Genetic Algorithm for Optimizing Urban District and Block Morphology to Minimize Solar Radiation Access and Maximize Building Floor Area in the UAE

Due to climate change, enhancing outdoor and indoor thermal comfort is increasingly important. Solar radiation drives temperature increases, making solar exposure reduction essential in urban design. Most previous research has focused on parametric analysis to optimize small urban blocks, often overlooking the impact of the overall urban district (UD) on reducing Solar Radiation Access (SRA). This work aims to find the optimized UD to minimize SRA and maximize Floor Area (FA). The proposed methodology is developed to achieve these objective functions using a single-objective Genetic Algorithm (GA) with three street layout patterns: random, radial, and grid layout. Further SRA analysis is conducted at the urban block level, focusing on blocks with the highest SRA in the optimized UD to achieve further SRA reduction while maintaining the same FA. Dubai Silicon Oasis district in the UAE was selected as a case study. Elk2-0.3.1 (GIS data), Ladybug (1.7.0), DeCodingSpaces-Toolbox (2021.10), and Galapagos (1.0.0007) Plugins in Grasshopper (0.9.0076) were used. The results show that the radial street pattern achieved better results with an 8.4% reduction in SRA with an 8.9% increase in FA. Additional analysis of the blocks with the highest SRA can achieve an additional 7.4% reduction in SRA.

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