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The Use and Development of Artificial Intelligence in Architectural Design Processes

Year 2024, , 1347 - 1360, 15.11.2024
https://doi.org/10.34248/bsengineering.1559637

Abstract

Artificial intelligence is widely used as an interactive technology in various professional disciplines. The widespread use of these technologies, which we benefit from in most areas of our lives, in the education sector will provide important developments in the field of education. The main purpose of this study is to analyze the existing studies in which the use of artificial intelligence helps in architectural design processes. In the study, identification, screening, eligibility, inclusion, and data analysis processes were carried out in three search engines such as Web of Science, ScienceDirect, and ULAKBIM. While reporting the research, ‘Systematic Literature Review’ and ‘Preferred Reporting Items for Meta-Analysis’ protocols were followed and a total of 35 relevant articles were identified. In the research, three popular Artificial Intelligence applications used in architectural design processes were identified as Generative Adversarial Networks (GAN), Machine Learning, and Data Mining. In addition, Systematic Literature Review (SLR) outputs show that most researchers are supported by artificial intelligence applications in architectural design processes. As a result of the research, it was determined that artificial intelligence is widely used in architectural design processes, however, it has positive effects in 3D and animation parts.

References

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  • Akdemi̇r N. 2017. Tasarım kavramının geniş çerçevesi: Tasarım odaklı yaklaşımlar üzerine bir inceleme. Ordu Üniv Sos Bil Enst Sos Bil Araş Derg, 7(1): 85-94.
  • Amer NA. 2023. Architectural design in the light of AI concepts and applications. MSA Eng J, 2(2): 628-646.
  • Austin S, Baldwin A, Li B, Waskett P. 1999. Analytical design planning technique: a model of the detailed building design process. Design Stud, 20(3): 279-296.
  • Baran Ergül D, Varol Malkoçoğlu AB, Acun Özgünler S. 2022. Use of artificial intelligence based fuzzy logic systems in architectural design decision making processes. J Architect Sci Appl, 7(2): 878-899.
  • Başarır L. 2021. Modelling AI in architectural education. Gazi Univ J Sci, 35(4): 1260-1278.
  • Baydoğan MÇ. 2013. Tip imar yönetmeliğine uygun vaziyet planı üreten bir yapay zeka destek sistemi. PhD Thesis, Istanbul Technical University, Institute of Science, İstanbul, Türkiye, pp: 175.
  • Bingöl K, Akan AE, Örmecioğlu HT, Er A. 2020. Artificial intelligence applications in earthquake resistant architectural design: Detection of irregular structural system with Deep Learning and image processing method. Gazi Univ J Eng Architect Fac, 35(4): 2197-2210.
  • Bozdemir M, Mendi F. 2005. The knowledge management system architecture for artificial intelligent aided systematic design. Gazi Univ J Eng Architect Fac, 20(2): 267-274.
  • Bozdemir M. 2017. The effects of humidity on cast PA6G during turning and milling machining. Adv Mater Sci Engin, 2017(1): 5408691.
  • Çeliker EY, Efendioğlu G, Balaban Ö. 2020. Cycle-GAN ile modern iç mekânların bilim kurgu ortamları olarak yeniden üretilmesi. J Comput Design, 1(3): 71-94.
  • Chaillou S. 2019. AI & architecture. Routledge New York, US, pp: 486.
  • Conklin J. 2001. The age of design. Working paper, URL= http://cognexus.org/ageofdesign.pdf (accessed date: May 15, 2024).
  • Conrads U. 1991. 20. Yüzyıl mimarisinde program ve manifestolar. Şevki Vanlı Mimarlık Vakfı, Ankara, Türkiye, pp: 170.
  • Cooper R, Press M. 1995. The design agenda: A guide to successful design management. John Wiley & Sons Ltd., Chichester, UK.
  • Demirarslan D. 2006. İç mekân tasarımına giriş. Kocaeli Üniversitesi Yayınları, Kocaeli, Türkiye.
  • Demirci MY, Yabanova İ. 2019. Model tabanlı tasarım metotları kullanılarak gerçek zamanlı bir görüntü işleme sisteminin tasarımı ve gerçeklemesi. Politeknik Derg, 22(4): 827-838.
  • Deveci M. 2022. Yapay Zekâ Uygulamalarının Sanat ve Tasarım Alanlarına Yansıması. Vankulu Sos Araş Derg, 9: 118-140.
  • Ding Y, Liu Z, Qiu C, Shi J. 2007. Metamaterial with simultaneously negative bulk modulus and mass density. Phys Rev Lett, 99(9): 093904.
  • Dym CL. 1996. AIEDAM: Artificial intelligence for engineering design, analysis and manufacturing. Cambridge University Pres, Cambridge, UK, pp: 143.
  • Güneş H, Orta E, Akdaş D. 2016. Akıllı ev sistemlerinde kullanılan yapay zekâ teknikleri için yapay veri üretici geliştirilmesi. Balıkesir Üniv Fen Bilim Enstit Derg, 18 (2): 1-11. DOI: 10.25092/baunfbed.280151
  • Gunes R, Arslan K. 2016. Development of numerical realistic model for predicting low-velocity impact response of aluminium honeycomb sandwich structures. J Sandwich Struct Mater, 18(1): 95-112.
  • Hajirasouli A, Banihashemi, S. 2022. Augmented reality in architecture and construction education: State of the field and opportunities. Inter J Edu Technol Higher Educ, 19(1): 1–28. https:// doi. org/ 10. 1186/ s41239- 022- 00343-9
  • Ha-Mim NM, Hossain MZ, Islam MT, Rahaman KR. 2024. Evaluating resilience of coastal communities upon integrating PRISMA protocol, composite resilience index, and analytical hierarchy process. Int J Disaster Risk Reduc, 101: 104256.
