Cement production significantly contributes to CO₂ emissions (8% of worldwide CO₂ emissions) and global warming, accelerating climate change and increasing air pollution, which harms ecosystems and human health. To this end, this research investigates the fresh and hardened properties of sustainable concrete fabricated with three different replacement percentages (0%, 5%, and 10% by weight) of ordinary Portland cement (OPC) using rice husk ash (RHA). The hardened properties were evaluated at 14, 28, 60, 90, and 120 days of water curing. In addition, data-based models were developed, validated, and optimised, and the models were compared with experimental results and validated with the literature findings. The outcomes reveal that the slump values increased (17% higher) with the increased content of RHA, which aligns with the lower temperatures (12% lower) of freshly mixed concrete with RHA than the control mix (100% OPC). The slopes of the stress–strain profiles decreased at early ages and improved at longer curing ages (more than 28 days), especially for mixes with 5% RHA. The compressive strength decreased slightly (18% at 28 days) with increased percentages of RHA, which was minimised with increased curing ages (8% at 90 days). The data-based model accurately predicted the stress–strain profiles (coefficient of determination, $R^2$ ≈ 0.9950–0.9993) and compressive strength at each curing age, including crack progression (i.e., highly nonlinear region) and validates its effectiveness. In contrast, the optimisation model shows excellent results, mirroring the experimental data throughout the profile. These outcomes indicate that the 10% RHA could potentially replace OPC due to its lower reduction in strength (8% at 90 days), which in turn lowers CO₂ emissions and promotes sustainability.

Optimization of Natural Ventilation process in highrise buildings is one of the most complex and least addressed phenomenon in the field of sustainable architecture. This issue requires urgent consideration to reduce the computation time due to fast growing demand of vertical construction in metropolitan cities. Until recently most highrise buildings have been operated with mechanical systems, causing high energy loads in hot climates and have high carbon footprints. Highrise buildings with natural ventilation and sky gardens can address these problems. This study involves the development of a Genetic Algorithm (GA) addressing the multi objective optimization of natural ventilation in tall buildings incorporated with Sky-Gardens at different levels all connected through a central ventilation shaft. The fitness function for this GA is composed of three scales; temperature reduction due to evapotranspiration of plants of sky-gardens, optimum wind velocity for channelizing air inside the corridors and ventilation shaft, and optimum building configuration. The aim is to find the best solutions for tall buildings constructed in hot climate through the provision of optimized airflow paths suitable for the effectiveness of natural ventilation, within a reasonably short computation time for supporting design processes at early stage.