Digital twins for structural inspection, assessment, and management
Date
2023
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Abstract
With the rapid advancements in remote sensing, uncrewed aircraft systems (UAS), computer vision, and machine learning, more techniques to maintain and evaluate the performance of the built infrastructure become available; however, these techniques are not always straightforward to adopt due to the remaining challenges in data analytics and the lack of executable actions that can be taken. The paper proposes a Digital Twin, which is a virtual representation of structures and has a myriad of applications to better assess and manage civil infrastructure. The proposed Digital Twin includes the techniques to store, visualize, and analyze the data collected from a UAS-enabled remote sensing inspection and computational models that support decision-making regarding the maintenance and operation of structures. The data analysis module identifies the location, extent, and growth of a defect over time, the structural components, and connections from the collected image with artificial intelligence (AI) and computer vision. In addition, the three-component (3C) dynamic displacements are measured from videos of the structure. A model library within the digital twin to assess the structure's performance, which includes three types of models, is proposed: 1) a visualization model to provide location-based data query, 2) an automatically generated finite element (FE) model as a basis for simulation, and 3) a surrogate model which can quickly predict a structure's behavior. Ultimately, the models in the library suggest executable actions that can be taken on a structure to better maintain and repair it. A discussion is presented showing how the Digital Twin can assist decision-making for structural management.