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Bibliometric Analysis on Artificial Intelligence Aided Architectural Design

Year 2024, Volume: 6 Issue: 2, 231 - 245, 20.12.2024
https://doi.org/10.46474/jds.1525949

Abstract

Technological advancements have created new dimensions in various fields, including architecture. The widespread use of information technology has particularly transformed architectural designs. In architectural design processes, methods and tools have diversified beyond traditional approaches, incorporating computer-aided programs, computational functions with plugins, and even contemporary artificial intelligence (AI)-supported software and applications tailored to specific needs. It is inevitable that AI-supported designs will increasingly feature in the future of architecture. The purpose of this study is to determine the role of AI applications in architectural design. To this end, a bibliometric analysis was conducted on 137 articles related to artificial intelligence and architectural design from the period 1991-2024, sourced from the Web of Science (WoS) database. The analysis focused on main topics, keywords, authors, sources, highly cited articles, and countries. The selected articles were analyzed using VOSviewer to examine the use of AI applications in architectural design. Quantitative analyses providing an overview of the topic are presented in tables, graphs, and maps. It has been determined that at least 30% of the studies were conducted in the field of architecture. It was found that studies on the subject have rapidly increased since 2020. The continued growth of AI usage across all fields is expected to extend into architectural design as well. Therefore, it is deemed necessary to address AI in architectural design education to align with these advancements. Finally, the paper discusses forward-looking recommendations on how to incorporate AI into architectural design education.

