Knowledge map and forecast of digital twin in the construction industry: State-of-the-art review using scientometric analysis. (10th January 2023)

• Record Type: Journal Article
• Authors: Xie, Haiyan; Xin, Mengyang; Lu, Caiwu; Xu, Jiayue
• Is Part Of: Journal of cleaner production. Volume 383(2023)
• Publication Date: 2023-01-10
• Subjects: Digital twin -- Bibliometric analysis -- Knowledge graph -- Evolution -- Intelligent construction; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.135231
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: With the advancement of Industry 4.0 and the development of science and technology, industries have seen the advantages of digital twin (DT) implementations and concepts. Although the construction industry is still in the early stage of DT development and implementation, it is urgent for the industry to have a DT agenda. This paper identifies 1158 related bibliographic records from the Web of Science and 745 records from the Derwent Patent Database and quantitatively analyzed them to describe patterns of publications. Two knowledge mapping tools were selected to visualize and analyze the literature in the relevant scientific domain. Then, we integrated clustering, knowledge mapping, and network analysis methods to show the current foci and future directions of DT-MT derived from the data of time distribution, journal areas, and subject distribution. The results indicate that DT is a cross-disciplinary information technology with great development potential, and its development in the construction industry is scattered with the leading advancement in the manufacturing of construction materials and components. The derived knowledge map shows three stages of construction DT, including (1) data collection and creation of DT framework and database; (2) implementation of planning and design modules in detail and control of construction sites; and (3) carrying out asset management and fault prediction in the operation and maintenance phase of projects. The novelty of this paper is the specific analysis of DT in the construction industry. This study adds value to the architecture, engineering, and construction industries by shedding light on the focus of DT practical applications and the formation of a development path of construction DT. Highlights: This paper seeks to improve the understanding of Digital Twins in the construction industry. Helps relevant industries to develop a well-structured development path. Smart engineering, manufacturing, and construction affect industrial performance and the quality of construction projects. Integration of clustering, knowledge mapping, and network analysis methods to identify the current foci and future directions. Prediction of the technology influences and its clean production to enhance domain-general strategy knowledge.