Analysis On Behavior Of High Rise Irregular R.c. Structure Against Influence Of Strong Strength Of Wind Load

Abstract

Rapid Urbanisation And Architectural Demand Have Increased The Use Of Slender High-rise Reinforced-concrete (RC) Buildings With Setbacks, Re-entrant Corners, Non-rectangular Plans, Twisting Forms, Unequal Stiffness Distributions, And Other Aerodynamic Or Structural Irregularities. Such Configurations Can Amplify Separation, Vortex Shedding, Torsional Loading, Cross-wind Response, Local Suction, And Occupant Discomfort Under Strong Winds. This Paper Systematically Reviews The Behaviour, Analysis And Mitigation Of High-rise Irregular RC Structures Subjected To Strong-wind Loading. A Hybrid Systematic And Structured Review Protocol Was Applied To Peer-reviewed Literature, Design Standards And Technical Guidance Published Principally Between 2020 And 2026, With Foundational Studies Retained Where Required. The Reviewed Evidence Was Classified Into Code-based Analytical Procedures, Boundary-layer Wind-tunnel Testing, Computational Fluid Dynamics (CFD) And Fluid–structure Interaction (FSI), Performance-based And Nonlinear Structural Analysis, Aerodynamic/structural Optimisation, Vibration Control, And Machine-learning Prediction. The Synthesis Shows That Irregular Geometry Cannot Be Represented Reliably Through A Single Generic Pressure Coefficient Or Equivalent-static Procedure. Wind Direction, Corner And Side Configuration, Aspect Ratio, Openings, Surrounding-building Interference, Turbulence Modelling, Damping, Modal Coupling And Torsional Eccentricity Strongly Govern Response. Reported Studies Demonstrate That Carefully Selected Irregular Or Modified Forms May Reduce Demand: One T-shaped Configuration Reduced Mean Base-moment Coefficient By 18.6%, Integrated CFD–structural Optimisation Reduced Concrete Volume By 35.71%, And Openings In A Triangular 40-storey Building Reduced Lateral Deformation By About 32–33%. Conversely, Other Shapes Increased Along-wind Base Moment Or Generated Larger Lift And Torque. Emerging Machine-learning Models Substantially Reduce Computational Or Wind-tunnel Burden, But Their Generalisation Remains Dependent On Dataset Diversity And Physics-consistent Validation. The Principal Gap Is The Limited Integration Of Directional Nonstationary Wind Climate, Validated Unsteady Aerodynamics, Nonlinear RC Behaviour, Uncertainty, And Serviceability Into A Unified Design Workflow. The Review Recommends A Tiered Framework Combining Code Screening, CFD/wind-tunnel Validation, Coupled Dynamic Analysis And Performance-based Verification For Reliable And Economical Design Of Irregular RC Towers.

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