Doctor of Nursing Practice, Mount St. Joseph University , 2021, Department of Nursing

Remote home blood pressure (BP) monitoring has the potential to improve patient engagement and adherence with the prescribed treatment plan for managing hypertension. This DNP project examined the effects of daily remote BP measurement using transmission of biometric data through a Bluetooth-equipped device paired to participants' smartphones. Twelve adults, with an age range of 37 to 69 years, completed four weeks of daily BP measurements and communicated via text, telephone call, or video visit with care team members to discuss the plan of care and address any concerns. A Wilcoxon signed-ranks test was performed to determine the magnitude of difference between the week 1 and week 4 systolic and diastolic BP measurements. The results revealed Week 4 systolic BPs (M = 127, SD = 12.48) were significantly lower than the Week 1 systolic BPs (M = 136, SD = 12.48), W = -2, p = .004 and the Week 4 diastolic BPs (M = 82, SD = 10.97) were significantly lower than the Week 1 diastolic BPs (M = 89, SD = 9.92), W = -4, p = .006. This average systolic decrease of 9 mmHg and average diastolic decrease of 7 mmHg indicated success in lowering BP within a four-week timeframe. The clinical management of a chronic condition such as hypertension is a long-term process, but the findings of this DNP project supported the empirical evidence showing that remote BP monitoring improves patient outcomes.

Master of Science (MS), Ohio University, 2013, Civil Engineering (Engineering and Technology)

The Nelsonville US 33 Bypass is a four-lane freeway with bridges and highway sections constructed over deep soil-fill embankments. According to the Ohio Department of Transportation (ODOT) site inspection, a portion of this bypass is located over abandoned coal mine galleries, which creates potential hazards to safety of the road. These risks can appear as subsidence, excessive settlement, and slope slippage. In order to mitigate these risks, ODOT injected hydraulic grout through boring holes to most of the underground mine voids. This thesis is focused on the implementation of an instrumentation plan for monitoring subsidence and ground movement along this highway during and after construction. For this purpose, the bypass was instrumented with geotechnical sensors and equipment such as multi-point and single-point borehole extensometers, inclinometers casings, piezometers, and Time Domain Reflectometry (TDR) technology. TDR was installed in a unique and innovative setup, connected to vertical rods grouted to the tip of the mine. This new and unique approach allows the detection of areas where differential movement is taking place before deformation reaches ground surface. This method generates an early warning that will enable ODOT to take action to prevent expensive repairs and restoration works by implementing corrective actions. In addition, a simple two-dimensional Finite Element Model (FEM) in plain strain condition was prepared, using ABAQUS CAE 6.11. Four sections in the embankment area were modeled to analyze the embankment impact on stresses in rock mass as to predict possible rock failure. In this FEM, the embankment was modeled as static load, and rock strata were characterized with values from literature for similar rocks and Rock Quality Designation (RQD) values available from the abandoned mines grouting stage. The monitoring system did not find significant movements. In addition, the rain water impact on field readings was also analyzed (open full item for complete abstract)

PhD, University of Cincinnati, 2018, Engineering and Applied Science: Electrical Engineering

Firefighting is a dangerous profession which has multiple hazards and risks that can result in injuries or death. The objective of this work is to create a novel device capable of monitoring and sending alerts with the comprehensive physiological status of the firefighter when a hazard is imminent or encountered. The device is a small, lightweight, flexible, and wireless Band-Aid like device that can be attached to the firefighter's skin positioned on the upper arm, preventing the device from interfering with required activities. The device monitors the firefighter's physiological health including heart rate, blood oxygen level, carboxyhemoglobin, skin temperature, and core temperature. Not only will the device monitor the firefighter's physiological health, it will also monitor the environmental temperature and humidity of the firefighter's encapsulated protective suit, and sudden event hazards including burns, impacts, falls, impact from objects, or contact with objects. The project device integrates multiple sensors together including a reflective pulse oximeter, accelerometer, environmental humidity and temperature sensor, and a contact temperature sensor. Each sensor integrated in the device shows good performance against similar stand-alone commercial devices. The pulse oximeter's heart rate and blood oxygen measurement error is 1.75% and 2.08% respectively, the skin temperature sensor's error is 2.83%, and the environmental humidity and temperature sensor's error is 4.43% for temperature and 7.44% for humidity. The device integrated each of these sensors into a small 2.25 inches by 4 inches patch, designed to be attached to the upper arm of the subject in order to monitor and wireless report back when physiological health hazards are high or when sudden events occur.

