(In)forming the new building envelope: A pedagogical study in generative design with precedents and multimodal large language models

Abstract

This pedagogical study delves into integrating established and emerging computational methods into architectural education, with a specific focus on building envelope design within a B.Arch. course. Students employ parametric modeling (PM), design optimization (DO), and multimodal large language models (MLLMs) to analyze and reinterpret building envelope precedents. Parametric design and optimization are utilized to explore envelope variations based on parametric logic and performance evaluation. In the case of MLLMs, students leverage visual patterns from precedents as a form-giving construct for new 3D envelope proposals. While students adeptly integrate MLLMs into their design process, generating successful 3D models, challenges arise in control and translation across representations, leading to unclear scale and tectonics in some design proposals. Survey results reveal that students perceive MLLMs as a valuable, uncomplicated method for rapid design ideation and refinement, but challenges persist in addressing real architectural constraints. Parametric modeling is viewed as a tool for structuring design and DO is seen as a later stage for refining designs based on metrics. The study underscores the importance of evolving user interfaces for MLLMs in specific design tasks, addressing challenges in precision and design scale through prompts and guiding images. It also discusses the potential to combine MLLMs with various generative methods and modeling software during transitions between design media to support future initiatives integrating computational methods into the design process.