considered a design tool, they now being involved in the decision-making process with the architects. Hence, modifying the role of architects. 3. AI transforming the design process
3.1.
Architectural drawing recognition and generation
Fig 2. Workflow of a typical GAN
A successful research and experiment conducted by Weixin Huang (Tsinghua University) and Hao Zheng (University of Pennsylvania), explored the use of Generative Adversarial Network (GAN) recognizing and generating architectural drawings. GAN is a framework in machine learning, specially designed to learn and generate output data with similar or identical characteristics (Huang, W., & Zheng, H. 2018). This showed that Artificial Intelligence can play a significant role in not only repetitive works, but also creative works. This study also deals with the possibility that human design ability would be greatly expanded when combined with artificial intelligence. The selected case provides a great example by showing how machinic learning can be integrated into architecture design, producing automated architectural plans based on inputs. It showcases how the barrier of technology can be pushed and bring us closer to an automated design process, a step at a time. This study guides me in solving the research question by proving that, the current technology is powerful enough in automating one of the most basic elements of architecture, the design of an architectural plan. The experimenters were able to train a Generative Adversarial Network by assigning color codes to each aspect of an architecture plan, for example the walkway was assigned red color, bedrooms were lite green, windows were dark red, doors dark green and so on. Then they fed several architectural plans to the machine and it was able to produce a colorcoded plan. Then they reversed this process, training the machine to produce architectural plans from color codes. As the system evolved, the generated plans 8|Page