  • Hayes-Roth B. 1995. An architecture for adaptive intelligent systems. Artif Intel, 72(1-2): 329-365.
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  • Hornick MF, Marcade E, Venkayala S. 2010. Java data mining: Strategy, standard, and practice: a practical guide for architecture, design, and implementation. Elsevier, San Francisco, US, pp: 544.
  • Ireland R, Liu A. 2018. Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP J Manufact Sci Technol, 23: 128-144.
  • İzgi U. 1999. Mimarlıkta süreç, kavramlar-ilişkiler, 1. Baskı. Yapı-Endüstri Merkezi Yayınları, İstanbul, Türkiye, pp: 199-200.
  • Jaihar J, Lingayat N, Vijaybhai PS, Venkatesh G, Upla KP. 2020. Smart home automation using machine learning algorithms. International Conference for Emerging Technology (INCET), June 5-7, Belgaum, India, pp: 1-4.
  • Ji A, Levinson D. 2020. A review of game theory models of lane changing. Transportmetrica A: Transport Sci, 16(3): 1628-1647.
  • Jin J, Ji P, Gu R. 2016. Identifying comparative customer requirements from product online reviews for competitor analysis. Eng Appl Artif Intell, 49: 61-73.
  • Jones JC. 1992. Design methods. John Wiley & Sons, New York, US, pp: 472.
  • Karahan HG, Aktaş B, Bingöl CK. 2023. Use of language to generate architectural scenery with AI-powered tools. International Conference on Computer-Aided Architectural Design Futures, July 5-7, Delft, the Netherlands, pp: 83-96.
  • Karslı M. 2019. Yapay zekânın tasarımcıyla iş birliği ve tasarıma olan etkisi. MSc Thesis, Istanbul Technical University, Institute of Science, İstanbul, Türkiye, pp: 99.
  • Kaya T, İnce M. 2012. Yapay sınır ağları yardımıyla modellenen pencere fonksiyonu kullanarak FIR filtre tasarımı. Gazi Üniv MMF Derg, 27(3): 599-606.
  • Kumar N, Goel PK, Aeron A. 2024. Beyond automation: exploring the synergy of cloud, AI, machine learning, and IoT for intelligent systems. J Electr Syst, 20(3): 1356-1364.
  • Kumar S, Gopi T, Harikeerthana N, Gupta MK, Gaur V, Krolczyk GM, Wu C. 2023. Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control. J Intel Manufact, 34(1): 21-55.
  • Li C, Zhang T, Du X, Zhang Y, Xie H. 2024. Generative AI for architectural design: A literature review. arXiv Preprint, arXiv: 2404.01335.
  • Li P, Li B, Li Z. 2024. Sketch-to-architecture: Generative ai-aided architectural design. arXiv preprint arXiv: 2403.20186.
  • Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Moher D. 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annals Internal Medic, 151(4): W-65.
  • Matić B, Jovanović S, Das DK, Zavadskas EK, Stević Ž, Sremac S, Marinković M. 2019. A new hybrid MCDM model: Sustainable supplier selection in a construction company. Symmetry, 11(3): 353.
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  • Mueller CT, Ochsendorf JA. 2015. Combining structural performance and designer preferences in evolutionary design space exploration. Automat Construct, 52: 70-82.
  • Munn Z, Peters MD, Stern C, Tufanaru C, McArthur A, Aromataris E. 2018. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medic Res Method, 18: 1-7.
  • Nowell LS, Norris JM, White DE, Moules NJ. 2017. Thematic analysis: Striving to meet the trustworthiness criteria. Int J Qualit Meth, 16(1): 1609406917733847.
  • Płoszaj-Mazurek M, Ryńska E, Grochulska-Salak M. 2020. Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning, convolutional neural network, and parametric design. Energies, 13(20): 5289.
  • Quan SJ. 2022. Urban-GAN: An artificial intelligence-aided computation system for plural urban design. Environ Plan B: Urban Analy City Sci, 49(9): 2500-2515.
  • Rego A, Canovas A, Jiménez JM, Lloret J. 2018. An intelligent system for video surveillance in IoT environments. IEEE Access, 6: 31580-31598.
  • Restarting Britain Report. 2011. Design Education and Growth. URL= http://www.policyconnect.org.uk/apdig/sites/site_apdig/files/report/284/fieldreportdownload /design-commission-restarting-britain-design-education-and-growth.pdf (accessed date: May 18, 2013).
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  • Şapcı B, Pektaş ŞT. 2021. Machine learning aracılığı ile kullanıcı deneyimi bilgilerinin erken mimari tasarım süreçleriyle bütünleştirilmesi. J Comput Design, 2(1): 67-94.
  • Sartipi K, Kontogiannis K, Mavaddat F. 2000, Architectural design recovery using data mining techniques. In Proceedings of the Fourth European Conference on Software Maintenance and Reengineering, March 03-03, Zurich, Switzerland, pp: 129-139.
  • Shao T, Zhang C. 2018. Architectural design model based on BIM management system model and data mining. Int J Performab Eng, 14(11): 2574.
  • Smith RP, Jeffrey AM. 1999. Product development process modeling. J Design Stud, 20(3): 237-261.
  • Sohail A. 2023. Genetic algorithms in the fields of artificial intelligence and data sciences. Annals Data Sci, 10(4): 1007-1018.
  • Sönmez F, Zontul M, Kaynar O, Tutar H. 2018. Anomaly detection using data mining methods in it systems: a decision support application. Sakarya Univ J Sci, 22(4): 1109-1123.
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The Use and Development of Artificial Intelligence in Architectural Design Processes

