AI-driven conceptual optimization of building façade layouts using a fuzzy-logic-based morphological index

This paper introduces a computational framework that bridges the gap between qualitatively driven architectural intent and quantitatively grounded engineering optimization in the context of building façade design. At the core of the framework is a Morphological Index (MI) based on fuzzy inference, which synthesizes measurable attributes of the façade layout into a single, interpretable score. This index, in turn, serves as the objective of an optimization algorithm tasked with shaping the façade’s morphology according to designers’ preferences. A series of numerical investigations illustrates the framework’s adaptability to diverse morphological design goals. Ultimately, the conversion of optimized layouts into expressive representations via artificial-intelligence-powered visualizations confirms the framework’s applicability to automated conceptual design of building façades.