# An Operational Image-Based Digital Twin for Large-Scale Structures
## 7 Concluding Remarks  
In this study, we proposed and demonstrated a novel concept and developed a working methodology to create an operational Digital Twin of large-scale structures based on drone inspection images and LiDAR scans. The Digital Twin presented in this study is a 3D visual representation of a physical structure, e.g., the wind turbine transition piece demonstrated in this study, on which both drone inspection and LiDAR scans were conducted. The Digital Twin could (1) visualize and quantify the geometric derivation of the real structure from the design, (2) map the surface defects/damages including the sizes, locations, and types detected by AI on the 3D geometric reconstruction, and (3) update whenever new sets of inspection data became available.  
The Digital Twin concept presented in this study is operational and opens many opportunities for preventive maintenance and optimal asset management of large-scale structures and infrastructure. Further improvement of the Digital Twin is necessary to make it more accurate, automated, robust, and faster. The remaining challenges include improving the accuracy of 3D geometric reconstruction when using inspection images of shining surfaces; automatizing and streamlining the working flow with a huge amount of data; and enhancing the robustness of the Digital Twin for other applications on different large-scale structures. The proposed framework is modularized and has flexibility to adapt and upgrade. For example, the current study uses AI to train the damage detection network; this module can be replaced by other methods, such as wavelet and contourlet transforms, which can also detect damages from images. In addition, the damage-related information obtained from the drone-based images could be complemented with data obtained from sensors installed in the structure, such as strain gauges and accelerometers, and from other  
Figure 13: (**a**) The meshed CAD model is superimposed with the positions of all the paint defects/damages found in the images collected during one flight. (**b**) The green points show the paint defects/damage in a small section of the tower.  
inspection techniques, such as thermography. Advanced numerical tools can be further developed to understand these sensor signals and inspection data, and to simulate the consequences of structural and material deterioration. These challenges will be the focus of our future study based on the foundation provided in the current work.  
[PERSON]-[PERSON] implemented software for geometry reconstruction, developed the concept for defect/damage mapping, and wrote the original manuscript; [PERSON] developed the research idea, supervised and managed the research, analyzed results, and wrote the original manuscript; [PERSON] implemented AI algorithms for defect/damage detection and contributed to writing the manuscript; [PERSON] contributed to the overall idea on Digital Twin and to writing the manuscript; [PERSON] contributed to data structure, working flow, testing, and writing the manuscript; [PERSON] provided all drone images and LiDAR measurements and contributed to writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the QualiDrone Project (Intelligent, autonomous drone inspection of large structures within the energy industry, 64020-2099) through the Danish Energy Technology Development and Demonstration Program (EUDP).  
Not applicable to this study.  
We would like to thank BLADT INDUSTRIES A/S for supporting this study. The authors are grateful to [PERSON] from Energy Cluster Denmark, [PERSON] and [PERSON] from Zebicon A/S, and [PERSON] from Desupervised for their constructive comments and useful discussions in the QualiDrone project.  
The authors declare no conflict of interest.