QA/QC Guidelines on Drone-based Remote Sensing for Bridge Element Inspection

This project aims to develop the quality assurance (QA) and quality control (QC) guidelines for robot-based bridge inspection. These QA/QC guidelines are developed to ensure good quality of multimodal sensor data for the inspection of bridges. They can help maintain a high degree of accuracy and consistency in bridge inspection data. The key factors that influence the quality of inspection data include unmanned aerial system inspection platforms, measurement sensors, measurement environments, and deterioration conditions. The scope of work includes (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 crawler-based nondestructive evaluation (NDE) data to detect internal defects; and (3) to perform case studies using drones/crawlers, NDE tools, photogrammetry software, and ground control and check points. The proposed guidelines cover the basic principles of remote sensing and NDE, contact inspection of fracture critical elements, non-contact inspection based on visual, thermal, and hyperspectral imaging, calibration procedure and criteria of cameras and LiDAR scanner, and field demonstration of emerging technologies for crack, corrosion, delamination, and spalling detection in bridge elements. The QA/QC guidelines are illustrated in field applications to multi-girder bridges with a highlighted hybrid vehicle flying to the region of a bridge and engaging with the bridge girder for detailed inspection on a stationary platform.

Language

  • English

Project

Subject/Index Terms

Filing Info

  • Accession Number: 01847758
  • Record Type: Research project
  • Source Agency: Inspecting and Preserving Infrastructure through Robotic Exploration University Transportation Center
  • Contract Numbers: 69A3551747126
  • Files: UTC, RIP
  • Created Date: May 31 2022 10:00AM