MS, University of Cincinnati, 2014, Engineering and Applied Science: Electrical Engineering

The Veterans? Glass City Skyway (VGCS) is a large cable - stayed bridge with a single pylon. The stays have stainless steel sheathing. It is in Toledo, Ohio and owned and managed by the Ohio Department of Transportation (ODOT). The stainless steel sheath was chosen as it provides aesthetic and life cycle cost advantages over other materials. During certain wintry weather conditions, ice accumulates on the Veterans? Glass City Skyway stays, up to thickness of 19mm (¾ inch) - 75mm. When the stays warm up, ice sheds and may fall up to two hundred and fifty feet on to the roadway. The ice pieces can be whisked across many traffic lanes on the bridge deck. This causes difficulty for the traveling public and may cause potential traffic accidents. ODOT closed the lanes due to ice accumulation on the bridge four times in the seven years the VGCS has been open. There are no existing ice anti/deicing technologies that are realistic for solving the problem. Therefore, an intelligent automated monitoring system (referred to as the dashboard) was developed. The first generation of the dashboard, developed in phase I of the project, used regional weather information to assist ODOT in managing icing events. This supplemented the visual and manual approach previously used. The VGCS has local weather conditions that influence icing and the original dashboard algorithm had deficiencies. To further assist ODOT in monitoring icing events, thesis presents a revision to the dashboard that addresses the microclimate on the bridge and improves the monitoring algorithm. To address the microclimate on the VGCS a local weather station was built on the bridge. This weather station includes stay mounted thermistors, a dielectric wetness sensor, a solar radiation sensor, ice an detector and a tipping bucket rain gage. This thesis describes the function of the new sensors, rigorous laboratory experiments conducted to validate their performance, the sensor calibration, installation on the bridg (open full item for complete abstract)

Master of Science (MS), Ohio University, 2004, Environmental Studies (Arts and Sciences)

In Belize, the use of remotely sensed information for monitoring landscape dynamics is a relatively new area. This study takes advantage of contemporary technologies, such as remote sensing, for monitoring land use and land cover changes in Belize. The study area covers approximately 6,190 square miles. Two Landsat images of 1993 and 2003 were used to identify, quantify, assess and map changes in land use and land cover. The Landsat images were classified using an unsupervised K-means algorithm. Comparison of ground truth points and the 2003 classification result shows a classification accuracy of 92%. The digital change detection methodology involved a pixel-by-pixel comparison of the classified images using ENVI software. The results show that urban expansion (12%/year) is occurring at a faster rate than population growth (3.5%/year). In addition, agricultural land expansion is occurring at a rate of 32 square miles per annum. Urban development, agricultural land expansion and extensive pine forest cover loss are contributing to an estimated deforestation rate of 35 square miles per annum. In general, this study provides urgent and needed information that will guide the Government of Belize to achieve the desired goals of sustainable development.

