Spatial layout design in architecture through deep neural network algorithms

Subject Areas : Space Ontology International Journal

Mahsa Hamouni ¹ , Hossein Soltanzadeh ² , Seyed Hadi Ghoddusifar ³ , Muharram Mansoorizadeh ⁴

1 - Department of Architecture, Faculty of Art & Architecture, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Architecture, Faculty of Architecture and Urban Planning, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3 - Department of Architecture, Faculty of Art and Architecture, South Tehran Branch, Islamic Azad University, Tehran, Iran.
4 - Department of Computer Science, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

Received: 2023-06-01 Accepted : 2024-08-29 Published : 2024-09-25

Keywords: Spatial layout design, Generative design, Deep learning, GAN,

Abstract :

The architectural layout design is a well-known algorithmic problem in computer-aided architectural design. It is the assignment of discrete space elements to their corresponding locations while attempting to satisfy geometrical and topological goals in their layout. This task requires maintaining consistency to ensure that the requirements are met and is exploratory and iterative in nature. The complexity of this problem has encouraged researchers to explore computational approaches for predicting the space layouts. This activity, takes place during the preliminary design phase and is highly significant, as it impacts later stages of the building lifecycle. Therefore, these methods has the potential to enhance the effectiveness and efficiency of spatial layout in design and suggested to make the work of architects easier and faster. Numerous methods have been proposed to solve the space layout design problem. Each one can be viewed as a rule-based strategy that attempts to simulate space layout design using some high-level rules. However, for producing space layout designs, in addition to the quantitative criteria that may be tested and assessed in a logical process, numerous non-quantitative elements also exist. These qualitative criteria are frequently based on a variety of factors and are challenging to describe; thus, hard-coding them would not be possible or effective. It would be best if the program could learn these rules from existing examples. Some potential solutions can be found in the rapidly expanding field of machine learning, which can serve as a tool for decision-making. Deep learning, a subfield of machine learning, can adaptively achieve goals by learning from data and interpreting experiences. The generative adversarial network (GAN), is a deep learning algorithm that has shown remarkable outcomes in the development of 2D designs. In this paper, GAN is applied to generate automated space layouts with given boundaries. A specialized training dataset, comprising 660 existing apartment layouts from Hamadan, is prepared, with each layout labelled using different colours to represent various spaces for training the model. After the model is trained, the boundary lines of 12 new apartments are tested. The performance of the model is also evaluated using two methods: the pixel accuracy measure as the quantitative method and a qualitative assessment by an expert architect based on the evaluation criteria. The results show that the proposed model successfully generates space layout plans from predefined boundaries. This issue indicates its potential for application in other cases and designs. We propose this model as a tool to facilitate the architectural layout design process, enabling architects to quickly and precisely meet client requests particularly in the projects with complex topological constraints.

References:

