# An Operational Image-Based Digital Twin for Large-Scale Structures
## 1 Introduction  
Digitalization is one of the priorities in transforming industrial research and innovation [1]. In addition, the offshore industry can be significantly transformed by embracing advanced digital technologies such as the Internet of Things, Big Data, and Cloud Computing, together with sensor technologies and high-fidelity structural modeling techniques. All of these technologies come together in what is broadly described as a Digital Twin. In this paper, the development of Digital Twin technologies for large-scale offshore structures is explored. The term \"Digital Twin\" has many different definitions in both academia and industry, in a rather broad sense. A state-of-the-art and a state-of-the-use review provide more than 400 definitions of Digital Twin [2]. However, in this work we adopt the definition of [PERSON] et al. [3], that a Digital Twin is a \"dynamic virtual representation of a physical object or system across its lifecycle, using real-time data to enable understanding, learning and reasoning.\"  
The vision for using a Digital Twin for smart, efficient, and reliable large-scale offshore structures is further elaborated in [4]. A Digital Twin can be connected to the real structure through a live feed of data streamed from embedded sensors, meteorology data, and, most importantly, from structural health monitoring and inspection systems. Ideally,therefore, a Digital Twin can be used to make predictions of the development of damage, based on the current damage state that is recorded and updated in a structural health journal maintained for each structural component. Based on physical models, the Digital Twin can then be used to simulate the effect on the structure for different damage modes and different operational scenarios, e.g., during installation or in the event of a storm, grid loss, shutting down, etc. [4]. A Digital Twin of a large-scale structure should start at the early design stage. Within the manufacturing stage, the monitoring of the process parameters is essential for future damage identification. Numerous in-service failures originate from fabrication defects. Therefore, each structure needs to be digitally reconstructed and scanned for flaws before leaving the factory; accordingly, a Digital Twin is updated with information on the manufacturing history and fabrication defects. This should be carried out automatically to limit the workload of such a task. Technologies such as autonomous robots that adapt to the surface of large-scale offshore structures, such as wind turbine blades, are now being developed to perform three-dimensional (3D) scans and to identify surface manufacturing defects before the structures leave the factory [5]. Although this type of technology seems to have good potential in terms of accuracy, the long scanning time and high costs, in addition to the robustness of the system, make it difficult to apply this technology on a large scale and use it in other lifecycle stages, such as inspection during operation. Alternative 3D reconstruction technologies, such as LiDAR (light detection and ranging) scanning and drone-based photogrammetry, implemented in the building and construction industry [6; 7; 8], could also be applied in the offshore industry not only to perform 3D digital reconstruction of large-scale structures but also to identify surface defects and damages. Scanning using LiDAR scanners has proven to be a good option, in terms of high extracting speed and high accuracy, for obtaining a 3D digital representation of complex objects, such as bridges, without physical contact [6]. In addition to these two advantages, drone-based photogrammetry has been shown to decrease overall reconstruction time and costs relative to the common manual and direct inspection techniques that are used in the building and construction industry [7; 8]. In this technology, analog cameras are mounted on unmanned aerial vehicles (UAVs), commonly known as drones, to take high-resolution RGB aerial images for different points of the object.