# An Operational Image-Based Digital Twin for Large-Scale Structures
## 1 Introduction
The images are then used to make a 3D reconstruction of the object by implementing post-processing techniques using matching key points through the structure from motion (SfM) or multi-view stereopsis (MVS) algorithms [9]. Once the Digital Twin is created with the 3D digital reconstruction of the large-scale structure and information on the manufacturing defects, it must be continuously updated throughout the life cycle of the structure, including transportation, installation, operation, maintenance, and repair. Advanced and robust inspection systems and sensors must be developed, such that the environmental exposure and deterioration of structures and materials may be monitored. Through the Digital Twin, the state of the individual structures can always be assessed, giving valuable information to the operator. Based on this information, the operator can make decisions that affect the lifetime of its assets, e.g., changing the operation mode to reduce the loading on the structures. Furthermore, information about necessary repairs will be available through processing data in the Digital Twin. Currently, inspections of large-scale offshore structures during operation, such as wind turbine structures, are often carried out manually using e.g., lifts and rope access. This is a time-consuming, expensive, and potentially dangerous task. Therefore, the use of drones is a potential alternative for this process, as they could be used to carry out remote, faster, safer, and cheaper inspections [10]. However, drone inspections could bring other challenges, such as creating a large amount of images and data that need to be processed. In addition, users have difficulty precisely and correctly locating surface defects and damages because the images are often shown out of context. Therefore, there is a need for a faster and more efficient solution to store, analyze, and report on the vast amount of data the inspections provide. Artificialintelligence (AI) and Digital Twins provide these opportunities. AI can be used to process many more images, much faster than a human can handle. Observations from multiple images can be grouped into unique issues by AI and, at the same time, the 3D location can be calculated so the issues can be automatically mapped in the Digital Twin. The result is a single issue that can be seen from multiple images and angles through the Digital Twin. In addition, smaller defects/damages can potentially be detected by AI, which would have been missed by the human eye. An example of wind turbine surface damage detection using AI-aided drone inspection is found in [10]. Depending on the application and the objective, the methodologies of creating Digital Twins can be significantly different. In the present study, we propose a novel methodology to create an operational image-based Digital Twin for large-scale offshore structures. We use drone inspection images and/or LiDAR scans to create a 3D geometric reconstruction of the structure, compare the reconstructed model to the original design to locate and quantify geometric deviation, apply AI to identify surface defects/damages, and map the identified surface defects/damages to the geometric reconstruction. The entire process can be done automatically, allowing efficient model updating and status tracking with new sets of inspection images. The proposed methodology is demonstrated on a wind turbine transition piece (TP) and can be applied to other large-scale structures where structural health monitoring and asset management are needed.