Algorithmic Optimization of Midrise Residential Building Plans Based on Space Syntax Theory
Subject Areas :sahba hasibi 1 , Ali Andaji Garmarodi 2
1 - Faculty of architecture, Department of Arts and Architecture, Islamic Azad University, South Tehran Branch, Tehran, Iran.
2 - Faculty of architecture, Department of Arts and Architecture, Islamic Azad University, South Tehran Branch, Assistant Professor, Pars Higher Education Institute of Art and Architecture, Tehran, Iran, (Corresponding Author)
Keywords: Space syntax, architectural design, midrise residential, personalization, algorithmic method, optimization algorithm,
Abstract :
The space syntax and geometric layout, influenced by a wide array of explicit and implicit parameters and lead to multiple solutions for design problems, are one of the primary steps for architectural designs. Two of the main challenges faced in this subject are: utilizing the computational power of computers to predict the space syntax of architectural plans and defining the problem in an Algorithmic language. The current study aims to present an algorithm for reaching a space syntax followed by the users' needs and preferences to form a meaningful connection between the houses and their residents and facilitate the user's participation in the design of midrise residential buildings. In the current study, multi-objective optimization was used to achieve space syntax designs based on multiple parameters. In order to optimize, a set of 200 manually layout design plans were used as input for the algorithm; the algorithm then generates plans for the midrise residential buildings based on criteria such as open and closed space area, requirements for orientations of various spaces, number of rooms. Then, numerous solutions are reviewed and compared, and the most suitable plan is chosen based on the results. Finally, the entire process of algorithm was tested for some case studies, and the results show a great capacity of the proposed method in providing space syntax plans with speed and variety.
1. رحمتی¬گواری، ر.، قدوسی¬فر، ه.، طاهباز، م.، و زارع میرک آباد، ف. (1399). بررسی رویکردها الگوریتمیک در چیدمان فضایی. معماری و شهر¬سازی آرمانشهر، 32، 111-99.
2. رهبر، م.، مهدوی نژاد، م. ج.، بمانیان، م.، و دوائی مرکزی، ا. (1399). الگوریتم سی گن در تولید نقشه حرارتی جانمایی فضایی در طراحی معماری. معماری و شهر¬سازی آرمانشهر، 32، 142-131.
3. عزیزی قهرودی، م.، و رضایی، م. (1400). تحلیل پارامتریک سایت پلان مبتنی بر روش ماتریس ارزیابی تأثیرات محیطی (مطالعه موردی: مجموعه آرامگاه شمس تبریزی). پژوهش¬های معماری نوین، 1(1)، 70-55.
4. گلابچی، م.، اندجی، ع.، و باستانی، ح. (1391). معماری دیجیتال. چاپ دوم، تهران، انتشارات دانشگاه تهران.
5. مختاری، ن.، و اسفندیاری فرد، ا. (1400). بررسی پیکربندی ساختار فضایی کاروانسرای شاه عباسی در کرج به روش نحو فضا. پژوهش¬های معماری نوین، 2(1)، 96-85..
6. معماریان، غ. ح. (1381). نحو فضای معماری. مجله صفه، 35، 74-84.
7. نجاتی، ن.، کلانتری، س.، بمانیان، م. (1400). آموزش طراحی معماری مبتنی بر هوش مصنوعی. پژوهش¬های معماری نوین، 21(1)، 25-7.
8. Anderson, J. (2017). Basics architecture 03: Architectural design. Bloomsbury Publishing.
9. Çolakoğlu, B., & Yazar, T. (2007). An innovative design education approach: Computational design teaching for architecture. METU JFA, 24(2), 159-168.
10. Baušys, R., & Pankrašovaite, I. (2005). Optimization of architectural layout by the improved genetic algorithm. Journal of Civil Engineering and Management, 11(1), 13-21.
11. Bonnaire, X., & Riff, M. C. (2002, June). A self-adaptable distributed evolutionary algorithm to tackle space planning problems. In International Workshop on Applied Parallel Computing (pp. 403-410). Springer, Berlin, Heidelberg.
