Project Title
Drone-Based Measurements for Bridge Field Testing – Development Phase
Researcher(s)
Mostafa Tazarv, PhD, PE – PI
Marco Ciarcia, PhD – Co-PI
Kwanghee Won, PhD – Co-PI
Project Description
More than 40% of the US in-service bridges are at least 50 years old, 10% are posted with limited passing loads, and the cost to repair the US bridges in their current conditions is $125 billion. Load rating is usually performed to assess the performance and capacity of old bridges, and is carried out either analytically or experimentally. Despite several advantages of bridge field testing, it is not a common practice for the evaluation of bridge performance. Instead, the analytical load rating is more common. One main reason is the cost of field operations and instrumentation. The single inspection cost of a typical bridge is approximately $5,000, and the inspection cost for a more complex structure such as a truss bridge will exceed $50,000. One effective way to reduce the inspection cost is to utilize new technologies such as drones. Furthermore, recent studies have confirmed the feasibility of field testing bridges using computer vision in which video recording from fixed cameras are used to extract bridge response with no need to use conventional sensors. The main goal of this proposal, which is the first phase of a two-phase project, is to develop a framework and necessary tools to field test bridges using drones and computer vision. Instead of using displacement/strain sensors and data acquisition system (DAQ), a fleet of drones will be deployed each equipped with a camera. The drones’ movement will be compensated using a ground-based camera and/or fixed targets. The main benefit of the proposed work is a substantial reduction of the field testing time, effort, and cost by eliminating the need for boom trucks, sensors, DAQ, field preparation, and the traffic closure. The main products in this phase will be a framework and an open-source computer program that processes the recorded video of a bridge during load testing to extract girder displacements and to estimate girder distribution factors.
Marco Ciarcia, PhD – Co-PI
Kwanghee Won, PhD – Co-PI