Aesthetic modular source and carbon potential as mathematical foundations for architectural AIGC design

Abstract Most architects rely on aesthetic intuition, often neglecting the mathematical foundations that underlie aesthetic judgement in design. This study addresses this gap by proposing a mathematical framework that supports both aesthetic excellence and low-carbon performance in architecture. The first contribution is to identify the essence of modular aesthetics as rooted in classical mathematical constructs—ratio, sequence, and spiral. The second contribution is an empirical investigation of 100 award-winning architectural projects using multivariate algorithms to examine the relationships among ratio–sequence–spiral features, award recognition, and decarbonization. The results indicate statistically significant associations between ratio-derived sequences and spiral configurations and both aesthetic recognition and carbon reduction, suggesting a viable pathway for integrating these principles into low-carbon architectural design. Overall, the proposed framework advances a mathematically grounded approach to architectural design that aligns aesthetic quality with environmental goals, while also offering foundational mathematical logic that may inform future robotic 3D-printing applications in extreme environments (e.g., lunar and Martian habitats) and the emerging AI-driven transformation of architectural design and construction.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-04-14
DOI
https://doi.org/10.1057/s41599-026-06849-1
Primary Topic
Architecture and Computational Design
Type
article
Field-Weighted Citation Impact
0.00

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article

Aesthetic modular source and carbon potential as mathematical foundations for architectural AIGC design

L Sun, 叶璐 YE Lu, Gangwei Cai, Bart Dewancker et al.
Humanities and Social Sciences Communications
Architecture and Computational Design
article

Aesthetic modular source and carbon potential as mathematical foundations for architectural AIGC design

L Sun, 叶璐 YE Lu, Gangwei Cai, Bart Dewancker, Binyan Xu, Feidong Lu, Wenwen Shi, Denguo Wu, Weijun Gao, Kang Liu
article en

Abstract

Abstract Most architects rely on aesthetic intuition, often neglecting the mathematical foundations that underlie aesthetic judgement in design. This study addresses this gap by proposing a mathematical framework that supports both aesthetic excellence and low-carbon performance in architecture. The first contribution is to identify the essence of modular aesthetics as rooted in classical mathematical constructs—ratio, sequence, and spiral. The second contribution is an empirical investigation of 100 award-winning architectural projects using multivariate algorithms to examine the relationships among ratio–sequence–spiral features, award recognition, and decarbonization. The results indicate statistically significant associations between ratio-derived sequences and spiral configurations and both aesthetic recognition and carbon reduction, suggesting a viable pathway for integrating these principles into low-carbon architectural design. Overall, the proposed framework advances a mathematically grounded approach to architectural design that aligns aesthetic quality with environmental goals, while also offering foundational mathematical logic that may inform future robotic 3D-printing applications in extreme environments (e.g., lunar and Martian habitats) and the emerging AI-driven transformation of architectural design and construction.

Humanities and Social Sciences Communications
Landscape Institute (GB), Sepuluh Nopember Institute of Technology (ID), The University of Kitakyushu (JP), Globeride (Japan) (JP), Zhejiang Lab (CN), Zhejiang Energy Group (China) (CN), Shanghai Tongji Urban Planning and Design Institute (CN), Powerchina Huadong Engineering Corporation (China) (CN), Zhejiang Tongji Vocational College of Science and Technology (CN), University of Technology Malaysia (MY), The University of Tokyo (JP), Chinese Academy of Governance (CN), Zhejiang University of Finance and Economics (CN)
Royal Astronomical Society
Openalex Percentile: Top 5%
Architecture and Computational Design
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