Search for: All records

Abstract This study presents an approach for structural health monitoring (SHM) of remote and hazardous structures using unpiloted aerial vehicles (UAVs). The method focuses on overcoming the challenges associated with traditional sensor deployment techniques, which are often costly and risky due to the decaying nature of the targeted structures. Utilizing a multi-rotor UAV platform, a streaming camera is integrated into a recovery cone to aid in visual alignment during deployment and retrieval providing a safe and cost-effective means of sensor delivery. The paper covers the design of a video-broadcasting deployment system with integrated electropermanent magnets (EPMs), housed in a 3D-printed recovery cone, supplemented by redundancy measures to enhance safety and reliability. This proposed system significantly improves the user’s spatial awareness and aids in precise sensor package alignment, facilitated by multiple camera views providing a dual purpose of conducting visual inspection in addition to aiding in sensor delivery. The experimental analysis presented in this study validates the system’s effectiveness, demonstrating the utility of camera-aided sensor delivery for rapid SHM applications. Navigation challenges due to proximity to metal structures and the difficulties associated with signal strength and reflections are also reported. The contribution of this work is a methodology for aerial sensor deployment and retrieval using a lightweight 3D-printed recovery cone with integrated cameras for navigation and sensor alignment. 

Abstract Levees play a critical role in safeguarding communities and assets from flooding, acting as essential defenses against the devastating impacts of inundation. Yet, earthen levees are prone to breaches, especially in the face of swift floodwaters. Distributed low-cost sensor networks offer the potential to generate spatial maps illustrating soil moisture levels. Long-term monitoring of these spatial maps could identify vulnerable zones in the levee while providing an understanding of how climate change affects levee stability. This study presents an investigation into spatial monitoring of soil saturation in levees using a wireless network of UAV-deployable sensing spike packages. The goal of this paper is to demonstrate the use of these sensors for assessing soil conductivity levels in sand-filled embankments. The obtained soil conductivity levels are crucial for determining soil saturation. The developed sensing spikes consist of a spike that penetrates the ground and measures conductivity between two electrically conductive contacts. The sensing spike consists of microprocessors for edge computing, and wireless data communication systems that report data to a way station in real-time. To validate the efficacy of the developed sensors, a flume test is developed as a replica of a levee and monitored under controlled water flow conditions. The analysis of data at different times revealed the progression of moisture throughout the earthen embankment. Initially, the soil is almost dry. As the controlled water flow proceeds, the soil becomes partially saturated, with the final stage showing a dominant presence of saturated soil. The collected data sampled at the measurement points is expanded to a continuous moisture profile using kriging. Gaussian kriging, also known as ordinary kriging, is one of the commonly used variants of the kriging method. In Gaussian kriging, the estimation of values at unsampled locations is based on a linear combination of nearby data points, with weights determined by their spatial relationships. The Gaussian assumption implies that the errors in the estimation process follow a normal distribution. The extended knowledge about saturation levels obtained through kriging can lead to insights for predicting vulnerable areas and preempting potential failures. Overall, this study paves the way for further development of a wireless network of sensing spike packages as a UAV-deployable system for levee health assessment and improved infrastructure management.