Exploration of Machine Learning Space Layout Generation of Apartments in Hamadan

Mahsa Hamouni ¹ ( Department of Architecture, Faculty of Art & Architecture, South Tehran Branch, Islamic Azad University, Tehran, Iran )
Hossein Soltanzadeh ² ( Associate Professor, Department of Architecture, Faculty of Art and Architecture, Central Tehran Branch, Islamic Azad University, Tehran, Iran. )
Hadi Ghoddusifar ³ ( Assistant Professor of Architecture, Department of Architecture, South Tehran, Islamic Azad University, Tehran, Iran. )
Muharram  Mansoorizadeh ⁴ ( Department of Computer Science, Faculty of Engineering, Bu-Ali Sina University, Hamadan, Iran )

Submited date : 2023-11-11 Accepted date : 2024-10-29

Keywords: Spatial layout design, Machine learning, Image&ndash, to-image translation, Conditional GAN,

Abstract :

Artificial intelligence technology has become an influential and trending topic in the field of architectural layout design. The core technology of artificial intelligence, machine learning, has attracted the attention of architects as a decision-making tool. The focus of many studies that apply machine learning to layout design is using the generative adversarial network (GAN), which generates space layout within a given boundary. Previous research demonstrates that training a GAN with labels can help a computer understand how spatial elements relate to each other and the logical relationship between spatial elements and boundaries. However, this paper applied conditional GAN to generate space layouts with given boundaries and supplementary conditions. The additional conditions provide designers control over the generated layout plans by satisfying both input boundary and user requirements. It also allows designers to generate different space layout plans within the same boundary. For this purpose, a specific dataset is created. The dataset consists of 660 apartment plans in Hamadan. We split the dataset into a training set and a test set. The training set includes 594 images, and the test set includes 66 (10%) of the images. After completing the process of training the model with the training set, we test the model by using the test set. Finally, the outputs of the model are evaluated based on quantitative and qualitative methods. The results show that the supplementary conditions provide further guidance to the model for space layout generation and reduce the image quality problems of the synthetic images.

References:

References
As, I., Pal, S., & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing. https:// doi.org /10.1177/14780771188 00 982.
Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9(2), 287-300. DOI:10.1016/j.foar.2019.12.008.
Cardoso Llach, D. (2015). Builders of the Vision: Software and the Imagination of Design (1st ed.). Routledge. 1-199. https://doi.org/ 10.4324/ 9781315798240.
Chaillou, S. (2019). Architecture, towards a new approach, Harvard. https://doi.org/10.9783/9781949057027-006.

Chang, K., Cheng, C., Luo, J., Murata, Sh., Nourbakhsh, M. & Tsuji, Y. (2021). Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation. https://doi.org/10.48550/arXiv .21 04. 13316.
Creswell, A., T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A., & Bharath. A. (2018). “Generative Adversarial Networks: An Overview.” IEEE Signal Processing Magazine 35, no. 1: 53–65. https:// doi.org/10.1109/MSP.2017.2765202.

Eisenstadt, V., Langenhan, C., Althoff, KD., & Dengel, A. (2020). Improved and Visually Enhanced Case-Based Retrieval of Room Configurations for Assistance in Architectural Design Education. In: Watson, I., Weber, R. (eds) Case-Based Reasoning Research and Development. ICCBR 2020. Lecture Notes in Computer Science(), vol 12311. Springer, Cham. https://doi.org/10.1007/978-3-030-58342-2_14.

Eisenstadt, V., Langenhan, C., & Althoff, K-D. (2019). Generation of Floor Plan Variations with Convolutional Neural Networks and Case-based Reasoning – An Approach for Unsupervised Adaptation of Room Configurations within a Framework for Support of Early Conceptual Design. 10.5151/proceedings-ecaadesigradi2019_648.

Ferrando, C. (2018). Towards a Machine Learning Framework in Space Syntax (Doctoral dissertation, Carnegie Mellon University). Retrieved from: https://kilthub.cmu.edu/articles/thesis/Towards_a_ Machine_Learning_Framework_in_Space_Syntax/7178417.

Goodfellow, I., Bengio, Y., & Courville, A. (2016) Deep Learning. MIT Press, Cambridge, MA. http://www.deeplearningbook.org.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative Adversarial Networks. Advances in Neural Information Processing Systems. 3. 10.1145/3422622.
Grover, R., Emmitt, S. & Copping, A. (2018). The typological learning framework: the application of structured precedent design knowledge in the architectural design studio. International Journal of Technology and Design Education. 28. 10.1007/s10798-017-9421-4.
Hong, T., Wang, Z., Luo, X., & Zhang, W. (2020). State-of-the-Art on Research and Applications of Machine Learning in the Building Life Cycle. Energy and Buildings. 212. 10.1016/j.enbuild.2020.109831.

Hu, R., Huang, Z., Tang, Y., Van Kaick, O. & Huang, H. (2020). Graph2Plan: learning floorplan generation from layout graphs. ACM Transactions on Graphics. 39. 10.1145/3386569.3392391.
Huang, J., Johanes, M., Chando Kim, F., Doumpioti, Ch. & Holz, G. (2021). On GANs, NLP and Architecture: Combining Human and Machine Intelligences for the Generation and Evaluation of Meaningful Designs, Technology|Architecture + Design, 5:2, 207-224, DOI: 10.1080/24751448.2021.1967060.
Huang, W., & Zheng, H. (2018). Architectural Drawings Recognition and Generation through Machine Learning. Doi:10.52842 /conf.acad ia.2018.156.
Isola, Ph., Zhu, J-Y., Zhou, T., & Efros, A. (2017). Image-to-Image Translation with Conditional Adversarial Networks. 5967-5976.10.11 09/CVPR.2017.632.
Karadag, I., Zeynel Güzelci, O., & Alaçam, S. (2022). "EDU-AI: a twofold machine learning model to support classroom layout generation", Construction Innovation. 10.1108/CI-02-2022-0034.

