## 1 Introduction  
The digital twin (DT) is the indistinguishable digital replica of an object, a physical system, or a procedure for the simulation, prediction, and optimization of its performance. DT fits, without a doubt, into modern technological and scientific developments. One of the main characteristics of digital twin technology is its connectivity. Distributed, online, low-cost sensors are being developed to support the internet of things (IoT); big data are generated and transmitted through modern wireless networks (e.g., 5G); and artificial intelligence (AI) and deep learning (DL) promise to accomplish complicated data-processing tasks. On the other side, multi-physics computational mechanics modeling reproduces physical reality with confidence. The resulting integrated system is a cyber-physical entity. All these elements pave the way to the so-called Industry 4.0 revolution, following stream power, electricity, and microchips. Industry 4.0' is the term currently used to describe the trend of digitalization and data exchange in production lines. The emphasis is placed on the 'digital factory', where the production is constantly supervised so that an updated and reliable model is available at all times that allows for the study of possible scenarios, including predictions of the remaining life for the predictive maintenance of equipment. Data collection and exchange between physical and digital systems is facilitated through the 'Internet of Things' and the availability of quick networks such as 5G. Multi-physics simulation and data analytics support digital twins. The creation of digital twins and their continuous updating based on the usage of big data of measurements during the life of a system constitutes a natural continuation of scientific disciplines such as modeling, parametric technical modeling, model and parameter identification, and process or structural health monitoring. It is feasible due to the continuous development of computational techniques, large-scale optimization through classical or nature-inspired methods, cheap sensors, and wireless data transfer (IoT and 5G). Looking at the techniques used, one may recognize that digital twins are a balanced mixture of multi-physics models, statistics, and artificial intelligence for the reconstruction of the natural reality through field measurements on possibly simplified or reduced-order digital models. The existence of data and a mechanism for model update and parameter identification are considered to differentiate a DT from a classical engineering model [1]. The evaluation of the DT concept can be traced in several sources, among them the references [2; 3; 4; 5; 6], which will be discussed later in this paper. Parametric computational models and classical parameter identification problems were developed earlier, as described in references [7; 8; 9; 10; 11; 12; 13; 14; 15]. The application of digital twin methodology on civil infrastructures is a current research topic. Wind turbines or floating structures are exposed to unpredictable and extreme loads. Therefore, their fatigue response and life prediction are challenging tasks that can be resolved by digital twin technology. When used as a part of a hybrid energy storage system, even more classical structures, such as hydropower facilities, are loaded unpredictably and require more detailed study. Applications on infrastructures are described in recent publications such as [16; 17; 18; 19; 20; 21; 22; 23; 24; 25], with an emphasis on extremely loaded structures such as lightweight steel structures and wind turbines that are prone to fatigue [26; 27]. The purpose of this paper is to facilitate an introduction to these new fields for people with a classical computational mechanics background, through the review of recent digital twin applications on infrastructure. The ways of connecting the introduced concepts of digital twins with conventional parametric modeling, reverse engineering, and parameter identification will be discussed.