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Project Details

Title:Development of an Autonomous Transportation Infrastructure Inspection System Based on Unmanned Aerial Vehicles
Principal Investigators:Yanlin Guo, Rebecca Atadero, and John van de Lindt
University:Colorado State University
Grant #:69A3551747108 (FAST Act)
Project #:MPC-592
RH Display ID:154123
Keywords:bridges, drones, infrastructure, inspection, remote sensing


With transportation infrastructure in the United States aging and deteriorating, maintenance and inspection of the existing infrastructure become critical. Accurate and efficient inspections inform engineers/managers for better repair decisions/planning, load-rating, and effective management of limited resources. Current human-based infrastructure inspection may be costly, lack quantitative measures of damage, as well as pose a danger to inspectors. Thus, there is a need to develop more cost-effective, quantitative, and safe approaches for infrastructure inspection. In response to this need and recognizing the rapid technological improvement of UAV-based remote sensing, this project will explore the potential of UAV-based remote sensing technology in transportation infrastructure inspection with a focus on bridges. The ultimate goal of the study is to develop an autonomous and quantitative infrastructure inspection procedure that requires minimum human intervention. The three project objectives include: (1) Develop an automated process to identify different elements of a structure and establish an as-built element-wise building information model (BIM), (2) Develop an automated damage evaluation tool that can identify the type, location and amount of structural damage for each element; and (3) Develop a damage documentation tool that maps the identified element-wise damage to the corresponding bridge element in a BIM model.

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