Applications of Genetic Algorithms for Designing Efficient Parking Shelters with Conoid-Shaped Roofs

Rapid urbanization, excessive motorization, and the imperative to reduce carbon footprints are driving the search for sustainable urban space solutions. One promising approach involves the effective design of small-scale architecture, such as parking shelters, optimized for structural material consumption and resilience to vehicle impacts. This research employed a novel approach during the initial design phase. Genetic algorithms and optimization techniques were utilized to define the optimal geometries of steel structures, focusing on the height of the conoidal roof and the shape and arrangement of columns. The subsequent analysis included static and strength calculations, dimensioning, and evaluating structural responses to exceptional loading, incorporating novel impact scenarios. The analysis yielded several key insights into the structural efficiency, dynamic behavior, and design optimization of the shelters. The research revealed that both roof geometry and column shape and arrangement significantly influenced material consumption and design effectiveness. The findings indicated that shelters with four straight, vertical, non-corner columns exhibited the most favorable dynamic behavior and highest impact resistance. These shelters also facilitated easy parking for both single-module and double-module roof types. The research findings provide a foundation for the parametric design of functional and structurally resilient parking shelters that cater to urban transportation needs and ecological objectives.