Arvin, S.A., House, D.H., Modeling architectural design objectives in physically based space planning, Automation in Construction 11 (2002) 213–225, https:// doi.org/10.1016/S0926-5805(00)00099-6.
As, I,. Pal, S., Basu, P., (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing. 16. 306-327.
Bahrehmand, T. Batard, R. Marques, A. Evans, J. Blat, Optimizing layout using spatial quality metrics and user preferences, Graphical Models 93 (2017) 25–38, https://doi.org/10.1016/j.gmod.2017.08.003.
Bao, F., Yan, D.M., Mitra, N.J., Wonka, P., Generating and exploring good building layouts, ACM Transactions on Graphics 32 (2013), https://doi.org/ 10.1145/ 2461912.2461977.
Bausys, R., and I. Pankrasovaite. 2005. Optimization of architectural layout by the improved genetic algorithm. Journal of Civil Engineering and Management 11 (1):13–21.doi:10.3846/13923730.2 005. 9636328.
Bisht, S., Transforming an Adjacency Graph into Dimensioned Floorplan 0, 2022, pp.1–18, https://doi .org/10.1111/cgf.14451.
Borji, A. (2019). Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding, 179, 41-65.
Chaillou, S., A.I. Architecture, towards a new approach, Harvard (2019), https://doi.org/ 10.9783/978194905 7027-006.
Chatzikonstantinou, I., A 3-dimensional architectural layout generation procedure for optimization applications, in: DC-RVD, Proceedings of 2014 ECAADe Conference Vol. 1, 2014, pp. 287–296. http:// papers. Cumincad .org/ cgi-bin/works/paper/ecaade2014_163.
Creswell, A., T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath. 2018. “Generative Adversarial Networks: An Overview.” IEEE Signal Processing Magazine 35, no. 1: 53–65. https:// doi.org/10.1109/MSP.2017.2765202.
Dino, I.G., An evolutionary approach for 3D architectural space layout design exploration, Automation in Construction 69 (2016) 131–150, https://doi.org/ 10.1016/j.autcon.2016.05.020.
Gero, J. S., and V. A. Kazakov. 1997. Learning and re-using information in space layout planning problems using genetic engineering. Artificial Intelligence in Engineering 11 (3):329–34. doi:10.1016/S0954-181 0(96)00051-9.
Geron, A., Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (2019). (2nd ed.). O’Reilly.
Goodfellow, I., et al. (2016) Deep Learning. MIT Press, Cambridge, MA.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y. 2014, Generative adversarial nets. NIPS 2014 (Conference on Neural Information Processing Systems.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio. Y., Generative adversarial nets. In NIPS, 2014. 2, 4, 6, 7.
Grover, R., Emmitt, S. & Copping, A. The typological learning framework: the application of structured precedent design knowledge in the architectural design studio. Int J Technol Des Educ 28, 1019–1038 (2018). https://doi.org/10.1007/s10798-017-9421-4.
Guo, Z., Li, B., Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system, Frontiers of Architectural Research 6 (2017) 53–62, https://doi. org/10.1016/j.foar.2016.11.003.
Helme, L., Derix, C., Spatial configuration: semi-automatic methods for layout generation in practice, The Journal of Space Syntax 5 (1) (2014) 35–49. ISSN:2044-7507http://joss.bartlett.ucl.ac.uk/journal/index.php / j oss/article/vie w/201/pdf.
Hua, H., Irregular architectural layout synthesis with graphical inputs, Automation in Construction 72 (2016) 388–396, https://doi.org/10.1016/j. autcon.2016.09.009.
Huang, W. and Zheng, H. (2018), “Architectural drawings recognition and generation through machine learning”, Recalibration on Imprecision and Infidelity - Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture, ACADIA 2018, doi: 10.52842/conf.acadia.2018.156.
Inoue, M., Takagi, H., Layout algorithm for an EC-based room layout planning support system, in: SMCia 2008 : IEEE Conference on Soft Computing in Industrial Applications, 2008, pp. 165–170, https://doi.org/10.1109/ SMCIA.2008.5045954.
Jagielski, R., and J. S. Gero. 1997. A genetic programming approach to the space layout planning problem. In CAAD futures. Dordrecht: Springer; pp. 875-884.
Jo, J. H., and J. S. Gero. 1998. Space Layout Planning using an evolutionary approach. Artificial Intelligence in Engineering 12 (3):149–62. Doi:10. 1016/ S0954-1810(97)00037-X.
Koenig, R., Knecht, K., Comparing two evolutionary algorithm based methods for layout generation: dense packing versus subdivision, AIEDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28 (2014) 285–299, https://doi.org/10.1017/S0890060414000237.