DNP, Otterbein University, 2025, Nursing

Chemical paralysis with neuromuscular blocking agents is common practice in anesthesia. Adequate reversal of these medications is essential for postoperative recovery and return to physiologic baseline. Inadequate reversal may lead to residual paralysis and respiratory complications such as hypoxemia, upper airway obstruction, atelectasis, and pneumonia. Quantitative neuromuscular monitoring was introduced as an objective measure of adequate recovery from neuromuscular blockade. The American Society of Anesthesiologists recommends utilizing quantitative neuromuscular monitoring before tracheal extubation in their 2023 practice guidelines, as the research involving quantitative neuromuscular monitoring shows a reduction in postoperative residual paralysis rates and adverse respiratory complications postoperatively. In this study, the project team will collect data on the target facility's baseline postoperative residual paralysis rates. During a trial implementation phase, the anesthesia staff will implement the proposed guidelines, and additional data will be collected on postoperative residual paralysis rates following guideline implementation. The data will be compared, and the project team anticipates a statistically significant reduction in the overall incidence of postoperative residual paralysis using quantitative neuromuscular monitoring. If the desired outcomes are unmet, the team will investigate potential causes and adjust the guidelines accordingly.

Doctor of Philosophy (PhD), Wright State University, 2023, Engineering PhD

Blood flow dynamics plays a critical role in maintaining tissue health, as it delivers nutrients and oxygen while removing waste products. It is especially important when there is a disruption in cerebral autoregulation due to trauma, which can induce ischemia or hyperemia and can lead to secondary brain injury. Thus, there is a need for noninvasive techniques that can allow continuous monitoring of blood flow during intervention. Optical techniques have become increasingly practical for measuring blood flow due to their non-invasive, continuous, and relatively lower-cost nature. This research focused on developing a low-cost, scalable optical technique for measuring blood flow by implementing speckle contrast optical spectroscopy using a fiber-camera-based approach. This technique is particularly well-suited for measuring blood flow in deep tissues, such as the brain, which is challenging to access using traditional optical methods. A two-channel continuous wave speckle contrast optical spectroscopy device was developed, and the device was rigorously tested using phantoms. Then, it is applied to monitor blood flow changes in the brain following traumatic brain injury (TBI) in mice. The results indicate that trauma-induced significant blood flow decreases consistent with the recent literature. Overall, this approach provides noninvasive continuous measurements of blood flow in preclinical models such as traumatic brain injury.

Master of Science, The Ohio State University, 2021, Materials Science and Engineering

Monitoring oxygen and glucose levels in vivo is crucial to ensuring human health, especially amidst the ongoing COVID-19 pandemic. Using blends of biodegradable polycaprolactone (PCL) and gelatin – along with an oxygen-sensitive porphyrin, Pd-MABP – implantable, biodegradable, electrospun oxygen sensors were fabricated. These sensors were first injected into horse hide and then into live horses to determine the effectiveness of oxygen monitoring system in mammalian tissue. Two different sensor experiments took place, both indicating oxygen contents behavior that did not correlate to simultaneous blood gas measurements. In vitro testing in 37°C PBS was also conducted and the data displayed linear Stern-Volmer behavior over the period of one month and that could be correlated to the induced oxygen concentrations. These electrospun oxygen sensors were also used as a basis for glucose sensors. By adding glucose oxidase (GOx) to the polymeric fibers, it was reasonable to expect that the sensors should be able to detect changes in glucose levels by the corresponding localized consumption of oxygen. GOx was incorporated through various methods including incorporation into the sensing fibers and as a post-spinning coating. These techniques showed varying degrees of success suggesting that a previously unknown interaction between the Pd-MABP and the gelatin was occurring. This mechanism was explored in some detail along with the impact of storage solution conditions.

MS, University of Cincinnati, 2020, Engineering and Applied Science: Aerospace Engineering

More than 350 years ago the first public transportation service was first operated by Blaise Pascal in 1662. Since then mass transportation has expanded to water route, rail route, and road route, in fact, public transportation has now leaping towards space travel. Over the years the transportation system has become faster, affordable, efficient, and more advanced than anything in terms of fuel efficiency, speed, and environmental impact. Although, one key aspect of transportation that has always been the concern since the beginning is safety. Safety has always been a critical parameter whenever the design of a system is developed, as history reveals due to some of the other ways the safety of humans is compromised due to some mechanical failure or components or human factors. Hence to create reliable systems of transportation, just like any other system health monitoring systems are used to track the system's critical components. This thesis aims to develop a similar health monitoring system for rotating propellers or elements. The objective is to create a wireless health monitoring system using the vibration signature generated by the system which can be used by anyone who is operating the machinery to track the working conditions during live operation knowing very little about the machine. My work involves research across previously performed work as well as principles that can give out the best results based on the application and affordability. The well renowned and basic mathematical concept of Fast Fourier Transform has been very useful in completing my research because it is very simple to understand but the level of sophistication it can provide depending on the system to be developed makes it very suitable for the task. This thesis will utilize concepts of the Internet of Things using electronic boards, LabView programming language for real-time data processing, and visualization further validated using MATLAB.

