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Preliminary Study on Architectural Skin Design Method Driven by Neural Style Transfer

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Beyond Digital Representation

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Abstract

With the development of new technologies and materials, architectural skins have become more complex and diverse, requiring a lot of effort to model different skins in architectural design. Artificial intelligence technology has a deep impact on different disciplines and can improve the efficiency of architectural skin design and inspire architects. In order to find new expressions of the building skin, it is necessary to focus on the dialogue between the architectural skin and the environment, and to innovate by breaking away from the patterned and formalized design. This work combines the development of artificial intelligence to initially explore the role of neural style transfer for architectural skin design, where the fusion of different styles of architectural images is programmed to generate another style, thus allowing architects to make clearer decisions and judgments. After performing neural style migration on five different types of buildings, the results obtained can present the new style form more completely, which provides new ideas and inspiration for the application of artificial intelligence in architectural design.

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References

  1. He, B., Ye, M., Yang, L., Fu, X., Mou, B., Griffy-Brown, C.: The combination of digital technology and architectural design to develop a process for enhancing energy-saving: the case of Maanshan China. Technol. Soc. 39, 77–87 (2014)

    Article  Google Scholar 

  2. Pena, M., Carballal, A., Rodríguez-Fernández, N., Santos, I., Romero, J.: Artificial intelligence applied to conceptual design. A review of its use in architecture. Autom. Constr. 124, 103550 (2021)

    Google Scholar 

  3. Sandaker, B., Kleven, B., Wang, A.: Structural typologies and the architectural space—studies of the relationship between structure and space by application of structural types to multistory buildings. Archit. Struct. Constr. 2, 199–221 (2022)

    Article  Google Scholar 

  4. Shahin, H.: Adaptive building envelopes of multistory buildings as an example of high performance building skins. Alexandria Eng. J. 58, 345–352 (2019)

    Article  Google Scholar 

  5. Bedon, C., Honfi, D., Machalicka, K., Eliasova, M., Vokac, M., Kozlowski, M., Wuest, T., Santos, F., Portal, N.: Structural characterisation of adaptive facades in Europe—Part I: Insight on classification rules, performance metrics and design methods. J. Build. Eng. 25, 100721 (2019)

    Article  Google Scholar 

  6. Sendi, M.: The effect of technology to integrate aesthetic desire of contemporary architecture with environmental principles in Façade design *. Int. Sci. J. Archit. Eng. 3, 1–7 (2016)

    Google Scholar 

  7. Böke, J., Knaack, U., Hemmerling, M.: Automated adaptive façade functions in practice—case studies on office buildings. Autom. Constr. 113, 103113 (2020)

    Google Scholar 

  8. Brown, N., Jusiega, V., Mueller, C.: Implementing data-driven parametric building design with a flexible toolbox. Autom. Constr. 118, 103252 (2020)

    Article  Google Scholar 

  9. Chi, N., Wang, J., Liao, J., Cheng, W., Chen, C.: Machine learning-based seismic capability evaluation for school buildings. Autom. Constr. 118, 103274 (2020)

    Article  Google Scholar 

  10. Jamei, M., Karbasi, M., Alawi, O., Kama, H., Khedher, K., Abba, S., Yaseen, Z.: Earth skin temperature long-term prediction using novel extended Kalman filter integrated with Artificial Intelligence models and information gain feature selection. Sustain. Comput. Informatics Syst. 35, 100721 (2022)

    Article  Google Scholar 

  11. Wang, H., Huan, J.: AGAN: Towards Automated Design of Generative Adversarial Networks. arXiv:1906.11080 (2019)

  12. Handa, A., Garg, P., Khare, V.: Masked neural style transfer using convolutional neural networks. In: 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), pp. 2099–2104. IEEE, Las Vegas, NV, USA (2018)

    Google Scholar 

  13. Newton, D.: Generative deep learning in architectural design. Technol. Archit. Des. 3, 176–189 (2019)

    Google Scholar 

  14. Silvestre, J., Ikeda, Y., Guéna, F.: Artificial imagination of architecture with deep convolutional neural network Laissez-faire: Loss of control in the esquisse phase. In: CAADRIA 2016, 21st International Conference on Computer-Aided Architectural Design Research in Asia—Living Systems and Micro-Utopias: Towards Continuous Designing, pp. 881–890 (2016)

    Google Scholar 

  15. Gatys, L., Ecker, A., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Pp. 2414–2423. IEEE, Las Vegas, NV, USA (2016)

    Google Scholar 

  16. Yoshimura, Y., Cai, B., Wang, Z., Ratti, C.: Deep learning architect: classification for architectural design through the eye of artificial intelligence. In: Lecture Notes in Geoinformation and Cartography, pp. 249–265 (2019)

    Google Scholar 

  17. Nazarinezhad, J., Dehghani, M.: A contextual-based segmentation of compact PolSAR images using Markov Random Field (MRF) model. Int J Remote Sens 40, 985–1010 (2019)

    Article  Google Scholar 

  18. Li, Y., Li, C., Li, X., Wang, K., Rahaman, M., Sun, C., Chen, H., Wu, X., Zhang, H., Wang, Q.: A comprehensive review of markov random field and conditional random field approaches in pathology image analysis. Arch. Comput. Methods Eng. 29(1), 609–639 (2022)

    Article  Google Scholar 

  19. Wang, Z., Liang, Q., Duarte, F., Zhang, F., Charron, L., Johnsen, L., Cai, B., Ratti, C.: Quantifying legibility of indoor spaces using deep convolutional neural networks: case studies in train stations. Build Environ. 160, 106099 (2019)

