Volume 5, Issue 2 (2024)
2024, 5(2): 116-128 |
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Tadayon K, mahdavinjad M, Shahcheraghi A. Application of Machine Learning Methodology in the Design of the Built Environment. Urban Design Discourse
a Review of Contemporary Litreatures and Theories 2024; 5 (2) :116-128
URL: http://udd.modares.ac.ir/article-40-75893-en.html
a Review of Contemporary Litreatures and Theories 2024; 5 (2) :116-128
URL: http://udd.modares.ac.ir/article-40-75893-en.html
1- PhD student at the Faculty of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Professor of Department of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran ,mahdavinejad@modares.ac.ir
3- Faculty of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Professor of Department of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran ,
3- Faculty of Civil Engineering, Architecture and Art, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abstract: (110 Views)
Aims: Today, the use of artificial intelligence has grown significantly, and is developing as a new field. The main goal of this research is to know the capabilities of artificial intelligence in advancing the design and implementation process in the artificial environment. The practical goal of research is the development and application of the most important achievements of machine learning in the field of design.
Methods: The main research method is "meta-analysis" research in the paradigm of "free research" with a critical approach and basic design, which examines the general knowledge field of this field using broad techniques. Then, to consolidate the literature on the topic, through searching three reliable knowledge bases of this field, we collected articles related to machine learning in the fields of unsupervised learning methods, semi-supervised learning, and reinforcement learning; The most important capacities and shortcomings, and strengths and weaknesses are reviewed.
Findings: Quantitative findings from the combined data indicate that supervised machine learning and directed deep learning can be the best option to recommend in the future of design. While the learning process in deep learning is gradual and slower, supervised machine learning works faster in the testing phase.
Conclusion: The research emphasizes that supervised machine learning is the best option for predicting answers in the design process. But if, in addition to prediction, the issue of creativity in design is desired, deep learning is more efficient.
Methods: The main research method is "meta-analysis" research in the paradigm of "free research" with a critical approach and basic design, which examines the general knowledge field of this field using broad techniques. Then, to consolidate the literature on the topic, through searching three reliable knowledge bases of this field, we collected articles related to machine learning in the fields of unsupervised learning methods, semi-supervised learning, and reinforcement learning; The most important capacities and shortcomings, and strengths and weaknesses are reviewed.
Findings: Quantitative findings from the combined data indicate that supervised machine learning and directed deep learning can be the best option to recommend in the future of design. While the learning process in deep learning is gradual and slower, supervised machine learning works faster in the testing phase.
Conclusion: The research emphasizes that supervised machine learning is the best option for predicting answers in the design process. But if, in addition to prediction, the issue of creativity in design is desired, deep learning is more efficient.
Keywords: designerly approach, supervised machine learning, new technologies of architecture and urban planning, AI (artificial intelligence), highperformance architecture, computational design.
Article Type: Original Research |
Subject:
Smart City & Technology
Received: 2024/07/2 | Accepted: 2024/07/21 | Published: 2024/08/31
Received: 2024/07/2 | Accepted: 2024/07/21 | Published: 2024/08/31
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