Year 2024, , 1347 - 1360, 15.11.2024
https://doi.org/10.34248/bsengineering.1559637

Abstract

Artificial intelligence is widely used as an interactive technology in various professional disciplines. The widespread use of these technologies, which we benefit from in most areas of our lives, in the education sector will provide important developments in the field of education. The main purpose of this study is to analyze the existing studies in which the use of artificial intelligence helps in architectural design processes. In the study, identification, screening, eligibility, inclusion, and data analysis processes were carried out in three search engines such as Web of Science, ScienceDirect, and ULAKBIM. While reporting the research, ‘Systematic Literature Review’ and ‘Preferred Reporting Items for Meta-Analysis’ protocols were followed and a total of 35 relevant articles were identified. In the research, three popular Artificial Intelligence applications used in architectural design processes were identified as Generative Adversarial Networks (GAN), Machine Learning, and Data Mining. In addition, Systematic Literature Review (SLR) outputs show that most researchers are supported by artificial intelligence applications in architectural design processes. As a result of the research, it was determined that artificial intelligence is widely used in architectural design processes, however, it has positive effects in 3D and animation parts.

References

  • Adem PÇ, Çağdaş G. 2020. Computational design thinking through cellular automata: reflections from design studios. J Design Stud, 2(2): 71-83.
  • Akdemi̇r N. 2017. Tasarım kavramının geniş çerçevesi: Tasarım odaklı yaklaşımlar üzerine bir inceleme. Ordu Üniv Sos Bil Enst Sos Bil Araş Derg, 7(1): 85-94.
  • Amer NA. 2023. Architectural design in the light of AI concepts and applications. MSA Eng J, 2(2): 628-646.
  • Austin S, Baldwin A, Li B, Waskett P. 1999. Analytical design planning technique: a model of the detailed building design process. Design Stud, 20(3): 279-296.
  • Baran Ergül D, Varol Malkoçoğlu AB, Acun Özgünler S. 2022. Use of artificial intelligence based fuzzy logic systems in architectural design decision making processes. J Architect Sci Appl, 7(2): 878-899.
  • Başarır L. 2021. Modelling AI in architectural education. Gazi Univ J Sci, 35(4): 1260-1278.
  • Baydoğan MÇ. 2013. Tip imar yönetmeliğine uygun vaziyet planı üreten bir yapay zeka destek sistemi. PhD Thesis, Istanbul Technical University, Institute of Science, İstanbul, Türkiye, pp: 175.
  • Bingöl K, Akan AE, Örmecioğlu HT, Er A. 2020. Artificial intelligence applications in earthquake resistant architectural design: Detection of irregular structural system with Deep Learning and image processing method. Gazi Univ J Eng Architect Fac, 35(4): 2197-2210.
  • Bozdemir M, Mendi F. 2005. The knowledge management system architecture for artificial intelligent aided systematic design. Gazi Univ J Eng Architect Fac, 20(2): 267-274.
  • Bozdemir M. 2017. The effects of humidity on cast PA6G during turning and milling machining. Adv Mater Sci Engin, 2017(1): 5408691.
  • Çeliker EY, Efendioğlu G, Balaban Ö. 2020. Cycle-GAN ile modern iç mekânların bilim kurgu ortamları olarak yeniden üretilmesi. J Comput Design, 1(3): 71-94.
  • Chaillou S. 2019. AI & architecture. Routledge New York, US, pp: 486.
  • Conklin J. 2001. The age of design. Working paper, URL= http://cognexus.org/ageofdesign.pdf (accessed date: May 15, 2024).
  • Conrads U. 1991. 20. Yüzyıl mimarisinde program ve manifestolar. Şevki Vanlı Mimarlık Vakfı, Ankara, Türkiye, pp: 170.
  • Cooper R, Press M. 1995. The design agenda: A guide to successful design management. John Wiley & Sons Ltd., Chichester, UK.
