Author(s)
Yi L, Jun Zhou, Fubin Chen, Mengmeng Sun
Abstract
Achieving effective wind resistance in high-rise buildings is vital for structural safety, serviceability, and economic efficiency. With growing demands on slender, taller structures, engineers face challenges in controlling wind-induced displacements and accelerations while managing material costs.
An innovative solution lies in leveraging computational intelligence—particularly particle swarm optimisation (PSO)—to drive smarter, real-time design decisions.
This advanced study introduces an improved PSO method for wind resistance building design, integrating it with a penalty function to handle complex, implicit constraints. The design variables are structural member dimensions, with the goal of minimising total weight while satisfying serviceability limits for top displacement, inter-storey drift, and natural frequency thresholds.
What makes this approach significant is its integration of simulation tools: MATLAB controls the optimisation loop while ANSYS performs real-time wind response analysis. Equivalent static wind loads (ESWLs) are updated dynamically as structural properties evolve, ensuring accurate wind-induced response prediction at each iteration. The method supports discrete cross-sections, making it highly applicable to practical engineering.
The case study of a 60-storey reinforced concrete frame-tube high-rise demonstrates remarkable performance. It achieves notable reductions in across-wind shear forces and top displacements while maintaining compliance with code limits. Key outcomes include increased structural stiffness, balanced natural frequencies, and reduced resonant acceleration—particularly critical for comfort and safety in residential towers.
Additionally, the optimised dimensions of columns, beams, and shear walls are fine-tuned to balance stiffness and material use. This not only ensures compliance with wind resistance requirements but also provides material savings and structural refinement. The improved algorithm shows excellent convergence and efficiency, outperforming traditional genetic and simulated annealing approaches.
In conclusion, this intelligent optimisation method significantly enhances wind resistance in high-rise buildings, providing engineers with a practical tool for safe, economical, and code-compliant designs. Its adaptability to other structural types further underscores its relevance in modern civil engineering.
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