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Drones are increasingly used during routine inspections of bridges to improve data consistency, work efficiency, inspector safety, and cost effectiveness. Most drones, however, are operated manually within a visual line of sight and thus unable to inspect long-span bridges that are not completely visible to operators. In this paper, aerial nondestructive evaluation (aNDE) will be envisioned for elevated structures such as bridges, buildings, dams, nuclear power plants, and tunnels. To enable aerial nondestructive testing (aNDT), a human-robot system will be created to integrate haptic sensing and dexterous manipulation into a drone or a structural crawler in augmented/virtual reality (AR/VR) for beyond-visual-line-of-sight (BVLOS) inspection of bridges. Some of the technical challenges and potential solutions associated with aNDT&E will be presented. Example applications of the advanced technologies will be demonstrated in simulated bridge decks with stipulated conditions. The developed human-robot system can transform current on-site inspection to future tele-inspection, minimizing impact to traffic passing over the bridges. The automated tele-inspection can save as much as 75% in time and 95% in cost. 

Bridge Weigh-in-Motion (B-WIM) is the concept of using measured strains on a bridge to calculate the axle weights of trucks as they pass overhead at full highway speed. There exist a consensus that conventional instrumentation faces substantial practical problems that halts the feasibility of this theory, namely cost, installation time and complexity. This article will go through a new concept by moving toward the first Portable Bridge Weigh-In-Motion (P-B-WIM) system. The system introduce flying sensor concept which consist of a swarm of drones that have accelerometers and able to latch bridge girders to record acceleration data. Some perching mechanisms have been introduce in this paper to allow drones to latch bridges girders. At the same time, a new algorithm is developed to allow the B-WIM system to use the acceleration data to estimate the truck weigh instead of the strain measurements. The algorithm uses the kalman-filter-based estimation algorithm to estimate the state vectors (displacement and velocities) using limited measured acceleration response (from drones). The estimated state vector is used to feed a moving force identification (MFI) algorithm that shows good results in estimating a quarter car model weight.