Rule-Based Process Governance in Early Architectural Design

Öz

Early architectural design is a dynamic stage characterised by high uncertainty, in which decision-making directly shapes process behaviour. Traditional approaches model this stage as linear, phase-based workflows, leaving feedback conditions, decision triggers, and process reconfiguration largely implicit. This study addresses that gap by extending the concept of rules from object production to process organisation, proposing a conceptual framework in which the design process itself is governed by explicit condition–action relationships. Accordingly, the study asks how explicit condition–action rules can be used to govern process transitions, decision gates, feedback, convergence, and traceability in early architectural design. The framework is developed through three integrated components: a process architecture defining phases and decision gates, a rule execution mechanism, and a process data layer. Within this structure, six rule families govern phase transitions, decision nodes, feedback thresholds, convergence, conflict resolution, and traceability. The framework treats the designer as the central agent: rules define the conditions under which judgement is exercised and recorded, rather than replacing it. The primary contribution is to reposition rule-based thinking from design objects to process organisation, enabling early-design transitions, decisions, and feedback to be specified rather than managed implicitly. The framework's operation is illustrated through a scenario across three threshold calibrations, with empirical validation identified as the primary direction for future work.

Anahtar Kelimeler

• Early design phase
• rule-based process model
• computational design
• design rationale
• from object to process.

Destekleyen Kurum

Financial Support: This study has not been supported by any institution or organisation. You said: teşekkür- ing ve türkçe

Etik Beyan

This study is conceptual in nature and does not involve human participants, experiments, clinical data, or confidential information. Ethics committee approval is not required. Informed consent was not applicable.

Teşekkür

Acknowledgements: None.

Erken Mimari Tasarımda Kural Tabanlı Süreç Yönetimi

Öz

Erken mimari tasarım evresi, yüksek belirsizlikle nitelendirilen ve karar alma sürecinin doğrudan süreç davranışını biçimlendirdiği dinamik bir aşamadır. Geleneksel yaklaşımlar bu evreyi doğrusal, aşama tabanlı iş akışları biçiminde modellemekte; geri bildirim koşullarını, karar tetikleyicilerini ve süreç yeniden yapılandırmasını büyük ölçüde örtük bırakmaktadır. Bu çalışma söz konusu boşluğu, kural kavramını nesne üretiminden süreç organizasyonuna genişleterek ele almakta ve tasarım sürecinin bizzat açık koşul-eylem ilişkileriyle yönetildiği bir kavramsal çerçeve önermektedir. Bu doğrultuda çalışma, açık koşul-eylem kurallarının erken mimari tasarımda süreç geçişlerini, karar kapılarını, geri bildirimi, yakınsamayı ve izlenebilirliği nasıl yönetebileceğini sorgulamaktadır. Çerçeve birbirine entegre üç bileşen üzerinden geliştirilmiştir: aşamaları ve karar kapılarını tanımlayan bir süreç mimarisi, bir kural yürütme mekanizması ve bir süreç veri katmanıdır. Bu yapı içinde altı kural ailesi; aşama geçişlerini, karar düğümlerini, geri bildirim eşiklerini, yakınsama koşullarını, çatışma çözümünü ve izlenebilirliği yönetmektedir. Çerçeve tasarımcıyı merkezi özne olarak konumlandırmaktadır: kurallar, yargının uygulandığı ve kayıt altına alındığı koşulları tanımlamakta; yargının yerini almamaktadır. Birincil katkı, kural tabanlı düşüncenin tasarım nesneleri düzeyinden süreç organizasyonu düzeyine kavramsal olarak yeniden konumlandırılmasıdır; bu yaklaşımla erken tasarım geçişleri, kararlar ve geri bildirimler örtük biçimde yönetilmek yerine açıkça tanımlanabilir hale gelmektedir. Çerçevenin işleyişi üç eşik kalibrasyonu üzerinden açıklayıcı bir senaryo aracılığıyla gösterilmekte; ampirik doğrulama çalışmaları ise gelecek araştırmaların öncelikli yönelimini oluşturmaktadır.

Anahtar Kelimeler

• Erken tasarım aşaması
• kural tabanlı süreç modeli
• hesaplamalı tasarım
• tasarım gerekçesi
• nesneden sürece

Destekleyen Kurum

Destekleyen Kurum: Yoktur.

Etik Beyan

Bu çalışma kavramsal nitelikte olup insan katılımcı, deney, klinik veri veya gizli bilgi içermemektedir. Etik kurul onayı gerekmemektedir. Bilgilendirilmiş onam alınmasına gerek duyulmamıştır.

Teşekkür

Teşekkür: Yoktur.

