| id |
caadria2019_650 |
| authors |
Papasotiriou, Tania |
| year |
2019 |
| title |
Identifying the Landscape of Machine Learning-Aided Architectural Design - A Term Clustering and Scientometrics Study |
| source |
M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 815-824 |
| doi |
https://doi.org/10.52842/conf.caadria.2019.2.815
|
| summary |
Recent advances in Machine Learning and Deep Learning revolutionise many industry disciplines and underpin new ways of problem-solving. This paradigm shift hasn't left Architecture unaffected. To investigate the impact on architectural design, this study utilises two approaches. First, a text mining method for content analysis is employed, to perform a robust review of the field's literature. This allows identifying and discussing current trends and possible future directions of this research domain in a systematic manner. Second, a Scientometrics study based on bibliometric reviews is employed to obtain quantitative measures of the global research activity in the described domain. Insights on research trends and identification of the most influential networks in this dataset were acquired by analysing terms co-occurrence, scientific collaborations, geographic distribution, and co-citation analysis. The paper concludes with a discussion on the limitations, opportunities and future research directions in the field of Machine Learning-aided architectural design. |
| keywords |
Machine Learning; Text mining; Scientometrics |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (5,006,010 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 08:00 |
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