Quantitative sensing of bridges, railways, and tunnels with autonomous unmanned aerial vehicles.

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Quantitative sensing of bridges, railways, and tunnels with autonomous unmanned aerial vehicles.
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    Managing a growing population of deteriorated transportation infrastructure systems (i.e. bridges, railways, tunnels) is one of biggest challenges faced by the nation. Traditional inspection and monitoring techniques (e.g., visual inspection, mechanical sounding, rebound hammer, cover meter, electrical potential measurements, ultrasound, ground penetrating radar) for bridges, railways, and tunnels require lane closure and are labor intensive and time-consuming. Existing remote sensing techniques (e.g., LiDAR, aerial photos) using fixed-wing aircraft are capable of conducting large-area inspection but are constrained by viewing areas accessible only from above, at a large distance, and are not capable of inspecting all areas of interest (e.g., bridge piers, bridge girder bottom, and tunnel walls). Automated, low-cost, efficient inspection techniques for interrogating critical components of bridges, railways, and tunnels are needed. The objective of this project is to develop a system-level, decision-support unmanned aerial vehicle (UAV) sensing system for the autonomous, efficient inspection of bridges, railways, and tunnels, using radar, digital image correlation, and thermal sensors. The proposed UAV sensing system represents the next generation of rapid, low-cost interrogation technology for infrastructure inspection and monitoring of critical transportation infrastructure. In this final report, major research achievements and findings from our conducted tasks are summarized and reported. Additional technical details in our research tasks can be found in submitted quarterly reports. The objective of this project is to develop an unmanned aerial vehicle (UAV) system that can be used to autonomously interrogate numerous areas on civil structures easily without requiring expensive, time consuming aerial lifts or inconsistent visual inspections. The proposed sensing platform includes innovative continuous wave imaging radar and digital image correlation to monitor structures or quantify damage. This project represents the next generation of localized, autonomous inspection and monitoring methods using robotic platform.
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