USW and about 500 m2 (5000 ft2) for GPR on survey lines with a 60 cm (2 ft) spacing. More information about the perfor- mance of NDE technologies for the condition assessment of bridge decks can be found in an SHRP 2 report (Gucunski et al. 2013). Condition assessment of other bridge components can be done using the same NDE technologies and devices as for concrete bridge decks. The biggest challenge in the NDE tech- nology deployment is access to the element to be evaluated. The application of the previously described five NDE technol- ogies in the condition assessment of concrete bridge piers and prestressed girders is shown in Figure 2. Automation of NDE Data Collection Comprehensive condition assessment of concrete bridges can be done using manual NDE technologies. However, and as illustrated in the previous section, manual multi-NDE tech- nology data collection requires significant time and effort. In addition, in bridge deck evaluation, and in cases when super- structure and substructure evaluation is done using snoopers, work zones for inspection cause traffic interruptions. All are adding to the cost of inspections, slowing or interrupting the traffic flow, and increasing risks for the inspectors and the traveling public. Considering that there are more than 620 000 bridges in the US, manual inspection and condition monitoring of the bridge network using NDE is not feasible. Automation of NDE data collection is, therefore, critical for their wide adoption as a tool for accurate condition assessment and monitoring and, thus, economic bridge management. The following sections describe some efforts in the automation of NDE data collection in the inspection of bridge decks and other bridge components. Automation of Bridge Deck NDE One of the earliest attempts for automated multi-NDE of concrete bridge decks was the robotic system BETOSCAN developed at the German Federal Institute for Material Research and Testing (BAM) (Raupach et al. 2009). BETOSCAN enabled the deployment of multiple NDE devices: ultra- sonic, potential mapping, microwaves, and cover meters. While the system had significant capabilities, it could be deployed in the evaluation of smaller areas only because of a single NDE sensor installation. The RABIT (Robotics Assisted Bridge Inspection Tool) platform (Gucunski et al. 2017) brings elements of the previous efforts and implements them in a much bigger robotic platform with multiple NDE devices or sensor arrays (Figure 3). RABIT integrates four NDE technologies: ER, GPR, USW, and IE. There are four ER probes and two acoustic arrays on the front end of the platform. The two acoustic arrays are equivalent to 16 IE devices and 12 USW devices. Two GPR arrays are mounted on the rear end of the platform, each having eight antenna pairs of dual polarization. Finally, two high-resolution cameras for deck surface imaging are mounted on the front end. RABIT is a fully autonomous system, whose navigation was achieved through an integration of the differ- ential global positioning system (DGPS), inertial measurement unit (IMU), and the wheel odometry. The data collection path is preprogrammed in terms of the deck sections to be evalu- ated and increments in the data collection. All the NDE data are streamed and monitored in real-time in the command van (Figure 3). The data collection production rate depends on the length of the bridge and data collection increment. For bridge decks approximately 100 m (330 ft) long, and data collection increment of 0.6 m (2 ft), RABIT can evaluate about 350 to 400 m2 (3500 to 4000 ft2) per hour. A sample of results from the RABIT includes the condition maps of a bridge shown in Figure 4, the ER map describing the severity of the corrosive environment, the IE delamination map, and GPR and USW maps of qualitative and quantitative concrete quality assessment. The condition maps are comple- mented by a high-resolution image of the deck surface which is obtained from stitched camera images. A section of the bridge deck with visible cracks is shown in the same figure. Such images become permanent records of the deck surface that can be reviewed at any time. GPR USW-IE ER ER USW-IE Camera Camera ER ER GPR Figure 3. Robotic platform RABIT: (a) front view (b) back view (c) unloading from the command van (d) screens in the command van. J A N U A R Y 2 0 2 3 • M AT E R I A L S E V A L U AT I O N 59 2301 ME Jan New.indd 59 12/20/22 8:15 AM

While the data collection speed of RABIT is significantly higher than of the manual data collection (approximately three times higher than a team of five NDE technicians), it could be significantly increased through the use of air-coupled and/or rolling probes that would eliminate RABIT’s test point stops. As an example, the use of air-coupled acoustic and vertical electrical impedance (alternative to ER) testing, along with GPR and high-definition imaging, was implemented on an NDE platform that enabled data collection at a walking speed (Pashtouni et al. 2020). Climbing Robots for Bridge Superstructure and Substructure The development of climbing robot systems for bridge inspec- tion has received great attention recently (Tirthankar et al. 2018 Nguyen and La 2021). Inspired by the way that animals and insects move, robots have demonstrated the feasibility of climbing over different connectors and surfaces on bridges (Minor et al. 2000 Nguyen and La 2019 Nguyen et al. 2020). However, each bridge has many locations to be checked, and they are usually not close together, so it will take a long time for those climbing robots to complete the inspection of a bridge, not to mention that the calculation to move also takes a lot of time and requires intelligent algorithms. Studies on using drones for inspection have found that drones allow a quick, efficient overview without being limited to the bridge element material. However, a comprehensive inspection of bridges requires multiple positions for in-depth testing, while the current drone capabilities can provide only a visual inspec- tion. Unlike the approaches mentioned above, a new hybrid robotic design is presented, which considers the advantages of a drone’s flying flexibility and a mobile robot’s steady climbing capability to perform in-depth inspections of bridges. With the new design, the in-depth inspection of the bridge will also be conducted faster because of the drone’s maneuverability. The mobile robot part is equipped with permanent magnets that can change the distance from the steel surface. Changing the distance between the magnet and the steel surface allows the robot to switch its operating modes between landing, taking off, and moving. The design concept of this robot is illustrated in Figure 5. The robot is integrated with multiple sensors: Intel camera T265 and GPS for robot location tracking, infrared sensors for a safe landing, and a GMR sensor for crack detection. The onboard computer for processing is a Raspberry Pi 4. The PX4 flight controller is for controlling the robot. The robot is sur- rounded by a sphere cage to protect it from a collision with the bridge. Overall, the robot is designed to work in two modes: ME | NDEOFBRIDGES 0 10 20 10 20 30 30 40 50 60 70 80 Longitudinal distance (ft.) Longitudinal distance (ft.) 90 100 0 10 20 30 40 50 60 70 80 90 100 Sound Fair Poor Serious (kOhm-cm) (ksi) 110 120 130 140 150 160 ER 0 10 20 10 20 30 30 40 50 60 70 80 90 100 110 120 130 140 150 160 IE 0 10 20 10 20 30 30 40 50 60 70 80 Longitudinal distance (ft.) Longitudinal distance (ft.) 90 100 110 120 130 140 150 160 GPR 0 10 0 3000 4000 5000 6000 (dB) –30–25–20–15–10 –8 –7 –6 –5 –4 –3–2.5–2–1.5–1–0.5 0 0.5 1 1.5 2 2.5 20 10 20 30 30 40 50 60 70 80 90 100 110 120 130 140 150 160 USW Figure 4. Condition maps from four NDE technology surveys: (a) ER (b) IE (c) GPR (d) USW and (e) section of the deck surface image. 60 M AT E R I A L S E V A L U AT I O N • J A N U A R Y 2 0 2 3 2301 ME Jan New.indd 60 12/20/22 8:15 AM Lateral distance (f Lateral distance (f Lateral distance (f Lateral distance (f