uk

Article

A systematic shape rules repertoire in architectural design: A proof-of-concept

Walid Bouhelis Abdelmalek Arrouf
Abstract

The aim of this study was to identify, structure, and empirically validate a systematic repertoire of elementary form-transformation rules that accurately depicted sketch-based architectural design processes and can be directly implemented in computational environments. To address the lack of a systematically organised, detailed repertoire of shape transformation operations in architectural design research, 48 shape rules were developed through literature review and refined through empirical observation of sketching sessions conducted by two experienced architects. The rules were assigned to two operational categories (Plastic, Structural), one meta-category (Figurative), and 14 rule classes. Protocol analysis confirmed that the repertoire captured the full range of observed form manipulations across 23 sketches and 267 coded transformations, with high intra-coder reliability (Cohen’s Kappa: 0.85-0.87), confirming the robustness and clarity of the proposed classification. Structural rules made up 74% of the observed transformations, highlighting the predominance of configurational exploration in early-stage design, whereas Plastic and Figurative rules accounted for 15% and 11%, respectively. Statistical analyses, including principal component analysis and hierarchical cluster analysis, showed a consistent bipartite structure across both designers: structural rules formed a distinct cluster, while plastic and figurative rules grouped, with PC1 explaining 97-99% of the total variance. The practical significance of this research lies in providing a transparent and reusable transformation framework that supports the analysis of architectural sketching behaviour and facilitates the development of rule-based computational design tools

Keywords

architectural sketching; form manipulation; shape grammar; protocol analysis; design cognition

Download article

Received 14.10.2025, Revised 14.10.2025, Accepted 24.02.2026 Published 26.03.2026

Retrieved from Vol. 12, No. 1, 2026

Suggested citation

Bouhelis, W., & Arrouf, A. (2026). A systematic shape rules repertoire in architectural design: A proof-of-concept. Architectural Studies, 12(1), 55-66. https://doi.org/10.56318/as/1.2026.55