Koenig, R., Schneider, S., Hierarchical structuring of layout problems in an interactive evolutionary layout system, AIEDAM - Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26 (2012) 129–142, https://doi. org/ 10.1017/S0890060 412000030.
Latha, H., Patil, S. & Kini, P.G. Influence of architectural space layout and building perimeter on the energy performance of buildings: A systematic literature review. Int J Energy Environ Eng (2022).
Liggett, R. S., and J. M. William. 1981. Optimal Space Planning in Practice. Computer-Aided Design 13 (5):277–88.
Liu, C., Schwing, A.G., Kundu, K., Urtasun, R., Fidler, S., Rent3D: floor-plan priors for monocular layout estimation, in: Proceedings of the IEEE Computer Conference on Computer Vision and Pattern Recognition. 07-12-June, 2013, pp. 3413–3421, https://doi.org/10.1109/CVPR.2015.7298963.
Marson, F., Musse, S.R., Automatic real-time generation of floor plans based on Squarified Treemaps algorithm, International Journal of Computer Games Technology 2010 (2010), 624817, https:/ /doi.org /10. 1155/ 2010 /624 817.
Medjdoub, B., Yannou, B., Separating topology and geometry in space planning, CAD Computer-Aided Design 32 (2000) 39–61, https://doi.org/10.1016/S0010- 4485(99)00084-6.
Merrell, P., Schkufza, E., Koltun, V., Computer-generated residential building layouts, ACM Transactions on Graphics 29 (2010) 1–12, https://doi.org/ 10.1145/1882261.1866203.
Michalek, J. J. Choudhary, R., Papalambros, P. 2002. Architectural Layout Design Optimization. Engineering Optimization.34 (5):461–84. doi: 10.1080/ 03052 1502 1 4 016.
Newton, D. (2019). Generative Deep Learning in Architectural Design. Technology|Architecture + Design. 3. 176-189. 10 .1080/ 2475 1448. 2019. 1 6 40536.
Ozerol, G., & Arslan Selçuk, S. Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020, (2022). International Journal of Architectural Computing. https://doi.org/10.1177_14780 771221100102.
Peters, N., Enabling Alternative Architectures: Collaborative Frameworks for Participatory Design, Harvard GSD Thesis, 2017.
Phillip, I., Zhu, J., Zhou, T., A. Efros, A., "Image-to-image translation with conditional adversarial networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125-1134. 2017.
Rahbar, M., Mahdavinejad, M., Bemanian, M., Davaie Markazi, AH., Hovestadt, L., (2019) Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs, Applied Artificial Intelligence, 33:8.
Rahbar, M., Mahdavinejad, M., Davaie Markazi, A.H., Bemanian, M., Architectural layout design through deep learning and agent-based modeling: A hybrid approach, Journal of Building Engineering, Volume 47, 2022.
Regateiro, F., Bento, J., Dias, J., Floor plan design using block algebra and constraint satisfaction, Advanced Engineering Informatics, Volume 26, Issue 2, 2012, Pages 361-382.
Rodrigues, E., Rodrigues, A., Gomes, ´A., Automated approach for design generation and thermal assessment of alternative floor plans, Energy and Buildings 81 (2014) 170–181, https://doi.org/10.1016/j.enbuild.2014.06.016.
Rosenman, M., Case-based evolutionary design, in: C. Fonlupt, J.-K. Hao, E. Lutton, M. Schoenauer, E. Ronald (Eds.), Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14, Cambridge University Press, 2000, pp. 17–29.
Russell, S. J., Norvig. P., 2016. Artificial Intelligence: A Modern Approach. 3rd ed. Boston: Pearson Education.
Thakur, M.K., Kumari, M., Das, M., Architectural layout planning using Genetic Algorithms, in: Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology. ICCSIT 2010. 4, 2010, pp. 5–11, https: //doi.org/ 10.1109/ ICCSIT. 2010 .5565165.
Topuz, B., & Çakici Alp, N. Machine learning in architecture. Automation in Construction, (2023). 154,105012.https://doi.org/10.1016/j.autcon.2023.105012Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004), “Image quality assessment: from error visibility to structural similarities on Image Processing, Vol. 13 No. 4, pp. 600-612.
Weber, R., Mueller, C., Reinhart, C., Automated floor plan generation in architectural design, Automation in Construction, Volume 140, 2022, 104385, ISSN 0926-5805, https://doi.org/ 10.1016/ j.autcon.2022. 104385.
Yi, H., Yi, Y.K., Performance based architectural design optimization: automated 3D space layout using simulated annealing, in: 2014 ASHRAE/IBPSA-USA Building Simulation Conference, 2014, pp. 292–299.http://www.scopus.com/inward/record.url? scp= 84938862924&partnerID=8YFLogxK.