| id |
ijac202321102 |
| authors |
Özerol, Gizem; Semra Arslan Selçuk |
| year |
2023 |
| title |
Machine learning in the discipline of architecture: A review on the research trends between 2014 and 2020 |
| source |
International Journal of Architectural Computing 2023, Vol. 21 - no. 1, pp. 23–41 |
| summary |
Abstract Through the recent technological developments within the fourth industrial revolution, artificial intelligence (AI) studies have had a huge impact on various disciplines such as social sciences, information communication technologies (ICTs), architecture, engineering, and construction (AEC). Regarding decision-making and forecasting systems in particular, AI and machine learning (ML) technologies have provided an opportunity to improve the mutual relationships between machines and humans. When the connection between ML and architecture is considered, it is possible to claim that there is no parallel acceleration as in other disciplines. In this study, and considering the latest breakthroughs, we focus on revealing what ML and architecture have in common. Our focal point is to reveal common points by classifying and analyzing current literature through describing the potential of ML in architecture. Studies conducted using ML techniques and subsets of AI technologies were used in this paper, and the resulting data were interpreted using the bibliometric analysis method. In order to discuss the state-of-the-art research articles which have been published between 2014 and 2020, main subjects, subsets, and keywords were refined through the search engines. The statistical figures were demonstrated as huge datasets, and the results were clearly delineated through Sankey diagrams. Thanks to bibliometric analyses of the current literature of WOS (Web of Science), CUMINCAD (Cumulative Index about publications in Computer Aided Architectural Design supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD, and CAAD futures), predictable data have been presented allowing recommendations for possible future studies for researchers. |
| keywords |
Artificial intelligence, machine learning, deep learning, architectural research, bibliometric analysis |
| series |
journal |
| references |
Content-type: text/plain
|
Anderson C, Bailey C, Heumann A, et al (2018)
 Augmented space planning: using procedural generation to automate desk layouts
, Int J Architectural Comput 2018; 16: 164–177
|
|
|
|
|
Arciszewski T, Michalski RS and Dybala T (1995)
STAR methodology-based learning about construction accidents and heir prevention
, Automation in Construction 1995; 4: 75–85.
|
|
|
|
|
As I, Pal S and Basu P (2018)
Artificial intelligence in architecture: generating conceptual design via deep learning
, Int J Architectural Comput 2018; 16: 306–327
|
|
|
|
|
Braun A and Borrmann A (2019)
Combining inverse photogrammetry and BIM for automated labeling of construction siten images for machine learning
, Automation in Construction 2019; 106: 102879, Article
|
|
|
|
|
Brugnaro G and Hanna S (2017)
Adaptive robotic training methods for subtractive manufacturing
, Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) 2017, Cambridge, MA, 2–4 November 2017. Acadia Publishing Company, pp. 164–169.
|
|
|
|
|
Cai BY, Li X, Seiferling I, et al (2018)
Treepedia 2.0: applying deep learning for large-scale quantification of urban tree cover
, 2018 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, USA, 2-7 July
|
|
|
|
|
Chai K-H and Xiao X (2012)
Understanding design research: a bibliometric analysis of design studies (1996-2010)
, Des Stud 2012; 33: 24–43.
|
|
|
|
|
Chaillou S (2019)
AI + Architecture | towards a New Approach[Master’s thesis, Harvard Graduate School of Design]
, Harvard University, 2019, https://view.publitas.com/harvard-university/ai-architecture-thesis-harvard-gsd-stanislaschaillou/page/6-7.
|
|
|
|
|
Chang M-C, Bus P, Tartar A, et al (2018)
Big-data informed citizen participatory Urban identity design
, Proceedings of ˇ the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19–21 September 2018, 2018.
|
|
|
|
|
Choi J, Gu B, Chin S, et al (2020)
Machine learning predictive model based on national data for fatal accidents of construction workers
, Automation in Construction 2020; 110: 102974.
|
|
|
|
|
Cole FJ and Eales NB (1917)
The history of comparative anatomy: Part I.—A statistical analysis of the literature
, Sci Prog (1916-1919) 1917; 11: 578–596.
|
|
|
|
|
Cutellic P (2019)
Towards encoding shape features with visual event-related potential based brain-computer interface for generative design
, Int J Architectural Comput 2019; 17: 88–102
|
|
|
|
|
Darko A, Chan APC, Adabre MA, et al (2020)
Artificial intelligence in the AEC industry: scientometric analysis and visualization of research activities
, Automation in Construction 2020;
|
|
|
|
|
Elgammal A., Liu B., Kim D., Elhoseiny M. and Mazzone M. (2018)
The shape of art history in the eyes of the machine
, Proceedings of the AAAI Conference on Artificial Intelligence 2018, April; (Vol. 32, No 1)
|
|
|
|
|
Feng D-C, Liu Z-T, Wang X-D, et al (2020)
Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach
, Construction Building Mater 2020; 230: 117000, Article
|
|
|
|
|
Fox M (2016)
Interactive Architecture: Adaptive World
, NY: Chronicle Books, 2016.
|
|
|
|
|
Fyfe C. Fyfe, C (2005)
Artificial neural networks
, In Do smart adaptive systems exist? Berlin, Heidelberg.: Springer, 2005, pp. 57–79.
|
|
|
|
|
Geron A (2017)
Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
, Beijing, Boston, Farnham, Sebastopol, Tokyo: O’Reilly Media, Inc., 2017. 38 International Journal of Architectural Computing 21(1)
|
|
|
|
|
Hong Y, Hammad AWA, Akbarnezhad A, et al (2020)
A neural network approach to predicting the net costs associated with BIM adoption
, Automation in Construction 2020; 119: 103306, Article
|
|
|
|
|
Hood WW and Wilson CS (2001)
The literature of bibliometrics, scientometrics, and informetrics
, Scientometrics 2001; 52: 291–314.
|
|
|
|
| last changed |
2024/04/17 14:30 |
|
