Visual inspection

Engineering

Analytics

Aerospace engineering

Marine engineering

Georeference

Real-time computing

Operating system

Bridge (graph theory)

Data mining

Computer science

Cloud computing

Physical geography

Structural health monitoring

Point cloud

Geography

Leverage (statistics)

Internal medicine

Artificial intelligence

Structural engineering

Medicine

Aeronautics

Measurement

Recently, the rapid development of commercial unmanned aerial vehicles (UAVs) has made collecting images of bridge conditions trivial. Measuring the damage extent, growth, and location from the collected big 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 3D location of defects using computer vision techniques; (2) generate a 3D point-cloud model and segment structural elements using human-in-the-loop machine learning; and (3) establish a georeferenced element-wise 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.

Streamlined bridge inspection system utilizing unmanned aerial vehicles (UAVs) and machine learning