# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 3 Digital Twins Current State of the Art  
### General  
The question of what a DT is, when it comes to civil infrastructure, is not clearly defined. There are different levels of representation of reality in a digital world, and different classifications have been developed accordingly. Based on the level of organisation, [PERSON] [15] established that prototypes, instances, aggregates and environments can be differentiated. Prototypes (DTPs) are a description of the physical artifact and, before it is fully built and/or manufactured and thus ready to start its operation phase, contains the information of the real objects. For example, it could be a 3D model with the bills of materials, specifications and processes of such entity. Instances (DTIs) contain the information included in the prototype along with the results of measurements, service records, replaced components and other similar information. Aggregates (DTAs) are the sum of DTIs and the connections among them. It retrieves various data from the DTI, including sensor readings, and analyses them together. Environments (DTEs) are the end-to-end environment setup to operate DTs. The operation receives information from machines, analyses the data, and performs a predictive analysis behavioural analysis. As mentioned before, [PERSON] [5; 6] proposed a definition with the same names but targeting different objectives. The first regarding the lifecycle of the product, the second regarding the degree of DT capabilities, powers, or levels of implementation. When the functions of DTs are considered, DTs might be classified as status twins, operational twins, or simulation twins [16]. Status twins are integrated into analytics systems and include monitoring tools, such as alerting systems, which will simulate various scenarios and situations to ensure the system will have a perfect response after installation. Operational twins are used to collect information on a certain process. For instance, it might entail running an engine test in order to detect problems and produce solutions. Lastly, simulation twins sustain an integration of AI capability to ease the decision-making process in understanding how a system functions. Apart from these classifications, if future developments of DTs are considered another classification might appear [17]. A pre-digital twin is a virtual prototype used for a preliminary development in order to reduce risks and identifying an upfront design. A DT is a virtual system that can incorporate performance or maintenance data of the real asset. The following level is called an adaptative DT. Such an entity offers an adaptative user interface which is capable of studying the preferences of human operators in different contexts. Lastly, the highest level is defined as an intelligent DT. Such twins can discern patterns and objects that might be found in the operating environment and apply them to an unsupervised machine learning capability. Apart from the potential classifications that have been mentioned, it can be said that city-scale DTs are focused on enhancing the lives of the citizeny, with mobility plus services through human-centred technological innovations, as stated in [18]. Some of these cities include Bogota [19], Helsinki [20], Brescia [21], Valencia [22], Dublin [23], Herrenberg [18], Shenzhen [24], and Zurich [25]. Different strategies have been employed to initiate actual urban DTs in their development by integrating data from multiple sources that generates huge operational data and also presents challenges for diagnosis and prognosis [26]. This amalgamation is facilitated by different types of information, like traffic, transportation, power generation, utility provisioning, water supply, and waste management. All this information can be managed using machine learning algorithms, such as k-means clustering [23].