Motion (physics)

Quantum mechanics

Linguistics

Engineering

Philosophy

Biology

Identification (biology)

Axle

Bridge (graph theory)

Power (physics)

Computer science

Botany

Sensor fusion

Structural health monitoring

Physics

Trajectory

Fusion

Simulation

Astronomy

Image (mathematics)

Weigh in motion

Machine vision

Internal medicine

Reliability (semiconductor)

Artificial intelligence

Structural engineering

Information fusion

Medicine

Optical flow

Computer vision

Measurement

Accurately identifying moving loads is of significance for the health monitoring of bridges. However, since the existing identification techniques can only realize load identification in one direction or for part of bridges, it is still a challenge to simultaneously identify transverse and longitudinal loads on the full deck of bridge. This paper proposed an information-fusion-based method for the load identification to be applied to bridges of different lengths. In this method, the pavement-based weigh-in-motion system (WIMs) laid out at the beginning of the bridge is used to obtain the weight of vehicles captured by cameras. The videos of traffic flow acquired by multiple cameras arranged along the bridge are employed to calculate the vehicle’s trajectory and location. The weight and location data are matched when the vehicle in the video crosses the piezoelectric sensor of WIMs for the same time as the WIMs records a weight information. Further, since the vehicles are equivalent to concentrated loads, values and locations of all moving loads on the whole bridge are identified in real time. The reliability and accuracy of the proposed approach is verified by multi-view 3D simulation video data and the field data from a ramp bridge.

Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision