GENERATIVE DESIGN AND IEQ PERFORMANCE OPTIMIZATION OF SCHOOL BUILDINGS BASED ON A PARAMETRIC ALGORITHM

Abstract

This research aims to examine the potential of generative and optimization algorithms in the early stage of a school building design in Tabriz to achieve better IEQ. It also investigates the compatibility of the evolutionary optimization tools combined with a parametric model in stimulating building comfort performance in achieving an optimized design. This process includes four steps: defining the parametric building model, defining its material and construction properties, stimulation of thermal and visual comfort and carbon dioxide concentration, optimization, and choosing the best result. The adaptive PMV model is used for thermal comfort, imageless daylight glare probability is used for visual comfort, and a CO2 concentration is used for IAQ assessment. It was found that the performance of the options introduced by the algorithm is more appropriate than the design prototype. However, the results show that the samples are acceptable in carbon dioxide concentration. What needs further investigation is thermal and visual comfort. Among the studied variables on IEQ performance, the WWR ratio of the southern wall had the most significant impact. Based on the optimization results, thermal comfort changed in the range of 10%, visual comfort in the range of 30%, and CO2 concentration in the range of 0.19%.