COMPUTATIONAL ANALYSIS OF WIND EFFECTS ON HIGH-RISE BUILDINGS USING MACHINE LEARNING

High-rise buildings are increasingly prominent in urban landscapes, but their design and construction require careful consideration of various factors, including wind effects. Computational analysis plays a vital role in assessing and predicting wind-induced responses, enabling engineers to ensure the safety and structural integrity of these buildings. In recent years, machine learning techniques have emerged as powerful tools for analysing complex data and making accurate predictions. Various features such as wind speed, wind direction, building geometry, and surrounding terrain characteristics are considered as input parameters. The investigates different machine learning approaches, including regression, classification, and ensemble techniques, to analyse wind-induced responses in highrise buildings. These responses may include structural displacements, accelerations, and wind-induced loads. The models are trained to capture the intricate relationships between the input parameters and the building responses, enabling accurate predictions. Explores the sensitivity and robustness of the machine learning models by considering different scenarios, such as variations in wind conditions and building geometries. The models' performance is assessed using statistical metrics, such as mean squared error, root mean squared error, and coefficient of determination, to evaluate their predictive capabilities. The integration of machine learning techniques with traditional wind engineering approaches offers a new paradigm for enhancing the safety and efficiency of high-rise building design and construction.