UAV-Deployable Sensing Network for Rapid Structural Health Monitoring

Natural disasters and extreme weather events pose significant threats to the structural integrity and safety of civil and environmental infrastructure. In this context, Structural Health Monitoring (SHM) emerges as a pivotal discipline, intersecting engineering, technology, and disaster resilience. SHM\u27s mission is to provide real-time, data-driven insights into the condition of critical infrastructure, encompassing bridges, buildings, dams, and transportation networks. These systems not only expedite assessment, but also wield substantial influence in mitigating catastrophic disasters. As the frequency and intensity of extreme weather conditions escalate due to climate change, the need for robust and proactive SHM strategies becomes increasingly apparent. Moreover, the continuous monitoring of structures in dynamic environments necessitates more versatile solutions. Traditionally, SHM relied on wired systems, laden with logistical complications and steep installation costs, particularly in remote or challenging locations. Unmanned aerial vehicles (UAVs) and wireless technologies have revolutionized rapid SHM, promising groundbreaking advancements in the way structures are evaluated and secured. Deploying wireless systems for rapid SHM confronts the intricate challenge of optimizing sensor placement while maintaining a robust connection. Furthermore, signal deterioration due to transmissibility loss and the imperative of low-power signal detection in sensing systems compound these challenges. An extensive report of the aerial deployment design, development procedure, and strategies employed to enhance the onboard vibration sensor\u27s signal-to-noise ratio is provided. These enhancements are achieved through the integration of lightweight 3D printed materials, small footprint low-power electronics, and the implementation of a machine learning-based Long Short-Term Memory (LSTM) error compensator. Deliverables of this work include 1) An overview of the aerial deployment and retrieval system via electropermanent magnets integrated into uncrewed aerial vehicles. 2) A breakdown of the sensor hardware and onboard subsystems. 3) A comprehensive report of the algorithm employed to combat signal degradation due to mechanical transmissibility loss. Finally, 4) a general view of the wireless system with a focus on network communication, low-latency wireless triggering, and transmission error-handling. The focus remains on enhancing structural safety, resilience, and adaptability, ultimately safeguarding critical infrastructure for a more secure and sustainable future

Drone-Based Vibration Monitoring and Assessment of Structures

This paper presents a novel method of procuring and processing data for the assessment of civil structures via vibration monitoring. This includes the development of a custom sensor package designed to minimize the size/weight while being fully self-sufficient (i.e., not relying on external power). The developed package is delivered to the structure utilizing a customized Unmanned Aircraft System (UAS), otherwise known as a drone. The sensor package features an electropermanent magnet for securing it to the civil structure while a second magnet is used to secure the package to the drone during flight. The novel B-Spline Impulse Response Function (BIRF) technique was utilized to extract the Dynamic Signature Response (DSR) from the data collected by the sensor package. Experimental results are presented to validate this method and show the feasibility of deploying the sensor package on structures and collecting data valuable for Structural Health Monitoring (SHM) data processing. The advantages and limitations of the proposed techniques are discussed, and recommendations for further developments are made

Accelerating LSTM-based High-Rate Dynamic System Models

In this paper, we evaluate the use of a trained Long Short-Term Memory (LSTM) network as a surrogate for a Euler-Bernoulli beam model, and then we describe and characterize an FPGA-based deployment of the model for use in real-time structural health monitoring applications. The focus of our efforts is the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) dataset, which was generated as a benchmark for the study of real-time structural modeling applications. The purpose of DROPBEAR is to evaluate models that take vibration data as input and give the initial conditions of the cantilever beam on which the measurements were taken as output. DROPBEAR is meant to serve an exemplar for emerging high-rate "active structures" that can be actively controlled with feedback latencies of less than one microsecond. Although the Euler-Bernoulli beam model is a well-known solution to this modeling problem, its computational cost is prohibitive for the time scales of interest. It has been previously shown that a properly structured LSTM network can achieve comparable accuracy with less workload, but achieving sub-microsecond model latency remains a challenge. Our approach is to deploy the LSTM optimized specifically for latency on FPGA. We designed the model using both high-level synthesis (HLS) and hardware description language (HDL). The lowest latency of 1.42 $\mu$S and the highest throughput of 7.87 Gops/s were achieved on Alveo U55C platform for HDL design.Comment: Accepted at 33rd International Conference on Field-Programmable Logic and Applications (FPL

UAV Rapidly-Deployable Stage Sensor With Electro-Permanent Magnet Docking Mechanism for Flood Monitoring in Undersampled Watersheds

The availability of historical flood data is vital in recognizing weather-related trends and outlining necessary precautions for at-risk communities. Flood frequency, magnitude, endurance, and volume are traditionally recorded using established streamgages; however, the material and installation costs allow only a few streamgages in a region, which yield a narrow data selection. In particular, stage, the vertical water height in a water body, is an important parameter in determining flood trends. This work investigates a low-cost, compact, rapidly-deployable alternative to traditional stage sensors that will allow for denser sampling within a watershed and a more detailed record of flood events. The package uses a HC-SR04 ultrasonic sensor to measure stage, onboard memory for recording flood events, and an electropermanet magnet (EPM) to enable Unmanned Aerial Vehicle (UAV) deployments. Optional modules for solar panels and wireless communication can also be added to extend package longevity or allow wireless control of the EPM. The stage sensor package was found to have a range of 0.02 to 4 m with a 6.9 mm accuracy and capable of a 6.4 day long deployment. With the total cost of production at 271.37 USD, it is a cheaper and more flexible alternative to traditional stage sensors that will enable dense sensor networks and rapid response to flooding events

Deterministic and low-latency time-series forecasting of nonstationary signals

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.This proceeding is published as Chowdhury, Puja, Vahid Barzegar, Joud Satme, Austin RJ Downey, Simon Laflamme, Jason D. Bakos, and Chao Hu. "Deterministic and low-latency time-series forecasting of nonstationary signals." In Active and Passive Smart Structures and Integrated Systems XVI, vol. 12043, pp. 466-472. SPIE, 2022. DOI: 10.1117/12.2629025. Copyright 2022 SPIE. Posted with permission