https://doi.org/10.56318/as/1.2026.55

Pages 55-66

References

  1. Al-Hashim, A.A.S., & Alalouch, C. (2025). Structured creativity: Shape grammar as a pedagogical tool for early design learning. Archnet-IJAR: International Journal of Architectural Research, 1-21. doi: 10.1108/ARCH-07-2025-0309.
  2. Berčič, T. (2024). The gradual process of change: Integrating shape grammars in parametric tools. Creativity Game, 12, 46-54. doi: 10.15292/IU-CG.2024.12.046-054.
  3. Borie, A., Micheloni, P., & Pinon, P. (2006). Form and deformation of architectural and urban objects. Marseille: Editions Parenthèses.
  4. Bouhelis, W., & Arrouf, A. (2023). Ethical approval – LEMPAU-2023-HRP-037. doi: 10.5281/zenodo.18822677.
  5. Bouhelis, W., & Arrouf, A. (2024). Excerpt of the design protocol of Architect 2 (H.A.): 10-minute video excerpt and selected sketches from a 45-minute experimental design session. doi: 10.5281/zenodo.18841255.
  6. Çelik, T., & Şahin Çağli, Z. (2024). Productive facade studies with rule-based design: Ankara Çınar Street sampling. Tasarım + Kuram, 20, 76-88. doi: 10.59215/tasarimkuram.dtj431.
  7. Chen, L., Zhang, Y., & Zheng, Y. (2025). A performance-based generative design framework based on a design grammar for high-rise office towers during early design stage. Frontiers of Architectural Research, 14(1), 145-171. doi: 10.1016/j.foar.2024.07.001.
  8. Ching, F.D.K. (2007). Architecture. Form, space & order (3rd ed.). Hoboken: John Wiley & Sons, Inc.
  9. De Biasi, P-M. (2000). For a genetic approach to architecture. Genesis, 14, 13-65.
  10. Elgohary, S.M., Abdin, A.R., & Mohamed, R.M. (2023). Performative driven form finding in the early design stage. Journal of Engineering and Applied Science, 70, article number 73. doi: 10.1186/s44147-023-00225-5.
  11. El-Mahdy, D. (2022). Learning by doing: Integrating shape grammar as a visual coding tool in architectural curricula. Nexus Network Journal, 24, 701-716. doi: 10.1007/s00004-022-00608-w.
  12. Huang, Y., Zhang, Z., Su, P., Li, T., Zhang, Y., He, X., & Li, H. (2025). Performance-driven generative design in buildings: A systematic review. Buildings, 15(24), article number 4556. doi: 10.3390/buildings15244556.
  13. Jang, S., Roh, H., & Lee, G. (2025). Generative AI in architectural design: Application, data, and evaluation methods. Automation in Construction, 174, article number 106174. doi: 10.1016/j.autcon.2025.106174.
  14. Kleiss, M., Mokhtarimousavi, S., Dai, S., & Alani, M. (2025). Bio-generative design morphology with Radiolaria: An application of a nature-based generative shape grammar for geometrical design of space frames. In SIGraDi 2024 (pp. 2479-2490). Barcelona: UIC Barcelona International University of Catalonia. doi: 10.48550/arXiv.2508.08572.
  15. Landis, J.R., & Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. doi: 10.2307/2529310.
  16. Linas, F.A., & Chithra, K. (2024). A comprehensive literature review and bibliometric analysis on shape grammar theory and applications in architecture. International Journal of Architectural Computing, 24(1), 7-34. doi: 10.1177/14780771241281809.
  17. Plocharski, A., Swidzinski, J., & Musialski, P. (2025). Pro-DG: Procedural diffusion guidance for architectural facade generation. arXiv. doi: 10.48550/arXiv.2504.01571.
  18. Prats, M., Lim, S., Jowers, I., Garner, S.W., & Chase, S. (2009). Transforming shape in design: Observations from studies of sketching. Design Studies, 30(5), 503-520. doi: 10.1016/j.destud.2009.04.002.
  19. Rodgers, P.A., Green, G., & McGowan, A. (2000). Using concept sketches to track design progress. Design Studies, 21(5), 451-464. doi: 10.1016/S0142-694X(00)00018-1.
  20. Sondakh, A.F.P., & Indraprastha, A. (2023). Spatial configuration by rules: An experimental parametric shape rules by shape grammar method. ARTEKS: Jurnal Teknik Arsitektur, 8(2), 205-218. doi: 10.30822/arteks.v8i2.1626.
  21. The Declaration of Helsinki. (2013). Retrieved from https://www.wma.net/what-we-do/medical-ethics/ declaration-of-helsinki/
  22. Wetzel, J.-P., Bignon, J-C., & Belblidia, S. (2006). Use of morphological operators to assist architectural design in early stage. In Joint international conference on computing and decision making in civil and building engineering (pp. 3511-3518). Montréal, Canada.
  23. Xie, X.-H., Guo, S., Yan, H., Xu, Y., Zhu, H., Hong, P., & Chen, Y. (2025). Parametric landscape facilities aesthetic design method based on SOR model and Hybrid Kansei engineering: A case of landscape corridors. Buildings, 15(17), article number 3065. doi: 10.3390/buildings15173065.
  24. Yiannoudes, S. (2025). Shaping architecture with generative Artificial Intelligence: Deep learning models in architectural design workflow. Architecture, 5(4), article number 94. doi: 10.3390/architecture5040094.
  25. Zhuang, X., Zhu, P., Yang, A., & Caldas, L. (2025). Machine learning for generative architectural design: Advancements, opportunities, and challenges. Automation in Construction, 174, article number 106129. doi: 10.1016/j.autcon.2025.106129.
ISSN 2411-801X e-ISSN 2786-7374  UDC 71;72
DOI: 10.56318/as