12. Fathi, A., Saleh, A., & Hegazy, M. (2016). Computational design as an approach to sustainable regional architecture in the Arab world. Procedia-Social and Behavioral Sciences, 225, 180-190.
13. Gero, J. S., & Maher, M. L. (2013). Modeling creativity and knowledge-based creative design. Psychology Press.
14. Gero, J. S., & Kazakov, V. A. (1997). Learning and re-using information in space layout planning problems using genetic engineering. Artificial Intelligence in Engineering, 11(3), 329-334.
15. Grason, J. (1971, June). An approach to computerized space planning using graph theory. In Proceedings of the 8th Design automation workshop (pp. 170-178).
16. Guo, Z., & Li, B. (2017). Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system. Frontiers of Architectural Research, 6(1), 53-62.
17. Hillier, B. (2007). Space is the machine: a configurational theory of architecture. Space Syntax.
18. Hillier, B., & Sahbaz, O. (2005). High resolution analysis of crime patterns in urban street networks: an initial statistical sketch from an ongoing study of a London borough. In Proceedings Space Syntax. 5th International Symposium, Delft.
19. Inoue, T., Kohama, Y., & Takada, T. (2000). Study on Aarchitectural space planning by optimality method. Japan Society of Mechanical Engineers (OPTIS2000), 2000(4), 281-285.
20. J. Ansel, C. Chan, Y. L. Wong, M. Olszewski, Q. Zhao, A. Edelman, and S. Amarasinghe. PetaBricks: A language and compiler for algorithmic choice. In ACM Programming Language Design and Implementation, 2009.
21. Jagielski, R., & Gero, J. S. (1997). A genetic programming approach to the space layout planning problem. In CAAD futures 1997 (pp. 875-884). Springer, Dordrecht.
22. Jo, J. H., & Gero, J. S. (1998). Space layout planning using an evolutionary approach. Artificial intelligence in Engineering, 12(3), 149-162.
23. Kilkelly, M. (5). Ways computational design will change the way you work. ArchSmarter. Saatavissa: https://archsmarter. com/computational-design/. Hakupäivä, 2, 2016.
24. Koopmans, T. C., & Beckmann, M. (1957). Assignment problems and the location of economic activities. Econometrica: journal of the Econometric Society, 53-76.
25. Levin, P. H. (1964). Use of graphs to decide the optimum layout of buildings. The Architects' Journal, 7, 809-815.
26. Liggett, R. S. & W. J. Mitchell. (1981). Optimal space planning in practice. Computer-Aided Design, 13(5), 277-288.
27. Markhede, H., & Carranza, P. M. (2007). Spatial Positioning Tool (SPOT). New developments in space syntax software, 1.
28. Menges, A., & Ahlquist, S. (2011). Computational design thinking: computation design thinking. John Wiley & Sons.
29. Michalek, J., Choudhary, R., & Papalambros, P. (2002). Architectural layout design optimization. Engineering optimization, 34(5), 461-484.
30. Pramanik, P. K. D., Mukherjee, B., Pal, S., Pal, T., & Singh, S. P. (2021). Green smart building: Requisites, architecture, challenges, and use cases. In Research Anthology on Environmental and Societal Well-Being Considerations in Buildings and Architecture (pp. 25-72). IGI Global.
31. Rodrigues, E., Gaspar, A. R., & Gomes, Á. (2013). An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 2: Validation and performance tests. Computer-Aided Design, 45(5), 898-910.
32. Roth, J., & Hashimshony, R. (1988). Algorithms in graph theory and their use for solving problems in architectural design. computer-aided design, 20(7), 373-381.
33. Simon, H. A. (1973). The structure of ill structured problems. Artificial intelligence, 4(3-4), 181-201.
34. Simon, M., & Hu, M. (2017). Value by design-systematic design decision making in the architectural design process. Proceedings of ARCC 2017: Architecture Of Complexity.
35. Uçar, B. (2006). An assessment of the architectural representation process within the computational design environment (Master's thesis, Middle East Technical University).
36. Yusuf, H. O. (2012). The impact of digital-computational design on the architectural design process. University of Salford.