Lawson, B., & Dorst, K. (2009). Design Expertise (1st ed.). Routledge. https://doi.org/10.4324/9781315072043.
Liu, C., Schwing, A.G., Kundu, K., Urtasun, R., & Fidler, S. (2015). 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 /10.11 09 / CVPR.2015.7298963.

Liu, Y., Fang, Ch., Yang, Z., Wang, X., Zhou, Z., Deng, Q. & Liang, L. (2022). Exploration on Machine Learning Layout Generation of Chinese Private Garden in Southern Yangtze. In: Yuan, P.F., Chai, H., Yan, C., Leach, N. (eds) Proceedings of the 2021 DigitalFUTURES. CDRF 2021. Springer, Singapore. https://doi.org/10.1007/978-981-16-5983-6_4.

Michalek, J. Choudhary, R., Papalambros, P. (2002). Architectural Layout Design Optimization. Engineering Optimization.34 (5):461–84. Doi: 10.1080/ 03052 1502 1 4 016.

McCulloch, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 115–133. http://dx.doi.org/10.1007/BF02478259.
Merrell, P., Schkufza, E., & Koltun, V. (2010). Computer-generated residential building layouts, ACM Transactions on Graphics 29. 1–12, https://doi.org/ 10.1145/1882261.1866203.
Nauata, N., Chang, K., Cheng, C., Mori, G & Furukawa, Y. (2020). House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation. 10.1007/978-3-030-58452-8_10.
Nauata, N., Hosseini, S., Chang, K., Chu, H., Cheng, C. & Furukawa, Y. (2021). House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects. 13627-13636.10.1109/CVPR46437.2021.01 342.
Newton, D. (2019). Generative Deep Learning in Architectural Design. Technology|Architecture + Design. 3. Doi: 176-189. 10 .1080/ 2475 1448. 2019. 1 6 40536.

Olgiati, V., (2013). The images of architects. Publisher: Quart Publishers. Editor: Valerio Olgiati. Pages: 424. Language: English. ISBN: 978-3-03761-069-5.
Özerol, G., & Arslan Selçuk, S. (2022). Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020. International Journal of Architectural Computing. https://doi.org/10.1177_14780771221100102.
Peters, N., Enabling Alternative Architectures: Collaborative Frameworks for Participatory Design, Harvard GSD Thesis, 2017.

Purcell, T., & Gero, J. (1998). Drawings and the design process. Design Studies. DOI 19. 10.1016/S0142-694X(98)00015-5.
Rahbar, M., Mahdavinejad, M., Bemanian, M., Davaie Markazi, A.H. & Hovestadt, L. (2019). Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs. Applied Artificial Intelligence. 33. 1-17. https:// 10.1080/08839514 .2019.159 2919.
Rahbar, M., Mahdavinejad, M., Markazi, A. H., & Bemanian, M. (2022). Architectural layout design through deep learning and agent-based modeling: A hybrid approach. Journal of Building Engineering, 47, 103822. https://doi.org/10.1016/j.jobe.2021.103822.
Rodrigues, R. C., & Duarte, R. B. (2022). Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes. International Journal of Architectural Computing.

Rodrigues, R. C., & Duarte, R. B. (2022). Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes. International Journal of Architectural Computing. https:// doi.org/10.1177_14780771221120842.

Uzun, C., Çolakoğlu, M., Inceoğlu, A. (2020). GAN as a generative architectural plan layout tool: A case study for training DCGAN with Palladian Plans and evaluation of DCGAN outputs. A|Z ITU Journal of Faculty of Architecture. 17. 185-198. 10.5505/itujfa.2020.54037.

Wang, T.-C., Liu, Y., Zhu, J., Tao, A., Kautz, J., & Catanzaro, B. (2018). “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Doi: 10.1109/CVPR.2018.00917.https://arxiv.org/ abs/17 1 1.11585.

Wang, Z., Bovik, A.C., Sheikh, H.R., & Simoncelli, E.P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. Image Processing, IEEE Transactions on. 13. 600 - 612. 10.1109/TIP.2003.819861.
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Thesis (Ph. D.). Appl. Math. Harvard University.
Whitley, D. (1994). A Genetic Algorithm Tutorial. Statistics and Computing, 4, 65-85. https://doi.org/10.1007/BF00175354.

Wu, W., Xiao-Ming, Fu., Tang, R., Wang, Y., Qi, Y & Liu, L. (2019). Data-driven interior plan generation for residential buildings. ACM Transactions on Graphics. 38. Doi: 234:1-234:12.10.1145 /3355089. 3356556.
Yoon, S-w., Kim, I. & Lee, K-C. (2018). The Architectural Design of Storage System for Power Data Management. 736-738. 10.1109/BigComp.2018.00139.
Zhao, C., Yang, J., & Xiong, W. et al. (2021). Two Generative Design Methods of Hospital Operating Department Layouts Based on Healthcare Systematic Layout Planning and Generative Adversarial Network. J. Shanghai Jiaotong Univ. (Sci.) 26, 103–115 https://doi.org/10.1007/s12204-021-2265-9.
Zheng, H., & Yuan, P. F. (2021). A generative architectural and urban design method through artificial neural networks. Building and Environment, 205, 108178. https://doi.org/10.1016/j.buildenv.2021.108178.
Zheng, H., An, K., Wei, J., & Ren, Y. (2020). Apartment Floor Plans Generation via Generative Adversarial Networks. In Proceedings of the RE: Anthropocene, Design in the Age of Humans: Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2020), Chulalongkorn University, Bangkok, Thailand. 10.52842/conf.caadria.2020.2.599.