Doctor of Philosophy, The Ohio State University, 2020, Civil Engineering

Vibration-based structural health monitoring (SHM) has become increasingly popular in recent years as a general and global method to detect possible damage scenarios. With the increase in the number of infrastructures that are in service beyond their initial design service age, more and more owners are relying on SHM to evaluate the integrity of their structures. As a result, SHM approaches that are applicable to a variety of structures with minimal service interruption and lower cost are of high importance. There are many research on SHM processes using a network of sensors placed on over a target structure. Although these approaches may result in more accurate results due to redundancy of the system, they are mostly cost prohibitive for currently in-service structures and are suitable for newly constructed projects with embedded sensors. This dissertation presents a feature-based SHM process using a new signal processing and feature extraction methodology and presents its application on a real-life vibration monitoring project completed of an energized substation structure. The new signal processing and feature extraction methodology uses specific filtering and optimization schemes which improved the performance in extracting features specifically when using a contaminated response signal. Next, the extracted features are used in a structural model updating to identify and localize the damage through an optimization process. Finally, a vortex-induced vibration analysis process is presented and applied to the real-life monitored structure. Currently there are no power utility industry standard methodology for the analysis and design of structures against wind-induced vibrations. The current codes or standards of practice recommend using damping devices such as chain dampers or strakes to mitigate the vibrations, when they are observed. This approach may not be feasible due to the energized in-service structures. In addition, modifications to the installed structure (open full item for complete abstract)

Master of Science (MS), Ohio University, 2020, Biological Sciences (Arts and Sciences)

There are inherent challenges to monitoring bat populations in the Rocky Mountain West of North America, due in part to the apparent scarcity of large hibernacula that facilitate population assessments via abundance counts, and to the expansive, difficult or impassable terrain that can thwart data-collection efforts. The use of bioacoustic monitoring equipment and techniques provides a viable option to overcome these challenges and to provide much needed and otherwise sparse population ecology data. The studies within the following chapters employ various acoustic monitoring strategies with the goals of providing baseline data on bat populations in Yellowstone National Park, comparing two popular acoustic survey frameworks, and tracking changes in bat activity patterns following habitat disturbance caused by wildfire. In an effort to provide baseline data on bat populations in Yellowstone, I deployed 32 ultrasonic acoustic bat detectors for single-season occupancy analysis coupled with per-night activity level assessments over the summer of 2018. This project design focused on identifying habitat characteristics that explain current distributions in occupancy and loci of activity. Habitats were selected for consideration based on their documented use by bats for foraging, roosting, and as water sources, and were represented on two spatial scales. This analysis produced results for habitat associations that were diverse and species-specific, emphasizing the disparate strategies and habitat requirements represented within the Yellowstone bat population at large. To compare the results of mobile acoustic transect and point station survey frameworks, I performed 12 acoustic transects in the summer of 2018. The resultant data was compared to a subset of sites deployed for the occupancy and analysis study selected for their proximity to transect routes. I found that point stations recorded significantly more bats and documented greater species richness than mobile (open full item for complete abstract)

Master of Science, Miami University, 2020, Mechanical and Manufacturing Engineering