  • Demirarslan D. 2006. İç mekân tasarımına giriş. Kocaeli Üniversitesi Yayınları, Kocaeli, Türkiye.
  • Demirci MY, Yabanova İ. 2019. Model tabanlı tasarım metotları kullanılarak gerçek zamanlı bir görüntü işleme sisteminin tasarımı ve gerçeklemesi. Politeknik Derg, 22(4): 827-838.
  • Deveci M. 2022. Yapay Zekâ Uygulamalarının Sanat ve Tasarım Alanlarına Yansıması. Vankulu Sos Araş Derg, 9: 118-140.
  • Ding Y, Liu Z, Qiu C, Shi J. 2007. Metamaterial with simultaneously negative bulk modulus and mass density. Phys Rev Lett, 99(9): 093904.
  • Dym CL. 1996. AIEDAM: Artificial intelligence for engineering design, analysis and manufacturing. Cambridge University Pres, Cambridge, UK, pp: 143.
  • Güneş H, Orta E, Akdaş D. 2016. Akıllı ev sistemlerinde kullanılan yapay zekâ teknikleri için yapay veri üretici geliştirilmesi. Balıkesir Üniv Fen Bilim Enstit Derg, 18 (2): 1-11. DOI: 10.25092/baunfbed.280151
  • Gunes R, Arslan K. 2016. Development of numerical realistic model for predicting low-velocity impact response of aluminium honeycomb sandwich structures. J Sandwich Struct Mater, 18(1): 95-112.
  • Hajirasouli A, Banihashemi, S. 2022. Augmented reality in architecture and construction education: State of the field and opportunities. Inter J Edu Technol Higher Educ, 19(1): 1–28. https:// doi. org/ 10. 1186/ s41239- 022- 00343-9
  • Ha-Mim NM, Hossain MZ, Islam MT, Rahaman KR. 2024. Evaluating resilience of coastal communities upon integrating PRISMA protocol, composite resilience index, and analytical hierarchy process. Int J Disaster Risk Reduc, 101: 104256.
  • Hayes-Roth B. 1995. An architecture for adaptive intelligent systems. Artif Intel, 72(1-2): 329-365.
  • Hobday M, Boddington A, Grantham A. 2012. An innovation perspective on design: Part 2. Design Issues, 28(1): 18-29.
  • Hornick MF, Marcade E, Venkayala S. 2010. Java data mining: Strategy, standard, and practice: a practical guide for architecture, design, and implementation. Elsevier, San Francisco, US, pp: 544.
  • Ireland R, Liu A. 2018. Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP J Manufact Sci Technol, 23: 128-144.
  • İzgi U. 1999. Mimarlıkta süreç, kavramlar-ilişkiler, 1. Baskı. Yapı-Endüstri Merkezi Yayınları, İstanbul, Türkiye, pp: 199-200.
  • Jaihar J, Lingayat N, Vijaybhai PS, Venkatesh G, Upla KP. 2020. Smart home automation using machine learning algorithms. International Conference for Emerging Technology (INCET), June 5-7, Belgaum, India, pp: 1-4.
  • Ji A, Levinson D. 2020. A review of game theory models of lane changing. Transportmetrica A: Transport Sci, 16(3): 1628-1647.
  • Jin J, Ji P, Gu R. 2016. Identifying comparative customer requirements from product online reviews for competitor analysis. Eng Appl Artif Intell, 49: 61-73.
  • Jones JC. 1992. Design methods. John Wiley & Sons, New York, US, pp: 472.
  • Karahan HG, Aktaş B, Bingöl CK. 2023. Use of language to generate architectural scenery with AI-powered tools. International Conference on Computer-Aided Architectural Design Futures, July 5-7, Delft, the Netherlands, pp: 83-96.
  • Karslı M. 2019. Yapay zekânın tasarımcıyla iş birliği ve tasarıma olan etkisi. MSc Thesis, Istanbul Technical University, Institute of Science, İstanbul, Türkiye, pp: 99.
  • Kaya T, İnce M. 2012. Yapay sınır ağları yardımıyla modellenen pencere fonksiyonu kullanarak FIR filtre tasarımı. Gazi Üniv MMF Derg, 27(3): 599-606.
  • Kumar N, Goel PK, Aeron A. 2024. Beyond automation: exploring the synergy of cloud, AI, machine learning, and IoT for intelligent systems. J Electr Syst, 20(3): 1356-1364.
  • Kumar S, Gopi T, Harikeerthana N, Gupta MK, Gaur V, Krolczyk GM, Wu C. 2023. Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control. J Intel Manufact, 34(1): 21-55.
