## 1 Introduction  
To meet the evolving needs of a dynamic, global urban space, infrastructure assets supporting modern cities should make use of technologies that drive the operations and predictive maintenance activities enabled by the capabilities of the industrial internet of things (IIoT). In recent years, more and more articles have discussed the concepts of digital twins and their use in the context of smart cities. The sense-acquire-transform-analyse-act sequence is not new, and it underpinned the topic of condition monitoring for the past two decades. The adoption of the related concepts of condition monitoring (CM), industrial internet of things (IIoT), digital twins (DTs), and cyber-physical systems (CPS) in modern city infrastructure applications is often described as the fourth generation of the industrial revolution. The central theme of this so-called revolution is the integration of the physical with the digital world and the visualisation of insights and realisation service offerings that can be obtained therefrom. Condition monitoring is the process of monitoring a condition parameter in machinery to identify a significant change, which is indicative of degradation/anomaly. The IIoT can be defined as the connection of industrial equipment to networked computing resources to collect data and use these data to drive relevant outcomes/services. In a similar vein, a digital twin can be described as a virtual data model that represents an actual physical entity in digital space--within the confines of a defined use case or experimental frame. Cyber-physical systems can comparably be described as systems that integrate the dynamics of physical processes with computation, networking, and decision-making capabilities. These four concepts are so closely related that they are sometimes confused with each other and used interchangeably, although, over the years, academia and industry have tried to define processes for their design and implementation, as well as standards and recommended practices for their realisation and deployment. [PERSON] et al. [1] provide a tabulated review of recent research works relating to digital twins and highlight some common misconceptions with other cognate notions of Industry 4.0. [PERSON] et al. [2] discuss the correlation and comparisons between DTs and CPS, whereas [PERSON] et al. [3] distinguish between all four concepts of CM, IIoT, DTs, and CPS and explain the interactions between them. As stated in [4; 5; 6], the IIoT provides the data framework upon which most industrial digital twins are built. Thus, a thorough understanding of the IIoT application development process is crucial in implementing digital twins. In the built environment space, the concept of building information modelling (BIM) has begun to be discussed more often in recent years in relation to DTs and CPS [7]. If BIM is typically employed as designed/as built during the delivery stages of a project supporting commissioning activities and facilities management, DT capabilities tap into the asset management, predictive maintenance, what-if analysis, and a diverse range of simulations supporting the optimisation of operations. At the same time, a different perspective on DT-related research actively discussed in various working groups is attributed to the definitions of layers describing the digital counterpart of a physical asset. These layers refer to data services (DS), integration (II), intelligence (I2), user experience (UX), management (M), and trustworthiness (T) [8]. Datasets supporting the development of DTs for smart infrastructure systems exist, and some can certainly support the instantiation of the DT layers mentioned above. An example of such a dataset is the one generated by [9] to target leak detection and localisation in water distribution systems. Also, several authors have explored different fault diagnosis and prognostics approaches for various systems with the use of machine learning as a more recent phenomenon. [PERSON] et al.