Vibration based monitoring techniques are widely used to detect damage, monitor the growth of inherent defects, system identification, and material parameter estimation for various engineering applications. These techniques present a non-invasive and relatively inexpensive tool for various biomedical applications, for example, in characterizing the mechanical properties of the bone and muscles of humans as well as animals. In recent years, it has been shown that fundamental natural frequencies and corresponding damping ratios can be correlated to the bone health quality indicators as associated with osteoporosis, osteoarthritis etc. In this research, through the investigation of clinical data, an analysis procedure is developed to investigate the correlation between the damping properties associated with both lower and higher modes of vibration and bone health quality. Subsequently, a data-driven system identification tool for reconstructing the parameters (mass, stiffness, damping distributions) in a low-dimensional human model is developed which utilizes selected measurements from the clinical study. It is anticipated that the analysis process and parameter identification techniques presented here can be developed and tuned for any individual human model and can be can be used as osteological monitoring tool for predicting early diagnostics pre-cursors of the bone or muscle related conditions or diseases.

Master of Science in Biomedical Engineering (MSBME), Wright State University, 2019, Biomedical Engineering

Neurovascular coupling is an important concept that indicates the direct link between neuronal electrical firing with the vascular hemodynamic changes. Functional Near Infrared Spectroscopy (fNIRS) can measure changes in cerebral vascular parameters of oxy-hemoglobin and deoxyhemoglobin concentrations and thus can provide neuronal activity through neurovascular coupling. Currently many commercial fNIRS devices are available, but they are limited by the number of channels (usually having only 8 detectors), which can limit the sensitivity, contrast, and resolution of imaging. High-density imaging can improve sensitivity, contrast, and resolution by providing many measurements and averaging the signals originating from the target cerebral focus area compared to background tissue. Here a multi-channel, low-cost, high-density imaging system based on scientific CMOS (Complementary Metal-Oxide-Semiconductor) detector will be presented. The CMOS camera is fiber-coupled such that on one end fibers are focused on the pixels on the CMOS camera, which allows individual pixels (or binned sub-pixels) to act as detectors, while the other end of the fibers can be positioned on a wearable optical probe. After the device details, I will show the device validation using a series of the dynamic flow phantom experiments mimicking the brain activation and finally human motor cortex experiments (finger tapping experiments). The results demonstrate that this system can obtain high-density data sets with higher contrast and resolution. This wearable, high-density optical neuroimaging technology is expected to find many applications including pediatric neuroimaging at clinics and assessing human cognitive performance.

Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Electrical Engineering

Hand-arm vibration syndrome has been an occupational hazard for a long time. However, a detail understanding of its vibration exposure inset is not yet available, largely because instrumentation for its in situ monitoring has been difficult to implement. With the development of wearable electronics, new solutions for its monitoring and study have become possible. This work studies the usefulness of monitoring vibration magnitude, EMG (electromyography) signal, PPG (photoplethysmogram) signal and finger skin temperature in quantifying continuous hand-arm vibration exposure. A commercial orbital sander was used. The results show that vibration magnitude and median frequency of EMG signal do not lead to better measurements than finger skin temperature and PPG signal for such monitoring. The finger skin temperature decreases 8.8% on average during 25 minutes of vibration exposure and increases 6.2% on average during 15 minutes of rest. The pulsatile component of PPG signal decreases 4.8% and increases 35% on average during vibration and rest, respectively. The non-pulsatile component of PPG signal decreases 28% and increases 5% on average during vibration and rest, respectively. Finger skin temperature and PPG signal can be effective indicators in HAV monitoring but have to be treated carefully.

