A streamlined bridge inspection framework utilizing unmanned aerial vehicles (UAVs)
Date
2019
Authors
Perry, Brandon J., author
Guo, Yanlin, advisor
Atadero, Rebecca, committee member
van de Lindt, John W., committee member
Beveridge, Ross, committee member
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Abstract
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.
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Subject
computer vision
remote sensing
bridge inspection
unmanned aerial vehicles
infrastructure inspection