## 4 Overview of Recent Applications  
The first recent digital twin application appeared in the field of mechanical engineering by General Electric and concerned the entire lifecycle of aircraft and engines. The engineering sector in construction will present great challenges and needs in the future. This company has paid a lot of attention to the field of digital twins with its applications in remote process monitoring, which yields savings [28], in the transmission of electricity and their units, and in many others that will be analyzed as applications in the work. Figure 6: Schematic representation of a DT that can be used for structural health monitoring and fatigue prediction within the hydraulic network of a hydraulic energy storage facility (inspired by information found in [31]). Another ambitious project is Britain's National Digital Twin in collaboration with the University of Cambridge [2]. The project aims to digitize national infrastructure to enable operations to be carried out that will save time and money and minimize problems in order to provide better services for all. This process is the evolution of building information modelling (BIM), building lifecycle management (BLM), and product lifecycle management (PLM) systems. Amongst the multitude of approaches to SHM, the digital twin model is gaining increasing attention. This model is a digital reconstruction (the digital twin) of a real-life asset (the physical twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter [16]. [PERSON] et al. [17] presented an exploratory study toward creating a digital twin of bridges for structural health monitoring purposes. The research involved an interdisciplinary collaboration between the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and the Alan Turing Institute (ATI), using two monitored railway bridges in Staffordshire UK as a case study. A synthesis of physics-based and data-driven analytical approaches has been explored and investigated for extracting greater value of information from the bridge SHM data. [PERSON] et al. [18] designed a new bridge maintenance system using a digital twin model concept. According to different maintenance tasks, the inventory and information requirements were investigated. The recorded responses from the sensors were used to update the digital twin model. Based on the design of the maintenance system, a pilot application at an existing cable-stayed bridge in Korea was conducted. [PERSON] et al. [19] proposed a DT-driven framework for the fatigue lifecycle management of steel bridges. The primary contribution presented in this work is the integration of the three main components of fatigue prediction, maintenance, and inspection/monitoring, which clarify the flow and dependency among data and form a dynamic closed-loop data stream between the virtual and physical spaces. The advantage of this DT-driven framework is that it returns more accurate management strategy results than existing frameworks do, indicating a further reduction in failure risks and lifecycle costs. [PERSON] et al. [20] presented a novel methodology for creating an operational digital twin for large-scale structures based on drone inspection images. The digital twin was primarily used as a virtualized representation of the structure, which was updated according to the physical changes during the lifecycle of the structure. The methodology was demonstrated on a wind turbine transition piece. A three-dimensional geometry reconstruction of a transition piece as manufactured was created using a large number (>500) of RGB images collected from a drone and/or several LiDAR scans. Comparing the reconstruction to the original design identified and quantified geometric deviations and production tolerances. An artificial intelligence algorithm was used to detect and classify paint defects/damages from images.