Master of Science, University of Toledo, 2014, Civil Engineering

The 82 year old Anthony Wayne Bridge (AWB) in Toledo, Ohio is undergoing an extensive rehabilitation in two phases starting in construction season 2014. The plan is to first replace the approaches and rehabilitate the superstructure. Upon completion of the superstructure rehabilitation, steps to preserve the main suspension cables will be taken. Prior to taking action to preserve the cables, however, it is necessary to evaluate the condition of the cables. Therefore, as part of cable condition evaluation, an acoustic monitoring system was installed on July 2011 and has been continuously monitoring the main cables since then. Acoustic emission (AE) is a non-destructive technique which is practical for monitoring elements of bridges where invasive inspection is either difficult or costly. The AE system can be accessed remotely in real time and it does not cause any interruption to traffic. In the case of a suspension bridge, main cables are of primary concern as their condition cannot be assessed externally unlike other bridge components and they are fracture critical. This paper presents a case study on the application of the acoustic emission technique to the main cables of the AWB. Several laboratory experiments were planned and executed to develop understanding of the potential AE sources. Wire breaks were the primary AE sources under concern. Rain and frictional activities induced by traffic and wind events would create secondary and/or noise sources. The rain, friction and wire break were all simulated in the laboratory and it was verified that, by using a combination of parameters along with signal signatures, a wire break signal can be discriminated against other secondary or noise sources. The AE monitoring system on the AWB uses a series of 7 algorithms that analyze the parameters of each detected AE event. For each feature that meets or exceeds the value of the classification, it is assigned a source type ranging from 0 to 7. Thus for a wire break, the (open full item for complete abstract)

MS, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science

The Wireless Body Area Sensor Network (WBASN) is a wireless network of wearable computing devices including few medical body sensors which capture and transmit different physiological data wirelessly to a monitoring base station like laptop so as to provide the real time health information of a person in a non-invasive way where the person, instead of implanting the miniaturized body sensor units inside the body, puts the sensors on the body surface. E-Healthcare is a popular healthcare application of WBASN, used today. Applications such as monitoring of patients with movement disorders, and specially the elderly for early fall detection, monitoring the injury of the athletes etc. are very recent and popular applications of WBASN. This research focuses on such important applications of continuous, non-invasive, wireless monitoring of both the patients suffering from Parkinson's disease (PD) to prevent falls and potential injuries and the sports-athletes to assess their injuries that occurs during the game. During the work of monitoring PD patients, we propose a non-invasive, wireless technique that could detect multiple occurrences of Freezing of Gait (FoG ) over single or multiple days of observation. Here we focus on building a home based monitoring system which involves embedding of wireless sensors in the patient's vicinity, such as in his room. Analyzing the values of Received Signal Strength Indicator (RSSI), which is the measure of received signal strength, from these sensor nodes we implement a robust patient's activity monitoring system for early fall detection. We also propose the use of force detection sensors to detect and record the multiple occurrences of FoG episodes, which is not possible to record at the doctor's clinic due to its relation with the degree of consciousness when a person is at hospital. We also plan to derive different mobility patterns for a PD patient based on the RSSI values during a day's activity and then train the system for (open full item for complete abstract)

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Mechanical Engineering

Monitoring the health condition of machinery has been an area of research for quite some time. Despites several advancements, the application of conventional signal analysis and pattern recognition methods face several challenges when the operating variables such as load, speed, and temperature vary considerably for the monitored asset. The residual clustering approach addresses the multi-regime monitoring challenge by first modeling the baseline non-linear correlation relationship in the measured signal features and by providing predicted signal features. Calculating the residual signal features allows one to normalize the effect of the operating variables, since one is considering how the response of the system compares with the predicted response based on the baseline behavior. In many instances the degradation signature of a component or system is more pronounced under certain operating conditions. The clustering portion of the residual clustering method specifically addresses the regime dependent signature aspect and bases the health value on the monitoring regime in which the degradation signature is more prevalent. This dissertation work highlights the mathematical framework and provides guidance on the appropriate processing methods for each portion of the approach. From simulation studies and wind speed data, the results highlight that the auto-associative neural network method provides the lowest prediction error when compared with regression, neural network, and principal component analysis methods. The results from this dissertation work also imply that the selection of the clustering algorithm does not significantly affect the calculated health value, and in general, most clustering algorithms appear suitable for detecting the problem using the residual clustering approach. The feasibility of the residual clustering approach is demonstrated in three case studies. For the wind speed sensor health monitoring case study, the residual clusterin (open full item for complete abstract)

Master of Science in Engineering (MSEgr), Wright State University, 2008, Electrical Engineering

