## 7 Discussion and Challenges  
### General Observations  
[PERSON] et al. have observed, in their review on the use of DT for maintenance, that the research in this domain is still recent [19]. In this literature review, we have made the same observation. Although our search dates back to 2005, we did not observe any studies before 2017 (see Figure 12), while the definition of Grieves dates from 2002 [17] and the one from [PERSON] and [PERSON] dates from 2012 [18]. However, we can observe a clear increase in the number of papers over the years since 2017. This increase can potentially be explained by the accelerating miniaturisation of computer systems, which makes it easier to set up smart monitoring systems [6,87]. To evaluate the advancement in research on the use of DT for building maintenance, we have decided to classify the resulting papers according to two main criteria. Firstly, we have separated the conceptual projects, use cases and reviews according to year (see Figure 12). We can observe that the number of use cases has increased over the years (from 3 in 2018 to 10 in 2020). However, the number of conceptual papers in the last five years shows how the research studies in this domain are still recent and how there is still a need to define clear implementation methodologies. The increase in use-case papers can also be explained by the improvements that have been done in the immers  
Figure 11: Collaborative inspection of an aircraft part. The expert’s avatar is represented by the red box and his gaze by the red beam [72]. Figure 12: Type of paper by year. their use to improve human-machine interactions [6; 88]. Furthermore, _hyper-automation_ has become increasingly common for industrial processes in recent years. This term defines the use of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), to automate processes and provide advanced analytics [5]. _Hyper-automation_ could then result in the creation of a DT of the entire organisation (e.g., roles, key performance indicators and processes). Then, to observe how many _true_ DTs have been developed in the last years, we have used the definitions of [PERSON] et al. to identify the evolution of the DT development by taking into consideration how the data are exchanged between the physical asset, its digital counterpart and the user. _True_ DTs are those for which changes in the physical asset cause a change in its digital counterpart but also for which the physical is directly impacted by changes in the digital environment [21] (see Figures 12 and 13). Papers classified as \"_undefined_\", which are not reviews, do not define the whole of the DT development but only a part of it. Others focus more on the implementation of I4.0 technologies (e.g., IoT, BIM, XR) to improve the DT than on the development of the DT itself. It can be observed that the number of papers using _true_ DTs has increased over the years (see Table 8), which can be explained by the improvements in IoT due to the miniaturisation of computer systems and to the improvements in interoperability between the equipment, the infrastructure and information systems [6; 87]. However, only a third of these papers are use cases, most of them focusing on the development of concepts or new implementation methodologies (see Figure 14). Furthermore, even if the main scope of this research was the usage of DT in the construction industry and facility management, most of the papers found focused on projects related to the general industry (e.g., manufacturing, energy industry and aerospace) (see Figures 14 and 15). This lack of use cases and usage in building management shows that there is still a need to show the added value of using DT to professionals in the construction sector. It could explain why we have found only a few papers related to the application of a BIM-based DT combined with XR technologies for building maintenance.