MPC Research Reports
Report Details

Title:	A Streamlined Bridge Inspection Framework Utilizing Unmanned Aerial Vehicles
Authors:	Brandon J. Perry, Yanlin Guo, Rebecca Atadero, and John W. van de Lindt
University:	Colorado State University
Publication Date:	Dec 2021
Report #:	MPC-21-443
Project #:	MPC-592, MPC-535
TRID #:	01832401
Keywords:	bridges, drones, inspection, machine learning, structural health monitoring

Abstract

The recent rapid development of commercial unmanned aerial vehicles (UAVs) has made collecting images of bridge conditions trivial. Measuring a defect's extent, growth, and location from the collected large image set, however, can be cumbersome. This paper proposes a streamlined bridge inspection system that offers advanced data analytics tools to automatically (1) identify type, extent, growth, and 3-D location of defects using computer vision techniques; (2) generate a 3-D point cloud model and segment structural elements using human-in-the-loop machine learning; and (3) establish a georeferenced elementwise as-built bridge information model to document and visualize damage information. This system allows bridge managers to better leverage UAV technologies in bridge inspection and conveniently monitor the health of a bridge through quantifying and visualizing the progression of damage for each structural element. The efficacy of the system is demonstrated using two bridges.

How to Cite

Perry, Brandon J., Yanlin Guo, Rebecca Atadero, and John W. van de Lindt. A Streamlined Bridge Inspection Framework Utilizing Unmanned Aerial Vehicles, MPC-21-443. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2021.