Browsing by Author "Perry, Brandon J., author"

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Open Access
A streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs)
(Colorado State University. Libraries, 2019) Perry, Brandon J., author; Guo, Yanlin, advisor; Atadero, Rebecca, committee member; van de Lindt, John W., committee member; Beveridge, Ross, committee member
The lack of quantitative measures and location information for instances of damage results in human-based bridge inspections that are variable and subjective in nature. With bridge owners and managers tasked with making major maintenance/repair decisions with inadequate funding and resources, it is appealing to develop a transparent bridge inspection and evaluation system that integrates field inspection and documentation of damage with quantitative measures and geo-referenced locations in a holistic process. A new, streamlined bridge inspection framework based on unmanned aerial vehicles (UAVs) is proposed to improve the efficiency, cost-effectiveness, and objectivity of these inspections while enhancing the safety of inspectors. Since the current bridge inspection practices use a component based structural rating system, the new UAV-based bridge inspection system should also follow a component-wise damage evaluation system to enable the seamless adoption of this new technology into practice. To provide bridge managers/owners with the streamlined decision-making support, this new system uniquely integrates UAV-based field inspection, automated damage/defect identification, and establishment of an element-wise As-Built Building Information Model (AB-BIM) for the damage documentation in a holistic manner. In this framework, a UAV platform carrying visual sensors first collects data for identifying defects (i.e. cracks, spalling and scaling of concrete). Next, an automated damage detection algorithm is developed to quickly extract quantitative damage information (i.e. type, size, amount, and location) from the data. By using UAV-enabled photogrammetry and unsupervised machine learning techniques, this system can automatically segment the bridge elements (i.e. beam, girders, deck, etc.) from a 3D point-cloud with minimal user input. In the end, the damage information is mapped to the corresponding structural components of the bridge and readily visualized in the AB-BIM. The documented element-wise damage information with quantitative measures in conjunction with the 3D visualization function in the proposed system can provide bridge managers with a transparent condition evaluation and a one-stop decision making support which can greatly ease the planning of repair/maintenance. The feasibility of this approach is demonstrated using a case study of a Colorado bridge.
Open Access
Digital twins for structural inspection, assessment, and management
(Colorado State University. Libraries, 2023) 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
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.