Architecture in the information age: A bibliometric analysis of machine learning studies in the fields of design, construction, and planning

In the information age, which is acknowledged as a new era with the emergence of Industry 4.0, it has been predicted that cyber systems and robots will replace the physical labour force in many professions for the near future. Because of this, it is expected that humanity will develop itself in order to adapt to the new era and master the technologies of its time. Considering the fact that artificial intelligence and machine learning, which is the last stage of artificial intelligence, are one of the first field that comes to mind when it comes to informatics, will be among the fundamental field in the near future. Either individuals or companies, it will be a great advantage for them in a competitive environment, if they have an idea about these issues. In some fields, which are aware of this situation, studies on artificial intelligence and machine learning have spread rapidly. In the fields of design, construction and planning, which are also the focus of this dissertation, machine learning software have been actively used for a while. Furthermore, in the field of design, working on these systems such as creating alternative design aids with machine learning, etc. is still in progress. It is one of the methods that can be done by looking at the academic studies in the literature in order to determine the current situation of a subject in a field. This method, called bibliometric analysis, gives a comprehensive result to the researcher about the trend of studies on a certain subject in a field in the direction the obtained data. This method can also be used for the current situation of artificial intelligence and machine learning in different fields; design, construction and planning are one of them. In this dissertation, a comprehensive bibliometric analysis was conducted in direction of the data which was obtained through browsers and pre-processed with different software in order to see the trends and current approaches in the field of using machine learning in the disciplines of design, construction and planning. In the dissertation, it is aimed to be defined as a guide for readers in order to turn the risk of reducing the need for human labour into an advantage with advanced technologies in the near future. When the results of the analysis are examined, it is clear that academic studies on construction and building fields are much more effective and in higher numbers compared to design subjects. It has been seen that engineering-based studies on these subjects constitute the majority of the total studies in this field. On the other hand, it has been determined that the academic contributions of design-related disciplines such as architecture and city planning, to the field of machine learning are less than the disciplines related to construction. In addition to the construction and building industry, it has been observed that energy and sustainability-themed subjects are also studied effectively, but they do not have a strong academic relationship with other subjects. During the analysis, it was determined that the theme of architectural design for the discipline of architecture and smart city studies for the discipline of urbanism came to the fore. A similar situation was found with the results of the analysis when a similarity was sought between the two fields regarding the use of these technologies in the sector, considering the density of the subject being studied in academia. While software for the construction industry has been released to the market, experimental projects are being made on the architectural design for the field of architecture or software are still being worked on. In the field of efficiency-related subjects, there are software offered to the use of the sectors such as performance optimization in buildings.