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1. Progress Towards Data-Driven High-Rate Structural State Estimation on Edge Computing Devices

   https://doi.org/10.1115/detc2022-90118  

   Satme, Joud; Coble, Daniel; Priddy, Braden; Downey, Austin R.; Bakos, Jason D.; Comert, Gurcan (August 2022, 34th Conference on Mechanical Vibration and Sound (VIB))

   Abstract Structures operating in high-rate dynamic environments, such as hypersonic vehicles, orbital space infrastructure, and blast mitigation systems, require microsecond (μs) decision-making. Advances in real-time sensing, edge-computing, and high-bandwidth computer memory are enabling emerging technologies such as High-rate structural health monitoring (HR-SHM) to become more feasible. Due to the time restrictions such systems operate under, a target of 1 millisecond (ms) from event detection to decision-making is set at the goal to enable HR-SHM. With minimizing latency in mind, a data-driven method that relies on time-series measurements processed in real-time to infer the state of the structure is investigated in this preliminary work. A methodology for deploying LSTM-based state estimators for structures using subsampled time-series vibration data is presented. The proposed estimator is deployed to an embedded real-time device and the achieved accuracy along with system timing are discussed. The proposed approach has shown potential for high-rate state estimation as it provides sufficient accuracy for the considered structure while a time-step of 2.5 ms is achieved. The Contributions of this work are twofold: 1) a framework for deploying LSTM models in real-time for high-rate state estimation, 2) an experimental validation of LSTMs running on a real-time computing system. 

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2. Structural health monitoring using extremely compressed data through deep learning

   https://doi.org/10.1111/mice.12517  

   Azimi, Mohsen; Pekcan, Gokhan (November 2019, Computer-Aided Civil and Infrastructure Engineering)

   Abstract This study introduces a novel convolutional neural network (CNN)‐based approach for structural health monitoring (SHM) that exploits a form of measured compressed response data through transfer learning (TL)‐based techniques. The implementation of the proposed methodology allows damage identification and localization within a realistic large‐scale system. To validate the proposed method, first, a well‐known benchmark model is numerically simulated. Using acceleration response histories, as well as compressed response data in terms of discrete histograms, CNN models are trained, and the robustness of the CNN architectures is evaluated. Finally, pretrained CNNs are fine‐tuned to be adaptable for three‐parameter, extremely compressed response data, based on the response mean, standard deviation, and a scale factor. The performance of each CNN implementation is assessed using training accuracy histories as well as confusion matrices, along with other performance metrics. In addition to the numerical study, the performance of the proposed method is demonstrated using experimental vibration response data for verification and validation. The results indicate that deep TL can be implemented effectively for SHM of similar structural systems with different types of sensors. 

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3. An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge

   Locke, William R; Mokalled, Stefani C; Abuodeh, Omar R; Redmond, Laura M; McMahan, Christopher S (January 2021, The Concrete Industry in the Era of AI)

   e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

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4. Integration of structural health monitoring in shape memory alloy reinforced self-healing matrix composites using piezoelectric transducers

   https://doi.org/10.1177/14759217251345863  

   Alzoubi, Jomah; Srivastava, Vaibhav; Bellah, Masum; Salowitz, Nathan; Rohatgi, Pradeep (July 2025, Structural Health Monitoring)

   Integrating self-healing materials with structural health monitoring (SHM) represents a significant advancement in materials science and engineering. In applications such as aerospace, self-healing composites with SHM can detect and repair damage autonomously, enhancing safety and reducing maintenance time, which significantly improves structural durability by maintaining integrity and extending material service life. This research employs an experimental approach to validate the self-healing process of self-healing metal matrix composites reinforced with shape memory alloy fibers, by utilizing lead zirconate titanate (PZT) piezoelectric transducers mounted on the surface of the composite. A three-point bending test is used to induce damage in the metal matrix composites by applying a load at its midpoint while supporting it at both ends; then, self-healing is used to return the specimen to its original state. The ultrasonic signals from the PZT sensor were compared at three stages: pristine state, during bending (i.e., damage), and after the healing process, to evaluate the effectiveness of the healing process and health monitoring technique adopted for this study. Real-time monitoring using digital image correlation, laser profilometry, and mechanical testing was used to validate the damaged/healed signal state—demonstrated by improved recovered signal amplitude, reduced scatter energy, a notable decrease in damage indices root-mean-square deviation, normalized scatter energy, and stabilized wave propagation. The successful self-healing composite design and health monitoring results confirm the restoration of the specimen, which regained 84% of its initial shape deformation at 135 °C for 30 min (i.e., during the first step of healing). Complete shape restoration was achieved by raising the temperature to 150 °C for 90 min (during the second step of healing), recovering approximately 96% of the original flexural strength after healing. 

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5. Poster: Quantifying Signal Quality Using Autoencoder for Robust RF-based Respiration Monitoring

   Xie, Zongxing; Nederlander, Ava; Park, Isac; Ye, Fan (January 2023, ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies)

   While radio frequency (RF) based respiration monitoring for at- home health screening is receiving increasing attention, robustness remains an open challenge. In recent work, deep learning (DL) methods have been demonstrated effective in dealing with non- linear issues from multi-path interference to motion disturbance, thus improving the accuracy of RF-based respiration monitoring. However, such DL methods usually require large amounts of train- ing data with intensive manual labeling efforts, and frequently not openly available. We propose RF-Q for robust RF-based respiration monitoring, using self-supervised learning with an autoencoder (AE) neural network to quantify the quality of respiratory signal based on the residual between the original and reconstructed sig- nals. We demonstrate that, by simply quantifying the signal quality with AE for weighted estimation we can boost the end-to-end (e2e) respiration monitoring accuracy by an improvement ratio of 2.75 compared to a baseline. 

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