A Research on the Use of Machine Learning on Building Facades

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Ezgi Günay
Seher Güzelçoban Mayuk


Artificial intelligence and its sub-branch, machine learning technologies, have developed rapidly in recent years and their use for various purposes has seemed to be increased in various sectors from automotive to medicine, from law to marketing. Similarly, these technologies have begun to be used in the building sector and in the field of architecture. These technologies are being used in many fields in architecture such as feasibility studies, building design, project control, occupational safety, earthquake resistant building design and applications, energy efficient system design, construction with smart construction equipment, smart building design, and smart facade design. Despite this increasing use in the field, it has been determined by the literature review that the number of studies focusing on the use of machine learning in architecture, especially on building facades, is low. In this sense in the study, it is aimed to examine the relationship between artificial intelligence and machine learning technologies with architecture in the context of building facades. Initially in the study the topics and the historical process related to artificial intelligence and machine learning were explained, subsequently the use of technologies on the building facades was examined through examples. In this way, a guiding resource has been created for those who want to work on this subject in the future.


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Günay, E., & Güzelçoban Mayuk, S. (2022). A Research on the Use of Machine Learning on Building Facades . The European Journal of Research and Development, 2(2), 224–240. https://doi.org/10.56038/ejrnd.v2i2.63


Abediniangerabi, B., Makhmalbaf, A., & Shahandashti, M. (2021). Deep learning for estimating energy savings of early-stage facade design decisions. Energy and AI, 5, 100077. DOI: https://doi.org/10.1016/j.egyai.2021.100077

Aydın, İ. H. & Değirmenci, C. H. (2018). Yapay Zekâ. İstanbul: Girdap Yayınları

Aznar, F., Echarri, V., Rizo, C., & Rizo, R. (2018). Modelling the thermal behaviour of a building facade using deep learning. Plos one, 13(12), e0207616. DOI: https://doi.org/10.1371/journal.pone.0207616

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798-1828. DOI: https://doi.org/10.1109/TPAMI.2013.50

Bhamare, D. K., Saikia, P., Rathod, M. K., Rakshit, D., & Banerjee, J. (2021). A machine learning and deep learning based approach to predict the thermal performance of phase change material integrated building envelope. Building and Environment, 199, 107927. DOI: https://doi.org/10.1016/j.buildenv.2021.107927

Bingöl, K., Er, Akan, A., Örmecioğlu, H. T., & Arzu, E. R. (2020). Depreme dayanıklı mimari tasarımda yapay zeka uygulamaları: Derin öğrenme ve görüntü işleme yöntemi ile düzensiz taşıyıcı sistem tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(4), 2197-2210. DOI: https://doi.org/10.17341/gazimmfd.647981

Cha, G. W., Moon, H. J., Kim, Y. M., Hong, W. H., Hwang, J. H., Park, W. J., & Kim, Y. C. (2020). Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets. International Journal of Environmental Research and Public Health, 17(19), 6997. DOI: https://doi.org/10.3390/ijerph17196997

Chollet, F. (2018). Deep Learning with Python. 1st edition. Manning Publications Co. 20 Baldwin Road PO Box 761 Shelter Island, NY 11964, 10-12.

Dhariwal, J. & Banerjee, R. (2015). Naturally ventilated building design under uncertainty using design of experiments. In Building Simulation Conference, 1708-1715.

Ertel, W. (2009). Under Graduate Topics Computer Sience: Introduction to Artificial Intelligence. Springer London Dordrecht Heidelberg New York, 221.

Friedman, J.H. (1998). Data Mining and Statistics: What's the connection?. Computing Science and Statistics. 29, 3-9.

Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc.

Han, Y., Fan, C., Geng, Z., Ma, B., Cong, D., Chen, K., & Yu, B. (2020). Energy Efficient Building Envelope Using Novel RBF Neural Network İntegrated Affinity Propagation. Energy, 209, 118414. DOI: https://doi.org/10.1016/j.energy.2020.118414

Kim, J., Jung, J. H., Kim, S. J., & Kim, S. A. (2018). Multi-Factor Optimization Method through Machine Learning in Building Envelope Design: Focusing on Perforated Metal Façade. International Journal of Architectural and Environmental Engineering, 11(11), 1602-1609.

Koza, J. R., Bennett, F. H., Andre, D., & Keane, M. A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht, 151-170. DOI: https://doi.org/10.1007/978-94-009-0279-4_9

Kuru, A., Fiorito, F., Oldfield, P., & Bonser, S. P. (2018). Multi-functional biomimetic adaptive façades: A case study. In Proceedings of the FACADE 2018 Final Conference of COST TU1403 Adaptive Facades Network, Lucerne, Switzerland (pp. 26-27).

