Towards a Novel Method for Architectural Design through μ-Concepts and Computational Intelligence

Architects typically tell stories about their design intentions and then translate the verbal descriptions into action-based commands in Computer Aided Design tools. In other words, during the creative process the designer works in a “concepts space” that is different than the “properties space” available in the software virtual world of CAD tools. In this paper we propose to study the vocabulary of concepts used in architectural design by employing machine learning methods over large data-sets of architectural drawings and their storytelling descriptions. The intention is to computationally characterize the meaning of a set of high-level concepts in architectural design, which we call them as μ-concepts, in terms of a set of low-level properties of drawings. With such a correlation, new opportunities can be explored for radically different design tools that allow architects to design by operating over such high-level architectural concepts. Eventually, this will can also provide a novel way of understanding the mental process of architectural design through verbal concepts.