( 2018) developed a generic Justified Plan Graph (g-JPG) grammar and proposed a hybrid method that combined Space Syntax and shape grammar to find out both the syntactical and grammatical genotypes of designs. The method is synthetic, predictive, and generative. Eilouti ( 2019) introduced a reverse engineering technique into generative design method and proposed a parsing tool to decode the morphogenesis in architecture.
In that research, the proposed method was tested on a commercial building, and different alternatives of circulation were generated successfully. Circulation is a design method used in architectural design that is formed by connecting the points left by indoor or outdoor space movements of human. ( 2018) introduced the concept of circulation into shape grammar. Ozdemir and Ozdemir ( 2018) proposed a novel generation method with multi-criteria decision making (MCDM) techniques to generate alternatives for specific architectural models. Contextualism was used in their work to represent the relationship between new designs and the existing surroundings. Lambe and Dongre ( 2019) proposed a SG method to create an architectural design scheme based on the style of the existing architecture. Designers will choose an optimal solution from a large number of generated design alternatives. In some cases, the performance evaluation is embedded into the generative design methods to drive the creation of schemes.
Generative design methods are developed in order to automatically create new design schemes based on the rules or constraints set by designers. Since then, many research projects utilized different approaches, like cellular automata (CA) and shape grammar (SG), to help designers with their designs. Generative design was proposed first in the 1970s and was used in architectural design in 1974 (Frazer 2002). The test results have proven that the method is a potentially effective way for assisting urban design.
#Building generator algorithm manual
Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. The agent arranges one building in the site at one time in a training episode according to the observation. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China.
In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In recent years, generative design methods are widely used to guide urban or architectural design.