Effective vehicular power management requires accurate knowledge of battery state, including state-of-charge (SOC) and state-of-health (SOH). An essential functionality of automotive batteries is delivering high power in short periods to crank the engine. A well-known approach to battery SOH monitoring is to infer battery state-of-health from battery impedance or resistance, which is not robust to variation of battery types. The research and development of more reliable battery state-of-health monitoring methods to ensure vehicle start-up ability are presented in this thesis. The methods include a battery cranking voltage based method, a parity-relation based method using battery voltage and cranking current signals, and a support vector machine based pattern recognition method utilizing battery voltage and engine cranking speed. The performances of these methods have been evaluated and compared through analysis of extensive real vehicle cranking data from 2 vehicles and 20 batteries. Cost benefit analysis is also conducted with different sensor options.

MS, University of Cincinnati, 2001, Engineering : Electrical Engineering

Instrumented bridge health monitoring has shown its significance in recent years and is causing more and more attention [1]. An instrumented bridge health monitoring surpasses a conventional visual bridge inspection in many aspects. While unsafe conclusions may be reached by visually proof-testing a bridge without an adequate instrumentation based test, instrumented monitoring may provide an objective measurement and lead to a clear understanding of bridge status. The collected long term and high-speed data, instrumented monitoring system may help civil engineers improve bridge design, construction, and maintenance mechanism and practice. Therefore, research on how to build a reliable, powerful, and convenient bridge health monitoring system is of great significance to improve bridge performance. The University of Cincinnati Infrastructure Institute (UCII) is making active work in the area of instrumented bridge health monitoring. In a project co-funded by the Ohio Department of Transportation (ODOT) and the Federal Highway Administration (FHWA), an instrumented monitoring system has been installed by UCII on a typical three-span steel stringer bridge located at the Cross-County Highway over Hamilton Avenue (HAM-126-0881L) in Cincinnati. The monitoring system is collecting a large amount of valuable data to civil engineers to help them establish a deeper understanding of bridge performance. An integrated Graphic User Interface (GUI) has also been developed with the LabVIEW G programming tool in order to provide a user-friendly interface for easy access, retrieval and processing of all the long term and high-speed bridge data. Also ADSL lines have been implemented for remote accessibility of the bridge monitoring system and transfer of bridge data to the lab server PC. The present thesis is based on future recommendations on Intelligent Health Monitoring mentioned in Dr Victor Hunt's doctoral thesis. It focuses on building a continuous and automatic traffic monitor f (open full item for complete abstract)

PhD, University of Cincinnati, 2006, Engineering : Mechanical Engineering

A method for structural health monitoring of large structures based on detecting acoustic emissions produced by damage was developed for this dissertation. The advantage of sensing acoustic emissions is that small damage can be detected in structures built with complex geometry and anisotropic materials. A longstanding limitation of the acoustic emission method is that a large number of bulky sensors are required to monitor cracks that can form at any location on a complex structure. The sensors and data acquisition system are also required to work at a high sampling rate because the frequencies of acoustic waves propagating in the structure due to damage are on the order of hundreds of kHz. To overcome the difficulties with using the acoustic emission method, a very elegant and powerful technique that many researchers have either missed or avoided is presented in this dissertation. The new sensing technique is called a structural neural system. The technique was difficult to develop, and required using electronic circuits to mimic the architecture of the biological neural system. In developing the technique, it was also necessary to recognize the strong linkage between fracture mechanics and fatigue damage detection. The structural neural system developed uses continuous (multi-node) sensors to mimic dendrites, receptors, and the axon which perform sensing in the biological neural system. Analog electronics were then developed to mimic the thresholding and firing functions of the soma (cell body) in the neural system. The end result is a structural neural system that tremendously reduces the complexity and number of data acquisition channels needed to monitor acoustic emissions and detect damage in structures that have high feature density. Simulation and laboratory testing of a prototype of the structural neural system showed that the structural neural system is sensitive to small damage and practical to use on large structures. A field test was also performed in (open full item for complete abstract)