Kaynakça

1. Archer, L. B. (1969). The structure of the design process. In G. Broadbent & A. Ward (Eds.), Design methods in architecture (pp. 76–102). Lund Humphries.
2. Austin, S., Baldwin, A., Li, B. & Waskett, P. (2000). Analytical Design Planning Technique (ADePT): A dependency structure matrix tool to schedule the building design process. Construction Management and Economics, 18(2), 173–182. https://doi.org/10.1080/014461900370807
3. Braha, D. & Reich, Y. (2003). Topological structures for modeling engineering design processes. Research in Engineering Design, 14(4), 185–199. https://doi.org/10.1007/s00163–003–0035–3
4. Browning, T. R. (2001). Applying the design structure matrix to system decomposition and integration problems: A review and new directions. IEEE Transactions on Engineering Management, 48(3), 292–306. https://doi.org/10.1109/17.946528
5. Caetano, I. & Leitão, A. (2020). Architecture meets computation: An overview of the evolution of computational design approaches in architecture. Architectural Science Review, 63(2), 165–174. https://doi.org/10.1080/00038628.2019.1680524
6. Casakin, H. (2008). Factors of design problem-solving and their contribution to creativity. Open House International, 33(1), 46–60. https://doi.org/10.1108/OHI-01–2008-B0005
7. Cash, P. (2020). Where next for design research? Understanding research impact and theory building. Design Studies, 68, 113–141. https://doi.org/10.1016/j.destud.2020.03.001
8. Chen, X. & Geyer, P. (2022). Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty. Applied Energy, 307, 118240. https://doi.org/10.1016/j.apenergy.2021.118240