References

  • As, I., Pal, S. & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4), 306-327.
  • Başarır, L. (2022). Modelling AI in architectural education. Gazi University Journal of Science, 35(4), 1260-1278.
  • Bottazzi, R., Hosmer, T. & Claypool, M. (2024). Disruptive ecologies: design with nonhuman intelligences, Architectural Design, 94(1), 30-37.
  • Cudzik, J. & Radziszewski, K. (2018). Artificial intelligence aided architectural design. 36th International Conference on Education and Research in Computer Aided Architectural Design in Europe: Computing for a Better Tomorrow, Lodz, Poland, 77-84.
  • Durán-Sánchez, A., Álvarez-García, J., del Río-Rama, M. D. L. C. & Oliveira, C. (2018). Religious tourism and pilgrimage: Bibliometric overview. Religions, 9(9), 249.
  • Do, E.Y.L. & Gross, M.D. (2001). Thinking with diagrams in architectural design. Artificial Intelligence Review, 15(1), 135-149.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Escobar, R. & De la Rosa, A. (2003). Architectural design for the survival optimization of panicking fleeing victims. 7th European Conference on Artifical Life, 2801, 97-106.
  • Fahimnia, B., Sarkis, J. & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114.
  • Güç, B. & Karadayı, A. (2007). Web üzerinden etkileşimli bir model önerisi üniversite kampüsü örneği. TMMOB Harita ve Kadastro Mühendisleri Odası Ulusal Coğrafi Bilgi Sistemleri Kongresi, Trabzon.
  • González, A.F. & Garcia, M. (2024). A posthuman architectural artificial intelligence speculum? text and images in future spaces. Architectural Design, 94(1), 22-29.
  • Jang, S., Lee, G., Oh, J., Lee, J. & Koo, B. (2024). Automated detailing of exterior walls using NADIA: Natural-language-based architectural detailing through interaction with AI. Advanced Engineering Informatics, 61, 102532.
  • Jin, S.T., Tu, H.J., Li, J.F., Fang, Y.W., Qu, Z., Xu, F., Liu, K. & Lin, Y.Q. (2024). Enhancing architectural education through artificial intelligence: a case study of an AI-assisted architectural programming and design course. Buildings, 14(6), 1613.
  • Jo, H., Lee, J.K., Lee, Y.C. & Choo, S. (2024). Generative artificial intelligence and building design: early photorealistic render visualization of facades using local identity-trained models. Journal of Computatıonal Design and Engineering, 11(2), 85-105.
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
  • Jurshari, M.Z., Tazakor, M.Y. & Yeganeh, M. (2024). Optimizing the dimensional ratio and orientation of residential buildings in the humid temperate climate to reduce energy consumption (Case: Rasht Iran). Case Studies in Thermal Engineering, 59, 104484.
  • Kakooee, R. & Dillenburger, B. (2024). Reimagining space layout design through deep reinforcement learning. Journal of Computatıonal Design and Engineering, 11(3), 43-55.
  • Karimi, H., Adibhesami, M.A., Hoseinzadeh, S., Salehi, A., Groppi, D. & Garcia, D.A. (2024). Harnessing deep learning and reinforcement learning synergy as a form of strategic energy optimization in architectural design: a case study in Famagusta, North Cyprus. Buildings, 14(5), 1342.
  • Lawson, B. (2005). How designers think (4th ed.) Burlington: Architectural Press.
  • McDermott, J., Swafford, J.M., Hemberg, M., Byrne, J., Hemberg, E., Fenton, M., McNally, C., Shotton, E. & O'Neill, M. (2012). String-rewriting grammars for evolutionary architectural design. Environment and Planning B-Planning & Design, 39(4), 713-731.
  • Ploszaj-Mazurek, M. & Rynska, E. (2024). Artificial intelligence and digital tools for assisting low-carbon architectural design: merging the use of machine learning, large language models, and building information modeling for life cycle assessment tool development. Energies, 17(12), 2997.
  • Simon, H.A. (1996). The sciences of the artificial (3rd ed.). Cambridge: MIT Press.
  • Su, Z.Z. & Yan, W. (2015). A fast genetic algorithm for solving architectural design optimization problems. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 29(4), 457-469.
  • Sukkar, A.W., Fareed, M.W., Yahia, M.W., Mushtaha, E. & De Giosa, S.L. (2024). Artificial intelligence islamic architecture (AIIA): what is islamic architecture in the age of artificial intelligence?. Buildings, 14(3), 781.
  • Tan, L.N. & Luhrs, M. (2024). Using generative AI midjourney to enhance divergent and convergent thinking in an architect's creative design process. Design Journal, 27(4), 677-699.
  • Tranfield, D., Denyer, D. & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
  • Velasco, C. A. B., Parra, V. F. G. & García, C. Q. (2011). Evolution of the literature on family business as a scientific discipline. Cuadernos De Economía y Dirección De La Empresa, 14(2), 78-90.
  • Visser, W. (2006). The cognitive artifacts of designing. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Wang, L.K., Janssen, P. & Ji, G.H. (2020). SSIEA: A hybrid evolutionary algorithm for supporting conceptual architectural design. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 34(4), 458-476.
  • Werner, S. & Long, P. (2003). Cognition meets Le Corbusier - Cognitive principles of architectural design. Spatial Cognition III, 2685, 112-126.
  • Wortmann, T., Costa, A., Nannicini, G. & Schroepfer, T. (2015). Advantages of surrogate models for architectural design optimization. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 29(4), 471-481.
  • Yan, L.A., Chen, Y.L., Zheng, L. & Zhang, Y. (2024). Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: a case study of the classical gardens of Suzhou. Heritage Science, 12(1), 72.
  • Yıldırım, T., Özen Yavuz, A. & İnan, N. (2010). Mimari tasarım eğitiminde geleneksel ve dijital görselleştirme teknolojilerinin karşılaştırılması. Gazi Üniversitesi Bilişim Enstitüsü Bilişim Teknolojileri Dergisi, 3(3).
  • Yi, H. (2020). Visualized co-simulation of adaptive human behavior and dynamic building performance: an agent-based model (ABM) and artificial intelligence (AI) approach for smart architectural design. Sustainability, 12(16), 6672.
  • Yi, H. & Kim, Y. (2021). Self-shaping building skin: Comparative environmental performance investigation of shape-memory-alloy (SMA) response and artificial-intelligence (AI) kinetic control. Journal of Building Engineering, 35, 102113.
  • Yo, Z. (2020). Visualizing artificial intelligence used in education over two decades. Journal of Information Technology Research, 13(4), 32–46.
  • Zhang, Z., Fort, J.M. & Giménez Mateu, L. (2023). Exploring the potential of artificial ıntelligence as a tool for architectural design: a perception study using Gaudí’s works. Buildings 2023, 13, 1863.
  • Zhang, Z.H., Fort, J.M. & Mateu, L.G. (2024). Decoding emotional responses to AI-generated architectural imagery. Frontıers in Psychology, 15, 1348083.
  • Zhao, L., Song, D.X., Chen, W.Z. & Kang, Q. (2024). Coloring and fusing architectural sketches by combining a Y-shaped generative adversarial network and a denoising diffusion implicit model. Computer-Aided Civil and Infrastructure Engineering, 39(7), 1003-1018.
  • Zou, X., Yue, W. L. & Le Vu, H. (2018). Visualization and analysis of mapping knowledge domain of road safety studies. Accident Analysis & Prevention, 118, 131-145.
Year 2024, Volume: 6 Issue: 2, 231 - 245, 20.12.2024
https://doi.org/10.46474/jds.1525949