  • Li C, Zhang T, Du X, Zhang Y, Xie H. 2024. Generative AI for architectural design: A literature review. arXiv Preprint, arXiv: 2404.01335.
  • Li P, Li B, Li Z. 2024. Sketch-to-architecture: Generative ai-aided architectural design. arXiv preprint arXiv: 2403.20186.
  • Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Moher D. 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Annals Internal Medic, 151(4): W-65.
  • Matić B, Jovanović S, Das DK, Zavadskas EK, Stević Ž, Sremac S, Marinković M. 2019. A new hybrid MCDM model: Sustainable supplier selection in a construction company. Symmetry, 11(3): 353.
  • Mirakhorli M, Chen HM, Kazman R. 2015, Mining big data for detecting, extracting and recommending architectural design concepts. IEEE/ACM 1st International Workshop on Big Data Software Engineering, May 23-23, Florence, Italy, pp: 15-18.
  • Mohamed Shaffril HA, Samsuddin SF, Abu Samah A. 2021. The ABC of systematic literature review: the basic methodological guidance for beginners. Qual Quant, 55: 1319-1346.
  • Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Prisma-P Group. 2015. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Rev, 4: 1-9.
  • Moor J. 2006. The Dartmouth College Artificial Intelligence Conference: The next fifty years, AI Magazine, 27(4): 87-90.
  • Mueller CT, Ochsendorf JA. 2015. Combining structural performance and designer preferences in evolutionary design space exploration. Automat Construct, 52: 70-82.
  • Munn Z, Peters MD, Stern C, Tufanaru C, McArthur A, Aromataris E. 2018. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medic Res Method, 18: 1-7.
  • Nowell LS, Norris JM, White DE, Moules NJ. 2017. Thematic analysis: Striving to meet the trustworthiness criteria. Int J Qualit Meth, 16(1): 1609406917733847.
  • Płoszaj-Mazurek M, Ryńska E, Grochulska-Salak M. 2020. Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning, convolutional neural network, and parametric design. Energies, 13(20): 5289.
  • Quan SJ. 2022. Urban-GAN: An artificial intelligence-aided computation system for plural urban design. Environ Plan B: Urban Analy City Sci, 49(9): 2500-2515.
  • Rego A, Canovas A, Jiménez JM, Lloret J. 2018. An intelligent system for video surveillance in IoT environments. IEEE Access, 6: 31580-31598.
  • Restarting Britain Report. 2011. Design Education and Growth. URL= http://www.policyconnect.org.uk/apdig/sites/site_apdig/files/report/284/fieldreportdownload /design-commission-restarting-britain-design-education-and-growth.pdf (accessed date: May 18, 2013).
  • Şahin İ. 2014. Yapay sinir ağları ile Al/Sic kompozit malzemenin yüzey pürüzlülüğünün tahmini. Gazi Üniv MMF Derg, 29(1): 209-216.
  • Şapcı B, Pektaş ŞT. 2021. Machine learning aracılığı ile kullanıcı deneyimi bilgilerinin erken mimari tasarım süreçleriyle bütünleştirilmesi. J Comput Design, 2(1): 67-94.
  • Sartipi K, Kontogiannis K, Mavaddat F. 2000, Architectural design recovery using data mining techniques. In Proceedings of the Fourth European Conference on Software Maintenance and Reengineering, March 03-03, Zurich, Switzerland, pp: 129-139.
  • Shao T, Zhang C. 2018. Architectural design model based on BIM management system model and data mining. Int J Performab Eng, 14(11): 2574.
  • Smith RP, Jeffrey AM. 1999. Product development process modeling. J Design Stud, 20(3): 237-261.
  • Sohail A. 2023. Genetic algorithms in the fields of artificial intelligence and data sciences. Annals Data Sci, 10(4): 1007-1018.
  • Sönmez F, Zontul M, Kaynar O, Tutar H. 2018. Anomaly detection using data mining methods in it systems: a decision support application. Sakarya Univ J Sci, 22(4): 1109-1123.
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There are 76 citations in total.

