Visual Character Analysis within Algorithmic Design: Quantifying Aesthetics Relative to Structural and Geometric Design Criteria

Buildings are responsible for 40% of world C02 emissions and 40% of the world's raw material consumption. Designing buildings with a reduced material volume is essential to securing a post-carbon built environment and supports a more affordable, accessible architecture. Architecture‚s material efficiency is correlated to structural efficiency however, buildings are seldom optimal structures. Architects must resolve several conflicting design criteria that can take precedence over structural concerns, while material-optimization is also impacted from limited means to quantitatively assess aesthetic decisions. Flexible design methods are required that can adapt to diverse constraints and generate filagree material arrangements, currently infeasible to explicitly model. A novel approach to generative topological design is proposed employing a custom multi-agent method that is adaptive to diverse structural conditions and incorporates quantitative analysis of visual formal character. Computer vision methods Gabor filtering, Canny Contouring and others are utilized to evaluate the visual appearance of designs and encode these within quantitative metrics. A matrix of design outcomes for a pavilion are developed to test adaptation to different spatial arrangements. Results are evaluated against visual character, structural, and geometric methods of analysis and demonstrate a limited set of aesthetic design criteria can be correlated with structural and geometric data in a quantitative metric.

Stuart-Smith, R., & Danahy, P. (2022). Visual Character Analysis within Algorithmic Design: Quantifying Aesthetics Relative to Structural and Geometric Design Criteria. Proceedings of the 27th Conference on Computer Aided Architectural Design Research in Asia (CAADRIA) [Volume 1], 1, 131–140. https://doi.org/10.52842/CONF.CAADRIA.2022.1.131

The paper is accessible on the cumincad website here.

The Autonomous Manufacturing Lab (AML) explores the integration of robotic manufacturing and construction within architectural design. The AML aims to reduce the environmental and economic costs of design and production while enhancing their cultural and aesthetic impact. The lab's integrative methods for generative design and manufacturing are developed as semi-autonomous systems, exploring possibilities for artisanal-like craftsmanship to be scaled to large production volumes. This approach provides greater specificity and enhanced aesthetic character in design responses compared to traditional automated methods, whilst also reducing material use and waste within bespoke design and production processes. The AML's research enhances design and production capabilities across diverse manufacturing methods and materials by developing industrial and mobile robot fabrication techniques, multi-agent systems, AI/machine learning computational approaches, and applications of real-time sensor and computer vision technologies. This multidisciplinary research includes the development of Aerial Additive Manufacturing, recently published in Nature, which demonstrates the world’s first in-flight additive manufacturing by cooperating drones, representing a rapidly deployable form of collective robotic construction.

University of Pennsylvania Weitzman School of Design
The Weitzman School of Design prepares students to address complex sociocultural and environmental issues through thoughtful inquiry, creative expression, and innovation. As a diverse community of scholars and practitioners, we are committed to advancing the public good–both locally and globally–through art, design, planning, and preservation.

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Visual Character Analysis within Algorithmic Design: Quantifying Aesthetics Relative to Structural and Geometric Design Criteria

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