Abstract

References

  • As, I., Pal, S. & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4), 306-327.
  • Başarır, L. (2022). Modelling AI in architectural education. Gazi University Journal of Science, 35(4), 1260-1278.
  • Bottazzi, R., Hosmer, T. & Claypool, M. (2024). Disruptive ecologies: design with nonhuman intelligences, Architectural Design, 94(1), 30-37.
  • Cudzik, J. & Radziszewski, K. (2018). Artificial intelligence aided architectural design. 36th International Conference on Education and Research in Computer Aided Architectural Design in Europe: Computing for a Better Tomorrow, Lodz, Poland, 77-84.
  • Durán-Sánchez, A., Álvarez-García, J., del Río-Rama, M. D. L. C. & Oliveira, C. (2018). Religious tourism and pilgrimage: Bibliometric overview. Religions, 9(9), 249.
  • Do, E.Y.L. & Gross, M.D. (2001). Thinking with diagrams in architectural design. Artificial Intelligence Review, 15(1), 135-149.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  • Escobar, R. & De la Rosa, A. (2003). Architectural design for the survival optimization of panicking fleeing victims. 7th European Conference on Artifical Life, 2801, 97-106.
  • Fahimnia, B., Sarkis, J. & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114.
  • Güç, B. & Karadayı, A. (2007). Web üzerinden etkileşimli bir model önerisi üniversite kampüsü örneği. TMMOB Harita ve Kadastro Mühendisleri Odası Ulusal Coğrafi Bilgi Sistemleri Kongresi, Trabzon.
  • González, A.F. & Garcia, M. (2024). A posthuman architectural artificial intelligence speculum? text and images in future spaces. Architectural Design, 94(1), 22-29.
  • Jang, S., Lee, G., Oh, J., Lee, J. & Koo, B. (2024). Automated detailing of exterior walls using NADIA: Natural-language-based architectural detailing through interaction with AI. Advanced Engineering Informatics, 61, 102532.
  • Jin, S.T., Tu, H.J., Li, J.F., Fang, Y.W., Qu, Z., Xu, F., Liu, K. & Lin, Y.Q. (2024). Enhancing architectural education through artificial intelligence: a case study of an AI-assisted architectural programming and design course. Buildings, 14(6), 1613.
  • Jo, H., Lee, J.K., Lee, Y.C. & Choo, S. (2024). Generative artificial intelligence and building design: early photorealistic render visualization of facades using local identity-trained models. Journal of Computatıonal Design and Engineering, 11(2), 85-105.
  • Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
  • Jurshari, M.Z., Tazakor, M.Y. & Yeganeh, M. (2024). Optimizing the dimensional ratio and orientation of residential buildings in the humid temperate climate to reduce energy consumption (Case: Rasht Iran). Case Studies in Thermal Engineering, 59, 104484.
  • Kakooee, R. & Dillenburger, B. (2024). Reimagining space layout design through deep reinforcement learning. Journal of Computatıonal Design and Engineering, 11(3), 43-55.
  • Karimi, H., Adibhesami, M.A., Hoseinzadeh, S., Salehi, A., Groppi, D. & Garcia, D.A. (2024). Harnessing deep learning and reinforcement learning synergy as a form of strategic energy optimization in architectural design: a case study in Famagusta, North Cyprus. Buildings, 14(5), 1342.
  • Lawson, B. (2005). How designers think (4th ed.) Burlington: Architectural Press.
  • McDermott, J., Swafford, J.M., Hemberg, M., Byrne, J., Hemberg, E., Fenton, M., McNally, C., Shotton, E. & O'Neill, M. (2012). String-rewriting grammars for evolutionary architectural design. Environment and Planning B-Planning & Design, 39(4), 713-731.
  • Ploszaj-Mazurek, M. & Rynska, E. (2024). Artificial intelligence and digital tools for assisting low-carbon architectural design: merging the use of machine learning, large language models, and building information modeling for life cycle assessment tool development. Energies, 17(12), 2997.
  • Simon, H.A. (1996). The sciences of the artificial (3rd ed.). Cambridge: MIT Press.
