This paper describes the development of new air-coupled ultrawideband ground-penetrating radar (GPR) for highway pavement and bridge deck inspections that can achieve high spatial resolution and high inspection performance while operating on vehicles driving at regular highway speeds. The major design features include dual-frequency band operation, 8 gigasamples per second high-speed real-time data acquisition, high-speed large volume data transmission and storage, and customized signal-processing algorithms for GPR image enhancement and feature extractions. For design validations, steel reinforcing bar detection experiments in various setups are conducted. The measurement results show good agreement with the subsurface feature configurations.

Early and accurate detection, location and assessment of damage in reinforced concrete bridge decks may be beneficial in the scheduling and performance of maintenance and rehabilitation activities. This article presents the results of a multiple sensor study of the condition of the reinforced concrete deck of the Van Buren Road Bridge in Dumfries, VA. The tests compared the following five different methods: (1) visual inspection and photographic recording; (2) half-cell electrochemical potential; (3) impulse type multipoint scanning ground penetrating radar; (4) chain drag and (5) impact echo. The bridge was tested on two separate occasions. The results of the tests were that each instrument nominally performed and collected data as expected, but that the condition assessments did not necessarily agree. The data are registered, overlaid and compared. The potential for developing automated multisensor systems that fuse data for efficient and effective bridge deck measurements is discussed.

Harmful algal blooms (HABs) frequently occur in coastal and inland water bodies, resulting in detrimental health and economic consequences. In freshwater, HABs are often dominated by cyanobacteria (CyanoHABs or cHABs), which produce a suite of secondary metabolites ranging from taste and odor compounds to liver and neurological toxins. Recently, Uncrewed Aerial Vehicles (UAVs) have emerged as a cost-effective remote sensing solution for using imaging cameras to monitor the distribution of HABs. However, most of these systems are unable to concurrently measure crucial water parameters, requiring additional in situ measurements and sample collection. The UAV system developed in this work presents a comprehensive platform that integrates multiple water sensors, including near-infrared (NIR) multispectral cameras and temperature, pH, and turbidity sensors. The integrated sensor system can not only monitor HAB distributions but also measure essential water quality parameters. In addition, the electromechanical 3D-printed water sampling structure is designed to collect water samples for laboratory analysis of chemical and biological samples. To ensure real-time data transmission, a Long-Range Wide Area Network (LoRaWAN) is developed. An ArcGIS database is implemented for water quality and HAB distribution mapping. Comprehensive field tests have been performed for system performance validation and assessment.

The use of chloride-based deicing agents to help clear U.S. highways of roadway hazards leads to associated chemical related problems. Fouling of local rivers and streams due to runoff of the waterborne chlorides is significant and has contributed to local ordinances that are attempting to force state agencies to reduce, if not eliminate, the use of these chlorides (typically at the cost of increased driving hazards). With respect to the corrosion aspects of chloride application, cracks that occur in the roadway/bridge pavement allow water to seep into the pavement carrying the chloride to the rebar with the resultant increase in corrosion. The costs of this corrosion are considerable and have led to the widespread use of chloride/water impermeable membranes on roadways and especially within bridges. Fiber optic sensors have repeatedly been shown to provide measurement capabilities of parameters within such reinforced concrete structures. Development of fiber optic chloride sensors capable of being embedded within a roadway or bridge deck is reported. © 1998 Society of Photo-Optical Instrumentation Engineers.

Ground penetrating radars (GPRs) carried by mobile platforms, such as vehicles and drones, have been applied in various applications, for instance, subsurface utility detection, structural health inspection, and autonomous driving. However, existing GPR systems are not able to operate autonomously and adaptively due to several challenges, including the lack of intelligence, uncertain and dynamic nature of sensing environments, and huge state and action spaces. To overcome these challenges, in this article, we propose an autonomous cognitive GPR (AC-GPR) enabled by a deep reinforcement learning (DRL) approach. Specifically, the operation of the proposed AC-GPR is first formulated as a sequential decision process. A novel reward function is developed for the DRL model by defining and combining two different types of entropy-based rewards resulting from object detection and recognition, respectively. A deep Q-learning network (DQN) is developed to address the extreme curse of dimensionality in the state space and learn a policy directing the actions of the AC-GPR. The AC-GPR is evaluated using software called GprMax by combining DRL with GPR modeling and simulation. Results show that our proposed DRL-based AC-GPR outperforms other GPR systems using different approaches in terms of detection accuracy and operating time.