AI Summary & Key Takeaways
AI InsightBy providing an evidence based design environment this is very much similar to the design processes of the landscape architect and urban designer.
📌 3 Key Takeaways
Abstract
Architectural design is an iterative research and discovery process. The architect learns through investigation and experimentally develops his/her ideas, builds and evaluates the space, and continuously reforms it. Although this is a subjective process, there are design tools and methods that provide objective criteria for evaluating potentials of the designed space and iterating with feedback. Tools for measuring space in network thinking allow visualization of architectural decisions and developing potentials for architectural programming and restructuring design scenarios. This study evaluates the use of graph theory-based thinking and Space Syntax in architectural design, emphasizes the experimental and cognitive qualities of the design process, and investigates how scientific data and processes can be transferred into design. In other words, it explores the potentials for using Space Syntax related methods that provide real-time information in the design process. The argument is exemplified with the design strategies of the project “Login Park” for International Bandırma Park Competition. By utilizing an ‘animated relational mapping’ as a generative tool during the site-plan investigations, the designer could iteratively assess potential relations and their metric ranges between the required buildings and programs and examine various scenarios through the graph theory-based tools. The authors suggest that these dynamic tools and thinking lead to powerful instigation, management and assessment of configured spaces. By providing an evidence based design environment this is very much similar to the design processes of the landscape architect and urban designer.