MacKnight, P. (2018). The Learning Facade. PhD Thesis, The University of North Carolina at Charlotte.

Masiero, A., & Costantino, D. (2019). TLS For Detecting Small Damages On A Building Façade. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42. DOI: https://doi.org/10.5194/isprs-archives-XLII-2-W11-831-2019

Melo, A. P., Cóstola, D., Lamberts, R., & Hensen, J. L. M. (2014). Development of surrogate models using artificial neural network for building shell energy labelling. Energy Policy, 69, 457-466. DOI: https://doi.org/10.1016/j.enpol.2014.02.001

Moghtadernejad, S., Chouinard, L. E., & Mirza, M. S. (2021). Enhanced façade design: A data-driven approach for decision analysis based on past experiences. Developments in the Built Environment, 5, 100038. DOI: https://doi.org/10.1016/j.dibe.2020.100038

Nabiyev, V.V. (2016). Yapay Zeka: İnsan-Bilgisayar Etkileşimi. 3. baskı, Seçkin Yayıncılık, Sözkesen Matbaacılık: Ankara, 2-55.

Nishida, G., Bousseau, A., & Aliaga, D. G. (2018). Procedural modeling of a building from a single image. In Computer Graphics Forum (Vol. 37, No. 2, pp. 415-429). DOI: https://doi.org/10.1111/cgf.13372

Oskouie, P., Becerik-Gerber, B., & Soibelman, L. (2017). Automated recognition of building façades for creation of As-Is Mock-Up 3D models. Journal of Computing in Civil Engineering, 31(6), 04017059. DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000711

Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2018). Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied energy, 211, 1343-1358. DOI: https://doi.org/10.1016/j.apenergy.2017.12.002

Pirim, H. (2011). Yapay Zeka. Journal of Yaşar University, 81-93.

Ross, T. J., Sorensen, H. C., Savage, S. J., & Carson, J. M. (1990). DAPS: Expert System for Structural Damage Assessment. Journal of Computing in Civil Engineering, 4(4), 327–348. doi:10.1061/(asce)0887-3801(1990)4:4(327) DOI: https://doi.org/10.1061/(ASCE)0887-3801(1990)4:4(327)

Saltık, E. (2021). Genetik Algoritmalar Kullanılarak Güneş Işınımı ve Gölgeye Göre Optimal Yüksek Yapı Form Önerileri Üretilmesi. Journal of Computational Design, 2(2), 25-50. DOI: https://doi.org/10.53710/jcode.984567

Sucu, İ. (2019). Yapay Zekanın Toplum Üzerindeki Etkisi ve Yapay Zeka (AI) Filmi Bağlamında Yapay Zekaya Bakış. Uluslararası Ders Kitapları ve Eğitim Materyalleri Dergisi, 2(2), 203-215.

Yurtcu Ş. & Özocak A. (2016). Prediction of compression index of fine-grained soils using statistical and artificial intelligence methods, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 597-608.

Zhou, Y., Zheng, S., & Zhang, G. (2020). A review on cooling performance enhancement for phase change materials integrated systems—flexible design and smart control with machine learning applications. Building and Environment, 174, 106786. DOI: https://doi.org/10.1016/j.buildenv.2020.106786

Web 1. (2021). https://www.ahr.co.uk/Al-Bahr-Towers , Accessed on: 8/01/2022

Web 2. (2021). https://airworks.io, Accessed on: 15/06/2021

Web 3. (2021). https://www.archdaily.com/270592/al-bahar-towers-responsive-facade-aedas, Accessed on: 28/01/2022

Web 4. (2021). https://www.builtrobotics.com, Accessed on: 15/06/2021

Web 5. (2021). https://en.wikiarquitectura.com/building/al-bahar-towers/ , Accessed on: 14/11/2021

Web 6. (2021). https://www.energis.cloud/wp/sensorea-optimises-energy-water-costs-at-the-hotel-brussels-using-energis-cloud/ , Accessed on: 06/06/2021

Web 7. (2021). https://medium.com/türkiye/yapay-zekanın-tarihçesi-ve-gelişim-sürecicb4c73deb01d, Accessed on: 10/05/2021

Web 8. (2021). https://www.openspace.ai, Accessed on: 11/06/2021

Web 9. (2021). https://www.som.com/projects/central_place_sydney /, Accessed on: 06/06/2021

Web 10. (2021). https://www.spacemakerai.com, Accessed on: 11/06/2021

Web 11. (2022). https://web11otics.com/heavy-picker, Accessed on: 10/05/2022