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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. 

Abstract Levees are built to safeguard human lives, essential infrastructure, and farmland. However, failure of levees can have catastrophic impacts due to a fast rate of inundation in areas protected by levees. Earthen levees are prone to failure due to excessive moisture content that reduces the shear strength of the soil. The use of levee monitoring systems has demonstrated the ability to reduce the likelihood of failure by creating maps that depict the saturation levels of the surface of the levee, both in terms of space and time. By utilizing extensive sensor networks to continuously monitor these geo-infrastructure systems, the structural deterioration attributed to changing climate can be studied. Measuring environmental parameters surrounding such structures provides insight into the potential stressors that cause structural failure. Steps can then be taken to mitigate those effects on the levees and maintain structural integrity. However, the massive scale of levees makes it difficult to monitor with conventional wired sensors. This paper presents a preliminary investigation into the development and validation of UAV-deployable smart sensing spikes for soil conductivity levels in levees, which is a measurement modality for determining soil saturation levels. For this work, Gaussian process regression (also known as kriging) is used to model the soil saturation levels between sensing spikes obtaining a continuous moisture map of the levees. The expanded data is then categorized using a clustering-based machine learning approach with conductivity data from sensing spikes as model inputs. The machine learning model output is sorted into three categories: dry, partially saturated, and saturated soil. The findings of a laboratory study are presented, and the implications of the raw and expanded data are discussed. This work will aid in predicting potential levee failure risks and maintenance requirements based on the analysis of the soil conditions using a network of smart sensing spikes. 

Hard real-time time-series forecasting of temporal signals has applications in the field of structural health monitoring and control. Particularly for structures experiencing high-rate dynamics, examples of such structures include hypersonic vehicles and space infrastructure. This work reports on the development of a coupled softwarehardware algorithm for deterministic and low-latency online time-series forecasting of structural vibrations that is capable of learning over nonstationary events and adjusting its forecasted signal following an event. The proposed algorithm uses an ensemble of multi-layer perceptrons trained offline on experimental and simulated data relevant to the structure. A dynamic attention layer is then used to selectively scale the outputs of the individual models to obtain a unified forecasted signal over the considered prediction horizon. The scalar values of the dynamic attention layer are continuously updated by quantifying the error between the signal’s measured value and its previously predicted value. Deterministic timing of the proposed algorithm is achieved through its deployment on a field programmable gate array. The performance of the proposed algorithm is validated on experimental data taken on a test structure. Results demonstrate that a total system latency of 25.76 µs can be achieved on a Kintex-7 70T FPGA with sufficient accuracy for the considered system. 

Abstract In this paper, a method for real-time forecasting of the dynamics of structures experiencing nonstationary inputs is described. This is presented as time series predictions across different timescales. The target applications include hypersonic vehicles, space launch systems, real-time prognostics, and monitoring of high-rate and energetic systems. This work presents numerical analysis and experimental results for the real-time implementation of a Fast Fourier Transform (FFT)-based approach for time series forecasting. For this preliminary study, a testbench structure that consists of a cantilever beam subjected to nonstationary inputs is used to generate experimental data. First, the data is de-trended, then the time series data is transferred into the frequency domain, and measures for frequency, amplitude, and phase are obtained. Thereafter, select frequency components are collected, transformed back to the time domain, recombined, and then the trend in the data is restored. Finally, the recombined signals are propagated into the future to the selected prediction horizon. This preliminary time series forecasting work is done offline using pre-recorded experimental data, and the FFT-based approach is implemented in a rolling window configuration. Here learning windows of 0.1, 0.5, and 1 s are considered with different computation times simulated. Results demonstrate that the proposed FFT-based approach can maintain a constant prediction horizon at 1 s with sufficient accuracy for the considered system. The performance of the system is quantified using a variety of metrics. Computational speed and prediction accuracy as a function of training time and learning window lengths are examined in this work. The algorithm configuration with the shortest learning window (0.1 s) is shown to converge faster following the nonstationary when compared to algorithm configuration with longer learning windows.