, the computational costs are prohibitively high for optimizing tall building layouts using high-fidelity simulations. Thus, integrating surrogate models offers a promising solution, enabling computationally affordable design optimization. This paper presents a comprehensive assessment of different data-driven models to develop a surrogate model of tall buildings subjected to dynamic wind loads based on their structural system layout. The study investigates the performance of six different machine learning models: Ridge Regression (RDG), Decision Trees (DT), Random Forests (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Deep Neural Networks (DNN). A training and testing dataset has been prepared using Finite Element Method (FEM) for a case study building subjected to wind load time history generated by an experimentally validated Computational Fluid Dynamics (CFD) model. For a valid comparison, each developed surrogate model is trained using a grid search algorithm through a k-fold cross-validation process to achieve optimal parameters and hyperparameters of each model. The investigation involves a detailed analysis of statistical performance metrics applied to training and testing datasets. The results show that the DNN model achieves the highest predictive accuracy, reaching an R 2 of 0.84 for demand-to-capacity ratio prediction under single-angle wind loading, while XGB provides competitive performance with faster inference times. The proposed surrogate framework enables rapid, data-driven estimation of global responses (peak displacement and drift) and local component demands (wall and column D/C ratios), providing a computationally efficient pathway for integrating optimization, sensitivity analysis, and early-stage PBWD decision-making.