## 3 Description of Methods and Tools
### Digital Twin Definition and Relations to BIM, BLM, and PLM  
A digital twin is a digital representation of a physical asset. It is used to help engineers understand what is happening to a physical building or piece of infrastructure. In contrast to the classical way of designing, the data and information accumulated through the design, construction, and usage of a structure constitute its digital twin. The Gemini Principles define a digital twin as \"a realistic digital representation of assets, processes or systems in the built or natural environment\" [2]. Digital twins are adaptive and therefore provide reliable predictions within a changing environment. They are in accordance with the current trend toward digitalization and have a remarkable effect on business and markets. A schematic representation of the elements that are used for a DT application on infrastructures is shown in Figure 3.  
Figure 2: DT classification for parts and systems, based on information given in [3]. Historically, DT is the evolution of building information modelling (BIM), building lifecycle management (BLM), and product lifecycle management (PLM). Depending on the maturity of DT, it may be restricted to a simple capture of reality, such as a photogrammetry measurement of an existing building, to a fully autonomous system, such as the control mechanism of a drone. The main tools required to create and operate a digital twin are the following:  
Computer-aided design (CAD) provides the tools to create a structured geometric model of an entity, which is usually available for a new or well-documented structure. For an existing structure with possible modifications, usage of terrestrial or even aerial laser or photogrammetric scanners can be used [7]. The automatic creation of a CAD model is not yet an easy task. Computational mechanics offers tools for parametric simulation. In this way, it is possible to generate a large amount of data from ideal experiments that can be used for solving optimal design and inverse problems and for evaluating scenarios. The accuracy of the model may be different in regions of particular interest, e.g., a 3D non-linear model in the region where stress concentrates, failures, and other phenomena are expected, and whole regions may be linked to the sub-structure technique and describe the mechanical behavior macroscopically (see Figure 4). The existence of model(s) that support DT facilitates the cognitive properties of the digital system. The result is characterized by some authors as a cognitive digital twin (CDT) [29]. A dense large-scale model, which guarantees adequate accuracy and reliability of results, may under certain conditions decrease drastically while maintaining reliability in results. The concept of static and dynamic condensation is well-known in the linear analysis of structures and has been extended to nonlinear systems. A schematic representation of a reduced-order model applied on a reinforced frame is shown in Figure 4, where areas of interest near joints can be modeled by using nonlinear detailed finite element models,  
Figure 4: Schematic representation of reduced order modelling for a concrete structure. (**a**) A reduced model in which areas of interest near joints are modelled by detailed finite elements, and (**b**) a full-scale model. Figure 3: Schematic representation of DT for infrastructures. Sensors collect data, possible EDGE on-Site processing reduces the data that are transmitted to a central database, and finally users evaluate the data and extract information from them. while areas of less importance or less vulnerable to damages are modeled by simpler beam elements. Optimization is a necessary tool for achieving the best possible results in a problem to be solved by optimizing the parameters of the problem. In addition to the classical optimization methods, there are those inspired by nature that are very efficient (genetic algorithms, particle swarm algorithms, etc.) as well as so-called loose soft computing tools (neural networks and neuro-fuzzy inference).