Obstacles to the implementation of digital twins : a review in the construction of industry

![CDATA[The advent of industry 4.0 gave rise to an array of digital technologies, including digital twins (DTs). DTs present the opportunity to develop digital models, which can be continually updated using several data sources to predict physical assets’ current and future conditions. These models can be simulated for real-time predictions, optimisation, monitoring, controlling and enhanced decision making regarding the status of a physical asset. In addition, DTs utilise other technologies, including artificial intelligence (AI), machine learning and data analytics. Due to the prowess of DTs, the construction industry, with its numerous challenges, have started DTs applications. Technologies such as building information modelling (BIM), Wireless Sensor Networks (WSNs), data analytics, and machine learning are currently supporting the adoption of DTs in the construction industry. Several studies (Boje et al. 2020; Opoku, DGJ et al. 2021; Sacks et al. 2020) have studied DTs in the construction industry and established their relevance. For instance, Opoku, DGJ et al. (2021) indicated that DTs are necessary for facility management since they can be employed in ‘What-if’ analysis in decision making regarding the operation and maintenance of the building. Researchers and practitioners are currently discovering the numerous potentials of DTs in the construction industry. There is, however, a misconception in the construction industry where DTs are likened to BIM due to their similarities (Khajavi et al. 2019). Opoku, D-GJ et al. (2022) and Khajavi et al. (2019) reported their differences based on their purposes, technologies, and end-users. Notwithstanding the advancement of DTs in the construction industry, it is essential to answer the question, “What barriers impede the prompt adoption of DTs in the construction industry?”. Unfortunately, little attention has been geared toward the barriers hindering DTs adoption in the construction industry. Though there have been several reviews (Alshammari et al. 2021; Deng et al. 2021; Opoku, D-GJ et al. 2022; Opoku, DGJ et al. 2021) on DTs in the construction industry, they did not pay enough attention to the barriers to DTs adoption. Thus, there is yet to be a study that comprehensively reviews the literature on the barriers to adopting DTs in the construction industry. This hinders the preparedness to fully embrace DTs in the construction industry. Therefore, this research aims to identify and rank the barriers to adopting DTs in the construction industry to support a roadmap for its adoption in the construction industry.]]