Author(s): Kanhaya Prasad Maletha and Astha Verma

Abstract: Tall buildings are highly susceptible to wind-induced forces, making wind load estimation a critical component of their design process. Tall buildings' height, flexibility, and slenderness make them more vulnerable to wind-induced forces. For structural safety, occupant comfort, and performance under dynamic wind effects, accurate wind load estimation is crucial. This review synthesises results from recent research on wind load analysis using Machine Learning (ML) models, wind tunnel testing, and Computational Fluid Dynamics (CFD) across different building shapes. The impact of geometry on aerodynamic performance such as twisting, cross-sectional tapering, and corner modifications-is specifically examined. According to studies, optimised shapes that minimise wind pressures and displacements include twisted forms, Y-shaped layouts, and filleted corners. The review also identifies shortcomings in codal standards (such as ASCE 7-16 and IS 875) that deal with complex shapes and interference effects. Moreover, sophisticated ML techniques.

DOI: 10.22271/27078280.2025.v6.i1a.39

Pages: 21-29 | Views: 162 | Downloads: 80

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