  • Su, Z.Z. & Yan, W. (2015). A fast genetic algorithm for solving architectural design optimization problems. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 29(4), 457-469.
  • Sukkar, A.W., Fareed, M.W., Yahia, M.W., Mushtaha, E. & De Giosa, S.L. (2024). Artificial intelligence islamic architecture (AIIA): what is islamic architecture in the age of artificial intelligence?. Buildings, 14(3), 781.
  • Tan, L.N. & Luhrs, M. (2024). Using generative AI midjourney to enhance divergent and convergent thinking in an architect's creative design process. Design Journal, 27(4), 677-699.
  • Tranfield, D., Denyer, D. & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222.
  • Velasco, C. A. B., Parra, V. F. G. & García, C. Q. (2011). Evolution of the literature on family business as a scientific discipline. Cuadernos De Economía y Dirección De La Empresa, 14(2), 78-90.
  • Visser, W. (2006). The cognitive artifacts of designing. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Wang, L.K., Janssen, P. & Ji, G.H. (2020). SSIEA: A hybrid evolutionary algorithm for supporting conceptual architectural design. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 34(4), 458-476.
  • Werner, S. & Long, P. (2003). Cognition meets Le Corbusier - Cognitive principles of architectural design. Spatial Cognition III, 2685, 112-126.
  • Wortmann, T., Costa, A., Nannicini, G. & Schroepfer, T. (2015). Advantages of surrogate models for architectural design optimization. Al Edam-Artifıcial Intelligence for Engineering Design Analysis and Manufacturing, 29(4), 471-481.
  • Yan, L.A., Chen, Y.L., Zheng, L. & Zhang, Y. (2024). Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: a case study of the classical gardens of Suzhou. Heritage Science, 12(1), 72.
  • Yıldırım, T., Özen Yavuz, A. & İnan, N. (2010). Mimari tasarım eğitiminde geleneksel ve dijital görselleştirme teknolojilerinin karşılaştırılması. Gazi Üniversitesi Bilişim Enstitüsü Bilişim Teknolojileri Dergisi, 3(3).
  • Yi, H. (2020). Visualized co-simulation of adaptive human behavior and dynamic building performance: an agent-based model (ABM) and artificial intelligence (AI) approach for smart architectural design. Sustainability, 12(16), 6672.
  • Yi, H. & Kim, Y. (2021). Self-shaping building skin: Comparative environmental performance investigation of shape-memory-alloy (SMA) response and artificial-intelligence (AI) kinetic control. Journal of Building Engineering, 35, 102113.
  • Yo, Z. (2020). Visualizing artificial intelligence used in education over two decades. Journal of Information Technology Research, 13(4), 32–46.
  • Zhang, Z., Fort, J.M. & Giménez Mateu, L. (2023). Exploring the potential of artificial ıntelligence as a tool for architectural design: a perception study using Gaudí’s works. Buildings 2023, 13, 1863.
  • Zhang, Z.H., Fort, J.M. & Mateu, L.G. (2024). Decoding emotional responses to AI-generated architectural imagery. Frontıers in Psychology, 15, 1348083.
  • Zhao, L., Song, D.X., Chen, W.Z. & Kang, Q. (2024). Coloring and fusing architectural sketches by combining a Y-shaped generative adversarial network and a denoising diffusion implicit model. Computer-Aided Civil and Infrastructure Engineering, 39(7), 1003-1018.
  • Zou, X., Yue, W. L. & Le Vu, H. (2018). Visualization and analysis of mapping knowledge domain of road safety studies. Accident Analysis & Prevention, 118, 131-145.
There are 40 citations in total.

Details

Primary Language English
Subjects Architecture (Other), Information Technology in Design
Journal Section Research Articles
Authors

Fulya Pelin Cengizoğlu 0000-0002-9133-6858

Early Pub Date December 13, 2024
Publication Date December 20, 2024
Submission Date August 1, 2024
Acceptance Date September 21, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Cengizoğlu, F. P. (2024). Bibliometric Analysis on Artificial Intelligence Aided Architectural Design. Journal of Design Studio, 6(2), 231-245. https://doi.org/10.46474/jds.1525949

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