Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review

Abstract

Digital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multiple data sources to predict their physical counterparts’ current and future conditions. This has promoted its relevance in various industries, including the construction industry (CI). However, recognising the existence of a distinct set of factors driving its adoption has not been established. Therefore, this study aims to identify the drivers and integrate them into a classification framework to enhance its understanding. Utilising popular databases, including Scopus, Web of Science, and ScienceDirect, a systematic literature review of 58 relevant DT adoptions in the CI research was conducted. From the review, the drivers for DT adoption in the CI were identified and classified. The results show that developed countries such as the UK, US, Australia, and Italy have been the top countries in advancing DT adoption in the CI, while developing countries have made commendable contributions. A conceptual framework has been developed to enhance the successful adoption of DT in the CI based on 50 identified drivers. The major categories of the framework include concept-oriented drivers, production-driven drivers, operational success drivers, and preservation-driven drivers. The developed framework serves as a guide to propel DT adoption in the CI. Furthermore, this study contributes to the body of knowledge about DT adoption drivers, which is essential for DT promotion in the CI.

Scientometric Insights of Digital Twins in Industry 4.0

“…It is identified as "digital twin adoption" by LLR. Notably, Opoku and Dj (2022) authored the primary citing article [78]. Cluster #2, the third largest, encompasses 15 members and has a silhouette value of 0.92.…”

Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey

“…Existing surveys addressing the DT field focus on characterizing DTs from modeling perspectives, architecture proposals, and the categories of services and applications most used in a field. Readers are referred to surveys that deal with foundation concepts of DTs [11]- [18]; industry verticals [19]- [22] and also in Industry 4.0 manufacturing models [23]; civil engineering [24]- [29]; agriculture [30]- [32]; energy [33]- [36]; healthcare [37], [38]; and, finally, to smart cities [39], [40]. Although DTs have been gaining popularity in transport system solutions, a survey that updates the state of the art of DTs for ITS is yet to be found in the literature.…”

Metaverse for Cultural Heritages

“…Tao et al [13] also analyzed ten questions relevant to DT. Opoku et al [14] elaborated the concept-oriented drivers, the production-driven drivers, the operational success drivers and the preservation-driven drivers of DT Adoption in the Construction Industry. Kor et al [15] investigated the potential integration of deep learning and DTs to facilitate Construction 4.0 through an exploratory analysis.…”