    Article  Google Scholar 

  20. Belém, C., Leitão, A. Santos, L., Leitão, A.: On the impact of machine learning: architecture without architects ? CAAD Futur, 247–293 (2019)

    Google Scholar 

  21. Nguyen, S., Tuyen, N., Phuc, N.: Deep feature rotation for multimodal image style transfer. In: 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), pp. 1–6. IEEE, Hanoi, Vietnam (2022)

    Google Scholar 

  22. Qian, K., Zhang, Y., Chang, S., Yang, X., Hasegawa-Johnson, M.: AUTOVC: zero-shot voice style transfer with only autoencoder loss. In: Proceedings of the 36th International Conference on Machine Learning, pp. 1–10, Long Beach, California (2019)

    Google Scholar 

  23. Huzaifah bin Md Shahrin, M., Wyse, L.: Applying visual domain style transfer and texture synthesis techniques to audio: insights and challenges. Neural. Comput. Appl. 32, 1051–1065 (2020)

    Google Scholar 

  24. Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. In: 5th International Conference on Learning Representations, ICLR 2017, pp. 1–26. arXiv:1610.07629 (2017)

  25. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, Italy (2017)

    Google Scholar 

  26. Wong, C., Xia, B., Peng, Q., Yuan, W., You, X.: MSN: Multi-Style Network for Trajectory Prediction, vol. 14, pp. 1–14 (2021). arXiv:2107.00932

  27. Rigter, M., Lacerda, B., Hawes, N.: RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning, arXiv:2204.12581 (2022)

  28. Dipasquale, L., Rovero, L., Fratini, F.: Ancient stone masonry constructions. In: Nonconventional and Vernacular Construction Materials, pp. 301–332 (2016)

    Google Scholar 

  29. Yi, Y.: Building facade multi-objective optimization for daylight and aesthetical perception. Build. Environ. 156, 178–190 (2019)

    Article  Google Scholar 

  30. Thenmozhi, K., Srinivasulu, R.: Crop pest classification based on deep convolutional neural network and transfer learning. Comput. Electron. Agric. 164, 104906 (2019)

    Article  Google Scholar 

  31. Gao, W., Li, Y., Yin, Y., Yang, M. Fast video multi-style transfer. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (WACV 2020), pp. 3211–3219. IEEE, Snowmass, CO, USA (2020)

    Google Scholar 

  32. Khan, M., Baig, D., Khan, U., Karim, A.: Malware classification framework using convolutional neural network. In: 1st Annual International Conference on Cyber Warfare and Security, ICCWS 2020 – Proceedings, pp. 1–7 (2020)

    Google Scholar 

  33. Xia, X., Xue, T., Lai, W., Sun, Z., Chang, A., Kulis, B., Chen, J.: Real-time localized photorealistic video style transfer. In: 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021, arXiv:2010.10056 (2021)

  34. Habib, N., Hasan, M., Reza, M., Rahman, M.: Ensemble of CheXNet and VGG-19 feature extractor with random forest classifier for pediatric pneumonia detection. SN Comput. Sci. 1, 1–9 (2020)

    Article  Google Scholar 

  35. Hu, Q., Cai, Y., Shi, Q., Xu, K., Yu, G., Ding, Z.: Iterative algorithm induced deep-unfolding neural networks: precoding design for multiuser MIMO systems. IEEE Trans. Wirel. Commun. 20, 1394–1410 (2021)

    Article  Google Scholar 

  36. Yigitcanlar, T., Corchado, J., Mehmood, R., Li, R., Mossberger, K., Desouza, K.: Responsible urban innovation with local government artificial intelligence (Ai): a conceptual framework and research agenda. J. Open Innov. Technol. Mark Complex 7, 1–16 (2021)

    Google Scholar 

  37. Caetano, I., Santos, L., Leitão, A.: Computational design in architecture: defining parametric, generative, and algorithmic design. Front. Archit. Res. 9, 287–300 (2020)

    Article  Google Scholar 

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Acknowledgements

Thanks to the editors for a rigorous process and significant contribution of the anonymous reviewers, which helped to produce a better article. The three authors discussed and determined the methods and data of the article together. Lu Xu mainly performed the specific operations of the neural style transfer algorithm, and his main contributions were focused on the second paragraph of the method section and the third paragraph application of neural style transfer in architectural skin design. Guiye Lin mainly contributed to the first paragraph by writing the introduction section, and the drawing of pictures. Andrea Giordano mainly contributed to the abstract and the fourth and fifth paragraphs by writing the results and conclusions, and the second paragraph.

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Authors and Affiliations

  1. Department of Civil, Environmental and Architectural Engineering, University of Padova, 35131, Padua, Italy

    Lu Xu, Guiye Lin & Andrea Giordano

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  1. Lu Xu
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  2. Guiye Lin
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  3. Andrea Giordano
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Corresponding author

Correspondence to Lu Xu .

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Editors and Affiliations

  1. DICEA, Università di Padova, Padova, Italy

    Andrea Giordano

  2. DSDRA, Sapienza Università di Roma, Roma, Italy

    Michele Russo

  3. DAD, Politecnico di Torino, Torino, Italy

    Roberta Spallone

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Xu, L., Lin, G., Giordano, A. (2024). Preliminary Study on Architectural Skin Design Method Driven by Neural Style Transfer. In: Giordano, A., Russo, M., Spallone, R. (eds) Beyond Digital Representation. Digital Innovations in Architecture, Engineering and Construction. Springer, Cham. https://doi.org/10.1007/978-3-031-36155-5_47

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  • DOI: https://doi.org/10.1007/978-3-031-36155-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36154-8

  • Online ISBN: 978-3-031-36155-5

  • eBook Packages: EngineeringEngineering (R0)

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