9. Crilly, N. (2021). The evolution of "co-evolution" (Part I): Problem solving, problem finding, and their interaction in design and other creative practices. She Ji: The Journal of Design, Economics, and Innovation, 7(3), 309–332. https://doi.org/10.1016/j.sheji.2021.07.003
10. Cross, N. (2006). Designerly ways of knowing. Springer. https://doi.org/10.1007/1–84628–301–9
11. Dino, İ. G. (2012). Creative design exploration by parametric generative systems in architecture. METU Journal of the Faculty of Architecture, 29(1), 207–224. https://doi.org/10.4305/METU.JFA.2012.1.12
12. Dorst, K. (2019). Co-evolution and emergence in design. Design Studies, 65, 60–77. https://doi.org/10.1016/j.destud.2019.10.005
13. Dyer, L., Power, J., Steen, A., Wallis, L. & Davison, A. (2021). Uncertainty and disciplinary difference: Mapping attitudes towards uncertainty across discipline boundaries. Design Studies, 77, 101055. https://doi.org/10.1016/j.destud.2021.101055
14. Elbeltagi, E., Wefki, H. & Khallaf, R. (2023). Sustainable building optimization model for early-stage design. Buildings, 13(1), 74. https://doi.org/10.3390/buildings13010074
15. Flemming, U. (1987). More than the sum of parts: The grammar of Queen Anne houses. Environment and Planning B: Planning and Design, 14(3), 323–350. https://doi.org/10.1068/b140323
16. Gero, J. S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–36. https://doi.org/10.1609/aimag.v11i4.854
17. Gilson, F. & Englebert, V. (2011). Rationale, decisions and alternatives traceability for architecture design. In Proceedings of the 5th European Conference on Software Architecture: Companion Volume (ECSA '11) (Article 4). ACM. https://doi.org/10.1145/2031759.2031764
18. Gürel, A. & Şenyapılı Ozcan, B. (2023). Cognitive comparison of design methods in the conceptual phase. International Journal of Architectural Computing, 21(4), 581–601. https://doi.org/10.1177/14780771231188474
19. Haakonsen, S. M., Rønnquist, A. & Labonnote, N. (2023). Fifty years of shape grammars: A systematic mapping of its application in engineering and architecture. International Journal of Architectural Computing, 21(1), 5–22. https://doi.org/10.1177/14780771221089882
20. Jang, S., Roh, H. & Lee, G. (2025). Generative AI in architectural design: Application, data, and evaluation methods. Automation in Construction, 174, 106174. https://doi.org/10.1016/j.autcon.2025.106174
21. Kalay, Y. E. (2004). Architecture's new media: Principles, theories, and methods of computer-aided design. MIT Press.
22. Kazemi, P., Ghisi, A. & Entezami, A. (2024). Artificial intelligence-powered computational strategies in selecting and augmenting data for early design of tall buildings with outer diagrids. Buildings, 14(4), 1118. https://doi.org/10.3390/buildings14041118
23. Koning, H. & Eizenberg, J. (1981). The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B: Planning and Design, 8(3), 295–323. https://doi.org/10.1068/b080295
24. Lawson, B. (2006). How designers think: The design process demystified (4th ed.). Routledge. https://doi.org/10.4324/9780080454979
25. Lee, S.-H. (2025). From computation to curation: Expanding the boundaries of design practice. Design Studies, 101, 101357. https://doi.org/10.1016/j.destud.2025.101357
26. Li, S., Liu, L. & Peng, C. (2020). A review of performance-oriented architectural design and optimization in the context of sustainability: Dividends and challenges. Sustainability, 12(4), 1427. https://doi.org/10.3390/su12041427
27. Lin, S.-H. E. & Gerber, D. J. (2014). Designing-in performance: A framework for evolutionary energy performance feedback in early stage design. Automation in Construction, 38, 59–73. https://doi.org/10.1016/j.autcon.2013.10.007
28. Muslimin, R. (2023). Experience grammar: Creative space planning with generative graph and shape for early design stage. Buildings, 13(4), 869. https://doi.org/10.3390/buildings13040869
29. Negendahl, K. (2015). Building performance simulation in the early design stage: An introduction to integrated dynamic models. Automation in Construction, 54, 39–53. https://doi.org/10.1016/j.autcon.2015.03.002
30. Oxman, R. (2006). Theory and design in the first digital age. Design Studies, 27(3), 229–265. https://doi.org/10.1016/j.destud.2005.11.002
31. Pektaş, Ş. T. & Pultar, M. (2006). Modelling detailed information flows in building design with the parameter-based design structure matrix. Design Studies, 27(1), 99–122. https://doi.org/10.1016/j.destud.2005.07.004
32. Pietrzyk, K. (2022). Wicked problems in architectural research: The role of research by design. ARENA Journal of Architectural Research, 7(1), 1. https://doi.org/10.5334/ajar.296
33. Purup, P. B. & Petersen, S. (2020). Requirement analysis for building performance simulation tools conformed to fit design practice. Automation in Construction, 116, 103226. https://doi.org/10.1016/j.autcon.2020.103226
34. Rittel, H. W. J. & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://doi.org/10.1007/BF01405730
35. Saadi, J. I. & Yang, M. C. (2023). Generative design: Reframing the role of the designer in early-stage design process. Journal of Mechanical Design, 145(4), 041411. https://doi.org/10.1115/1.4056799
36. Sardeshpande, D. & Gokhale, V. (2022). "Legibility" a product of obligatory processes in parametric architectural design: A study of implications of associative modeling on design thinking in a parametric architectural design studio. International Journal of Architectural Computing, 20(4), 728–741. https://doi.org/10.1177/14780771221139911
37. Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
38. Sim, S. K. & Duffy, A. H. B. (2003). Towards an ontology of generic engineering design activities. Research in Engineering Design, 14(4), 200–223. https://doi.org/10.1007/s00163–003–0037–1
39. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). MIT Press.
40. Singh, M. M., Deb, C. & Geyer, P. (2022). Early-stage design support combining machine learning and building information modelling. Automation in Construction, 136, 104147. https://doi.org/10.1016/j.autcon.2022.104147
41. Stiny, G. (1980). Introduction to shape and shape grammars. Environment and Planning B: Planning and Design, 7(3), 343–351. https://doi.org/10.1068/b070343
42. Tang, A., Babar, M. A., Gorton, I. & Han, J. (2005). A survey of the use and documentation of architecture design rationale. In Proceedings of the 5th Working IEEE/IFIP Conference on Software Architecture (WICSA 2005) (pp. 89–98). IEEE. https://doi.org/10.1109/WICSA.2005.7
43. Wang, R., Nellippallil, A. B., Wang, G., Yan, Y., Allen, J. K. & Mistree, F. (2021). A process knowledge representation approach for decision support in design of complex engineered systems. Advanced Engineering Informatics, 48, 101257. https://doi.org/10.1016/j.aei.2021.101257
44. Wang, W. & Burge, J. E. (2010). Using rationale to support pattern-based architectural design. In Proceedings of the 2010 ICSE Workshop on Sharing and Reusing Architectural Knowledge (SHARK '10) (pp. 1–8). ACM. https://doi.org/10.1145/1833335.1833336
45. Wang, Y., Bian, S., Dong, L. & Li, H. (2023). Multiresolution modeling of a modular building design process based on design structure matrix. Buildings, 13(9), 2330. https://doi.org/10.3390/buildings13092330
46. Yi, H. (2020). Visualized co-simulation of adaptive human behavior and dynamic building performance: An agent-based model (ABM) and artificial intelligence (AI) approach for smart architectural design. Sustainability, 12(16), 6672. https://doi.org/10.3390/su12166672
47. Zahedi, A., Abualdenien, J., Petzold, F. & Borrmann, A. (2022). BIM-based design decisions documentation using design episodes, explanation tags, and constraints. Journal of Information Technology in Construction, 27, 756–780. https://doi.org/10.36680/j.itcon.2022.037
48. Zhou, Y., Wang, W., Wang, K. & Song, J. (2022). Application of LightGBM algorithm in the initial design of a library in the cold area of China based on comprehensive performance. Buildings, 12(9), 1309. https://doi.org/10.3390/buildings12091309