{"id":{"repo_id":"colostate","oai_identifier":"oai:mountainscholar.org:10217/237400"},"canonical_url":"https://search.dev.ndltd.org/etd/colostate/oai:mountainscholar.org:10217/237400","repository":{"repo_id":"colostate","name":"Colorado State University","base_url":"https://api.mountainscholar.org/server/oai/request"},"display":{"title":"Digital twins for structural inspection, assessment, and management","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Perry, Brandon J., author","Guo, Yanlin, advisor","Atadero, Rebecca, committee member","van de Lindt, John, committee member","Mahmoud, Hussam, committee member","Ortega, Francisco, committee member"],"institution":"Colorado State University. Libraries","degree_name":"Doctor of Philosophy (Ph.D.)","degree_level":"Doctoral","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-27T19:13:04Z","subjects":[],"languages":["eng","English"],"rights":["Copyright and other restrictions may apply. 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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. 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