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Structural Plan Schema Generation Through Generative Adversarial Networks

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Abstract

This paper suggests a workflow that generates floor plans with structural elements. Generating structural layouts in a BIM environment with the implementation of a machine learning method allows a future projection for fast and easy exploration of multiple design options. Pix2Pix, a Generative Adversarial Networks (GAN) model, takes the wall layout as input and generates a structural layout by learning from existing knowledge used to generate a decision support system for structural layout generation. The paper also suggest an additional script as a fine-adjustment model to refine the structural layout based on predetermined structural rules. This script increases the accuracy of the structural layouts generated by the GAN algorithm. Based on the test dataset, the research demonstrates a 64% success rate in providing structural schema assistance. Considering the results, this study seems to have the potential to be a supportive application in the early design phase.

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References

  • Alloghani, M., D. Al-Jumeily, J. Mustafina, A. Hussain, et al. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. https://doi.org/10.1007/978-3-030-22475-2_1

  • As, I., S. Pal, and P. Basu. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4): 306–327. https://doi.org/https://doi.org/10.1177/1478077118800982

    Article  Google Scholar 

  • Carta, S. (2021). Self-Organizing Floor Plans. Harvard Data Science Review, 3: 1–39. https://doi.org/https://doi.org/10.1162/99608f92.e5f9a0c7

    Article  Google Scholar 

  • Chaillou, S. (2020). ArchiGAN: Artificial Intelligence x Architecture. In X. Yuan, P. F., Xie, M., Leach, N., Yao, J., & Wang (Eds.), Architectural Intelligence, 117–127. https://doi.org/10.1007/978-981-15-6568-7_6

  • Chattopadhyay, C. (2022). Robin (repository of building plans). Retrieved February 4 2022, from https://github.com/gesstalt/ROBIN

  • Chen, X., Y. Duan, R. Houthooft, J. Schulman, et al. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. Advances in Neural Information Processing Systems, 2180–2188.

  • Goodfellow, I., Y. Bengio, A. Courville. (2016). Deep Learning. The MIT Press. https://doi.org/10.2172/1462436

  • Hofmeyer, H., and J. M. Davila Delgado. (2013). Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation. Advanced Engineering Informatics, 27(4): 427–443. https://doi.org/https://doi.org/10.1016/j.aei.2013.03.003

    Article  Google Scholar 

  • Isola, P., J. Y. Zhu, T. Zhou, and A. A. Efros. (2017). Image-to-image translation with conditional adversarial networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 5967–5976. https://doi.org/10.1109/CVPR.2017.632

  • Koning, H., and J. Eizenberg. (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/https://doi.org/10.1068/b080295

    Article  Google Scholar 

  • Lee, S., J. Ha, M. Zokhirova, H. Moon, and J. Lee. (2018). Background Information of Deep Learning for Structural Engineering. Archives of Computational Methods in Engineering, 25(1): 121–129. https://doi.org/https://doi.org/10.1007/s11831-017-9237-0

    Article  MathSciNet  Google Scholar 

  • Lee, Y., and S. H. Kim. (2016). Algorithmic Design Paradigm Utilizing Cellular Automata for the Han-ok. Nexus Network Journal, 18(2): 481–503. https://doi.org/https://doi.org/10.1007/s00004-016-0292-x

    Article  Google Scholar 

  • Liao, W., X. Lu, Y. Huang, Z. Zheng, et al. (2021). Automated structural design of shear wall residential buildings using generative adversarial networks. Automation in Construction, 132(February), 103931. https://doi.org/https://doi.org/10.1016/j.autcon.2021.103931

    Article  Google Scholar 

  • Lobos, D., and D. Donath. (2010). The problem of space layout in architecture: A survey and reflection. Arquiteturarevista, 6(2): 136–161. https://doi.org/https://doi.org/10.4013/arq.2010.62.05

    Article  Google Scholar 

  • Lopes, R., T. Tutenel, R. M. Smelik, K. J., de Kraker, et al. (2010). A constrained growth method for procedural floor plan generation. 11th International Conference on Intelligent Games and Simulation, GAME-ON 2010, January, 13–20.

