Implementing a generative AI model in the architectural concept development phase: Context-based empirical research

In the context of the continual evolution of advanced architectural design tools, driven by computational and artificial intelligence technologies, the practice of design can now benefit from data-driven and iterative design workflows that forward contextually relevant design outputs. Thus, computational and AI tools have huge potential to enhance the conceptual design processes in architecture, due to their special iterative workflow qualities. Node-based interfaces of these tools act as a catalyst to allow more custom and interconnected workflows. Moreover, it is important to test new design potentials that these tools contribute to when used in parallel for the conceptual design process. This thesis explores juxtaposing node-based computational design and AI tools in a dependent workflow process using Grasshopper3D and ComfyUI, within the framework of an experimental architectural concept development process, through a qualitative Research-through-Design methodology(RtD). By experimenting with their workflows, the aim is to formulate different design schemes that are contextually relevant but also achieved iteratively. Visualizing abstract forms to test architectural forms relevant to four styles, which are blob, biophilic, vernacular, and modern architectural styles. Gathering the data about these outputs and analyzing the overall experiment. Ultimately, this thesis achieves iterative, data-informed design schemes with detailed architectural features. Aiding contextually relevant but also architecturally detailed design schemes during the conceptual phase of the architectural design process.