Details

Primary Language English
Subjects Computer-Assisted Design, Visual Communication Design (Other)
Journal Section Research Articles
Authors

Metin Demir 0000-0001-9374-6079

Meryem Akti 0000-0003-0330-5988

Publication Date November 15, 2024
Submission Date October 1, 2024
Acceptance Date November 5, 2024
Published in Issue Year 2024

Cite

APA Demir, M., & Akti, M. (2024). The Use and Development of Artificial Intelligence in Architectural Design Processes. Black Sea Journal of Engineering and Science, 7(6), 1347-1360. https://doi.org/10.34248/bsengineering.1559637
AMA Demir M, Akti M. The Use and Development of Artificial Intelligence in Architectural Design Processes. BSJ Eng. Sci. November 2024;7(6):1347-1360. doi:10.34248/bsengineering.1559637
Chicago Demir, Metin, and Meryem Akti. “The Use and Development of Artificial Intelligence in Architectural Design Processes”. Black Sea Journal of Engineering and Science 7, no. 6 (November 2024): 1347-60. https://doi.org/10.34248/bsengineering.1559637.
EndNote Demir M, Akti M (November 1, 2024) The Use and Development of Artificial Intelligence in Architectural Design Processes. Black Sea Journal of Engineering and Science 7 6 1347–1360.
IEEE M. Demir and M. Akti, “The Use and Development of Artificial Intelligence in Architectural Design Processes”, BSJ Eng. Sci., vol. 7, no. 6, pp. 1347–1360, 2024, doi: 10.34248/bsengineering.1559637.
ISNAD Demir, Metin - Akti, Meryem. “The Use and Development of Artificial Intelligence in Architectural Design Processes”. Black Sea Journal of Engineering and Science 7/6 (November 2024), 1347-1360. https://doi.org/10.34248/bsengineering.1559637.
JAMA Demir M, Akti M. The Use and Development of Artificial Intelligence in Architectural Design Processes. BSJ Eng. Sci. 2024;7:1347–1360.
MLA Demir, Metin and Meryem Akti. “The Use and Development of Artificial Intelligence in Architectural Design Processes”. Black Sea Journal of Engineering and Science, vol. 7, no. 6, 2024, pp. 1347-60, doi:10.34248/bsengineering.1559637.
Vancouver Demir M, Akti M. The Use and Development of Artificial Intelligence in Architectural Design Processes. BSJ Eng. Sci. 2024;7(6):1347-60.

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