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Levees and embankments are important infrastructure for protecting industrial, commercial, residential, and agricultural regions from flooding. As global temperatures increase due to climate change, flooding is expected to become more severe in the future. As flooding occurs more often with higher levels and streamflow, levee structures face a greater risk from events exceeding prior design expectations. Existing monitoring technologies either focus on discrete or continuous measurements with complicated installation, which is a challenge in large-scale implementation. Therefore, it is of interest to develop a levee monitoring system to effectively implement on a large scale for assessing and maintaining the levee condition under extreme events. This presentation includes preliminary research on a novel embeddable passive radio frequency (RF) sensing system and its applicability in levee monitoring applications. Previous studies have demonstrated the sensing system’s ability to detect changes in displacement and water content. This study focuses on implementation challenges, particularly the use of a distant exciter to power passive RF sensors. The exciter is expected to be carried by unmanned aerial vehicles (UAV), positioned a few meters from the levee surface. Controlled laboratory experiments are conducted to analyze the exciter’s effects. The main challenge of this research includes replicating the potential trajectory of the exciter and identifying the influence of the exciter’s position on the system performance. The results demonstrate the impact of the exciter’s position on the collected data. These findings demonstrate the feasibility of the RF sensing system for levee monitoring while addressing the challenges associated with UAV-mounted exciters. By further investigating and addressing these challenges, the system can be made a step closer to real-world implementations. 

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring. 

Large scale networks of intelligent sensors that can function without any batteries will have enormous implications in applications that range from smart spaces to structural and environmental monitoring. RF tags present an amenable platform for sensor integration as the backscatter communication offers low energy cost of communication. Current RF tags either use extremely low-power sensors or perform tasks of tag localization and identification based on the strength of the backscatter signal. We present a technique for estimation of amplitude and phase of the tag-to-tag channel that can be performed with very limited computational and energy resources. This enables monitoring of the interactions between tagged objects and activities around tags, as well as assessment of a variety of engineering structures. Experimental results demonstrate high resolution in the amplitude and phase channel measurement at a distances ranging from 22 cm to 1.34 m. 

Embankments, levees, and dikes are earthen protection structures built to protect industrial, commercial, residential, and agricultural regions from flooding. The inadequate maintenance of such structures can lead to failures, resulting in significant property damage, loss of farmland, and loss of life. Thus, effective monitoring of earthen structures is essential for risk management. This paper presents the envisioned design of a Smart Earthen Embankment system, battery-less sensors, and associated networking technologies, as well as data analytics and machine learning methods for the predictive modeling of structural health based on the collected data. In particular, the aim of this paper is to describe the vision of the system design, substantiate its effectiveness, and investigate the physical foundations of its feasibility through experimental study. The paper establishes the conceptual feasibility of a system that is capable of supporting spatially continuous, autonomous, and multiparameter Structural Health Monitoring (SHM) of earthen flood barriers. Deployment of the envisioned system will aid in addressing some of the increased dangers caused by climate change.