Andrei V. Gribok

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Structural health monitoring of concrete structures aims to understand the current health condition of a structure based on heterogeneous measurements to produce high-confidence actionable information regarding structural integrity that supports operational and maintenance decisions. This report describes alkali-silica reaction (ASR) degradation mechanisms and factors influencing the ASR. A fully coupled thermo-hydro-mechanical-chemical model developed by Saouma and Perotti by taking into consideration the effects of stress on the reaction kinetics and anisotropic volumetric expansion is presented in this report. This model is implemented in the GRIZZLY code based on the Multiphysics Object Oriented Simulation Environment. The implemented model in the GRIZZLY code is randomly used to initiate ASR in a 2D and 3D lattice to study the percolation aspects of concrete. The percolation aspects help determine the transport properties of the material and therefore the durability and service life of concrete. This report summarizes the effort to develop small-size concrete samples with embedded glass to mimic ASR. The concrete samples were treated in water and sodium hydroxide solution at elevated temperature to study how ingress ofmore » sodium ions and hydroxide ions at elevated temperature impacts concrete samples embedded with glass. Thermal camera was used to monitor the changes in the concrete sample and results are summarized.« less

This study compares and contrasts the ability of three different mathematical modeling techniques to predict individual-specific body core temperature variations during physical activity. The techniques include a first-principles, physiology-based (SCENARIO) model, a purely data-driven model, and a hybrid model that combines first-principles and data-driven components to provide an early, short-term (20-30 min ahead) warning of an impending heat injury. Their performance is investigated using two distinct datasets, a field study and a laboratory study. The results indicate that, for up to a 30 min prediction horizon, the purely data-driven model is the most accurate technique, followed by the hybrid. For this prediction horizon, the first-principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques. Another important finding is that, if properly regularized and developed with representative data, data-driven and hybrid models can be made ldquoportablerdquo from individual to individual and across studies, thus significantly reducing the need for collecting developmental data and constructing and tuning individual-specific models.

In this study, we analyzed a dataset of time-series vital-signs data collected by standard Propaq travel monitor during helicopter transport of 898 civilian trauma casualties from the scene of injury to a receiving trauma center. The goals of the analysis are two fold. First, to determine which combination of the automatically-collected and -qualified vital signs provides the best discrimination between casualties with and without major hemorrhage. Second, to determine whether nonlinear classifiers provide improved discrimination over simpler, linear classifiers. Major hemorrhage is defined by the presence of injuries consistent with hemorrhage in casualties who received one or more units of blood. We randomly selected a subset of the casualties to train and test the classifiers with multiple combinations of the vital-signs variables, and used the area under the receiver operating characteristic curve (ROC AUC) as a decision metric. Based on the results of 100 simulations, we observe that: (i) the best two features obtained are systolic blood pressure and heart rate (mean AUC = 0.75 from a linear classifier), and (ii) the use of nonlinear classifiers does not improve discrimination. These results support earlier findings that the interaction of systolic blood pressure and heart rate is useful for the identification of trauma hemorrhage and that linear classifiers are adequate for many real-world applications.

This paper describes the use of a data-driven autoregressive integrated moving average model to predict body core temperature in humans during physical activity. We also propose a bootstrap technique to provide a measure of reliability of such predictions in the form of prediction intervals. We investigate the model's predictive capabilities and associated reliability using two distinct datasets, both obtained in the field under different environmental conditions. One dataset is used to develop the model, and the other one, containing an example of heat illness, is used to test the model. We demonstrate that accurate and reliable predictions of an extreme core temperature value of 39.5 degC, can be made 20 minutes ahead of time, even when the predictive model is developed on a different individual having core temperatures within healthy physiological limits. This result suggests that data-driven models can be made portable across different core temperature levels and across different individuals. Also, we show that the bootstrap prediction intervals cover the actual core temperature, and that they exhibit intuitively expected behavior as a function of the prediction horizon and the core temperature variability.

This paper deals with the prediction of body core temperature during physical activity in different environmental conditions using first-principles models and data-driven models. We argue that prediction of physiological variables through other correlated physiological variables using data-driven techniques is an ill-posed problem. To make predictions produced by data-driven models accurate and stable they need to be regularized. We demonstrate on data collected during laboratory study that data-driven models, if regularized properly, can outperform first-principles models in terms of accuracy of core temperature predictions. We also show that data-driven models can be made "portable" from one subject to another, thus, making them a valuable, practical tool when data from only one subject is available to "train" the model

This paper deals with the prediction of body core temperature during physical activity in different environmental conditions using first-principles models and data-driven models. We argue that prediction of physiological variables through other correlated physiological variables using data-driven techniques is an ill-posed problem. To make predictions produced by data-driven models accurate and stable they need to be regularized. We demonstrate on data collected during laboratory study that data-driven models, if regularized properly, can outperform first-principles models in terms of accuracy of core temperature predictions. We also show that data-driven models can be made "portable" from one subject to another, thus, making them a valuable, practical tool when data from only one subject is available to "train" the model

The current threats to U.S. security both military and civilian have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the Imaging, Robotics, and Intelligent Systems (IRIS) Laboratory at The University of Tennessee (UT) has established a research consortium, known as SAFER (Security Automation and Future Electromotive Robotics), to develop, test, and deploy sensing and imaging systems for unmanned ground vehicles (UGV). The targeted missions for these UGV systems include -- but are not limited to --under vehicle threat assessment, stand-off check-point inspections, scout surveillance, intruder detection, obstacle-breach situations, and render-safe scenarios. This paper presents a general overview of the SAFER project. Beyond this general overview, we further focus on a specific problem where we collect 3D range scans of under vehicle carriages. These scans require appropriate segmentation and representation algorithms to facilitate the vehicle inspection process. We discuss the theory for these algorithms and present results from applying them to actual vehicle scans.