  • Mao, X., Q. Li, H. Xie, R. Y. K. Lau, et al. (2017). Least Squares Generative Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, 2813–2821. https://doi.org/10.1109/ICCV.2017.304

  • Mirza, M., and S. Osindero (2014). Conditional Generative Adversarial Nets. 1–7. http://arxiv.org/abs/1411.1784

  • Nauata, N., S. Hosseini, K.-H. Chang, H. Chu, et al. (2021). House-GAN++: Generative Adversarial Layout Refinement Networks. http://arxiv.org/abs/2103.02574

  • Nimtawat, A., and P. Nanakorn. (2010). A genetic algorithm for beam-slab layout design of rectilinear floors. Engineering Structures, 32(11): 3488–3500. https://doi.org/https://doi.org/10.1016/j.engstruct.2010.07.018

    Article  Google Scholar 

  • Park, K.-W., and D. E. Grierson. (1999). Legriel, Pareto-Optimal Conceptual Design of the Structural Layout of Buildings Using a Multicriteria Genetic Algorithm, O. Computer- Aided Civil and Infrastructure Engineering, 14: 163–170.

    Article  Google Scholar 

  • Radford, A., L. Metz, and S. Chintala. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, 1–16.

  • Rahbar, M., M. Mahdavinejad, M. Bemanian, A. H. D. Markazi, et al. (2019). Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs. Applied Artificial Intelligence, 33(8): 689–705. https://doi.org/https://doi.org/10.1080/08839514.2019.1592919

    Article  Google Scholar 

  • Schön, D. A. (1987). Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions. Jossey-Bass.

  • Shaw, D., J. Miles, and A. Gray. (2008). Determining the structural layout of orthogonal framed buildings. Computers and Structures, 86: 1856–1864. https://doi.org/https://doi.org/10.1016/j.compstruc.2008.04.009

    Article  Google Scholar 

  • Stiny, G., and W. J. Mitchell. (1978). The Palladian grammar. Environment and Planning B, 5, 5–18. https://doi.org/https://doi.org/10.1068/b050005

    Article  Google Scholar 

  • Taborda, B., A. De Almeida, F. Santos, S. Eloy, et al. (2018). Shaper-GA: Automatic shape generation for modular house design. GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference, July, 937–942. https://doi.org/10.1145/3205455.3205609

  • URL-1: Retrieved January 10, 2024, from https://numpy.org/about/

  • URL-2: Retrieved January 10, 2024, from https://opencv.org/about/

  • Zhao, C. W., J. Yang, J., and J. Li. (2021). Generation of hospital emergency department layouts based on generative adversarial networks. In Journal of Building Engineering 43. Elsevier Ltd. https://doi.org/https://doi.org/10.1016/j.jobe.2021.102539

    Article  Google Scholar 

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Acknowledgements

The authors Kamile Öztürk Kösenciğ and Bahar Okuyucu are equal contributors to this research. Furthermore, the authors express their sincere gratitude to Alper Boray for his invaluable support and expertise in significantly enhancing the fine adjustment script, which played a crucial role in the outcome. His contributions are greatly appreciated.

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Authors and Affiliations

  1. Department of Architecture, Kocaeli University, Anitpark, 41050, İzmit, Kocaeli, Turkey

    Kamile Öztürk Kösenciğ

  2. Department of Architecture, Istanbul Technical University, Taşkışla, Istanbul, Turkey

    Elif Bahar Okuyucu

  3. Tilburg School of Economics and Management, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, Netherlands

    Özgün Balaban

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  1. Kamile Öztürk Kösenciğ
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  2. Elif Bahar Okuyucu
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  3. Özgün Balaban
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Correspondence to Kamile Öztürk Kösenciğ.

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Kösenciğ, K.Ö., Okuyucu, E.B. & Balaban, Ö. Structural Plan Schema Generation Through Generative Adversarial Networks. Nexus Netw J 26, 409–427 (2024). https://doi.org/10.1007/s00004-024-00766-z

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