Abstract
Remote sensing of elevated infrastructure via robotic platforms has recently gained increasing interest in bridge inspection. Such automated bridge inspection can not only overcome accessibility and safety challenges but also improve objectivity and consistency of the measured data compared to current visual inspection practices. With the advent of advanced technologies, quality assurance (QA) and quality control (QC) for robot-based bridge inspection become imperative. This study aims (1) to understand actionable inspection activities and procedures in route planning, sensor preparation and measurement, ground truth selection, and statistical analysis; (2) to outline assessment matrices and best practices for drone-based images to achieve a practical level of surface mapping accuracy and internal defect detection; and (3) to perform two case studies using drones, photogrammetry software, and ground control toward three-dimensional (3D) reconstruction of bridges and horizontal hyperspectral imaging. The proposed guidelines review the basic principles of remote sensing, non-contact inspection based on visual, thermal, and hyperspectral imaging, calibration procedure and criteria of cameras and Light Detection and Ranging (LiDAR) scanners, and field demonstration of emerging technologies for crack, corrosion, delamination, and spalling detection in bridge elements.
Recommended Citation
Chen, Genda; Taffese, Woubishet Z.; Sharma, Ritesh; and Shi, Zhenhua, "Final Report - QA/QC Guidelines on Drone-based Remote Sensing for Bridge Element Inspection" (2024). Project IM-4. 1.
https://scholarsmine.mst.edu/project_im-4/1
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Sponsor(s)
Office of the Assistant Secretary for Research and Technology U.S. Department of Transportation 1200 New Jersey Avenue, SE Washington, DC 20590
Keywords and Phrases
Remote sensing, drones, crawlers, quality assurance, quality control, elevated infrastructure
Report Number
INSPIRE-025
Document Type
Technical Report
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 Missouri University of Science and Technology, All rights reserved.
Publication Date
November 30, 2024
Comments
Principal Investigator: Genda Chen, Ph. D., P. E.
Grant #: USDOT # 69A3551747126
Grant Period: 11/30/2016 - 09/30/2024
Project Period: 07/001/2022 - 09/30/2024
This investigation was conducted under the auspices of the INSPIRE University Transportation Center.