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)

Committee: Mark Schulz (Advisor) Subjects: Engineering, Mechanical

Master of Science in Engineering, Youngstown State University, 2012, Department of Civil/Environmental and Chemical Engineering

This paper describes a method for estimating remaining service life of a bridge based on real-time responses of the bridge. Real-time responses were recorded using wireless sensor network. With a significant percentage of nation's bridges being structurally deficient or functionally obsolete and with no quantitative method of health monitoring being used in general practice, it has become the necessity to develop a SHM method, which will provide a quantitative assessment of overall bridge health. This research focuses on estimating overall condition of the bridge analyzing dynamic response rather than focusing on individual damage types, their severity and locations. SHM process in this research uses dynamic responses of a bridge subjected to service loads, collects the response through a system of wireless sensor network, simulates an ideal and practical bridge using finite element model, and then estimates the remaining service life of the bridge based on the modal correlation between the existing and an ideal bridge condition. Results indicate that the bridge under this study has lost approximately 47% of its approximately 50 years of service life in 30 years of service. It was also observed that only higher order modes are more sensitive to damage compared to lower ones. With limited budget available for bridge maintenance and repair, this research can help bridge owners, policy makers, transportation planners or any related professionals or organizations in prioritizing and allocating budgets based on actual bridge condition.

Committee: AKM Anwarul Islam PhD (Advisor); Javed Alam PhD (Committee Member); Frank Li PhD (Committee Member) Subjects: Civil Engineering; Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

A major concern in the oil and gas industry is erosion corrosion which can cause catastrophic failure in pipelines. To monitor and prevent this failure, networks of acoustic emission sensors have been installed on pipelines to detect the presence of abrasive particles in the fluid flow. These abrasive particles damage the inside walls of the pipes through high-velocity impact. It would be advantageous to utilize the ultrasonic transducers in these existing monitoring systems to measure wall thickness. Two main roadblocks exist in utilizing these transducers for wall thickness measurements. First, these systems do not have a way of providing the typical excitation needed for ultrasonic measurements. To combat this issue, this thesis explores two different passive approaches: one that requires no purposeful excitation and another that utilizes acoustic emission from particle impact and fluid flow within the pipe. The second challenge in measuring wall thickness using existing transducers is the frequency range of these transducers which is much lower than what is typically used for ultrasonic time-of-flight thickness measurements. To address this problem, this thesis explores the sensitivity of transducers to the upper limits of their frequency range using a time-of-flight method. Additionally, for thinner-walled components which would require even higher frequencies, a resonant ultrasound spectroscopy method is explored. Experimental measurements using the different measurement modalities and passive excitation approaches are shown using multiple transducers. Several of the experimental combinations tested show good agreement with active measurements and show promise in determining wall thickness.

Committee: Joseph Corcoran Ph.D. (Committee Chair); Francesco Simonetti Ph.D. (Committee Member); Gui-Rong Liu Ph.D. (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2023, Engineering and Applied Science: Mechanical Engineering

Metamaterials are artificially engineered materials that exhibit properties not found in any naturally occurring materials. Recently, there has been a significant interest in locally resonant elastic metamaterials because of their remarkable ability to attenuate shock and vibration significantly due to the presence of bandgaps, defined as the frequency range in which elastic waves cannot propagate. The existing literature on metamaterials assumes that the structure remains intact and undamaged. However, all the structures in the real world are inevitably susceptible to damage caused by the factors such as manufacturing defects, material impurities, corrosion, etc. It is crucial to understand how metamaterials perform when subjected to damage for their effective utilization. The main objective of this research is to address the insufficient knowledge on monitoring and evaluating the structural health of locally resonant elastic metamaterials, specifically with regard to detecting any damage. This research aims to develop a physics-based mathematical framework to explain the fundamental structural dynamics of damaged metamaterials. A new damage detection method has been formulated to detect the extent of damage and locality in locally resonant elastic metamaterials. This method involves utilizing the driving point FRFs to determine the damage index for undamaged and damaged metastructures under consideration. The existence of damage introduces anomalies in the Frequency Response Functions (FRFs), and multiple high-magnitude peaks have been observed inside the bandgap region for the damaged metamaterials. These peaks result from the occurrence of highly localized modes near the damage location. The presented method has been extensively validated through FE simulations using ANSYS and experimental modal analysis using a laser Doppler vibrometer. The results showed excellent agreement, thus providing a reliable way to evaluate the structural health of locally resonant el (open full item for complete abstract)

Committee: Yongfeng Xu Ph.D. (Committee Chair); Allyn Phillips Ph.D. (Committee Member); Daniel Kiracofe Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Civil Engineering

There are three main research conducted in this paper, including using deep learning methods with vision-based technologies on Structural Damage Detection (SDD), Structural Health Monitoring (SHM) and progressive collapse study. During the learning and improvement process, many goals of automation in SDD and SHM have been achieved, although there will be a large room for further improvement and development on these studies. In progressive collapse study, remote sensing technologies and data fusion are applied on a field experiment of a real building at the Central Campus of the Ohio State University. The major contributions of this paper are shown as follows: A few comprehensive experimental studies for automated SDD in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual Network (ResNet) is utilized to identify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, material types, etc. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipeline, cascaded networks, for categorizing and locating structural damage. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network. In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes. One of the proposed networks can achieve an accuracy above $67.6\%$ for all tested images at various scales and resolutions, and shows its robustness for these human-free detection tasks. Studies are conducted with a pipeline to automatically track and measure displacements and vibrations of structures or structural components in laboratory and field experiments. This novel framework (open full item for complete abstract)

Committee: Halil Sezen Dr. (Advisor); Farhang Pourboghrat Dr. (Committee Member); Rongjun Qin Dr. (Committee Member); Alper Yilmaz Dr. (Advisor) Subjects: Civil Engineering; Computer Science; Mechanics

Doctor of Philosophy (Ph.D.), University of Dayton, 2021, Materials Engineering

In this study, silver/epoxy conductive nanocomposite-based sensors were developed as follows: First, abundant silver nanomaterials were synthesized using a rapid polyol reduction method. Factors that affected silver nanomaterial morphology and the mechanism of nanosilver growth in large-scale synthesis were studied in detail. Controlling the silver nanomaterial's size and uniformity and efficiently purifying the silver nanowire were the main challenges in the development of large-scale synthesis. Second, the morphology, crystallinity, and orientation of various silver nanofillers were characterized. Then, silver nanoparticle/polyacrylonitrile and silver nanowire/polyacrylonitrile-based nanocomposites were fabricated by spin coating and used to investigate the silver nanocomposite conductive network. Silver nanowire-based nanocomposite showed a lower percolation threshold. A conductive unit-based model was established and successfully explained the evolution of the conductive network and aggregation. The aggregation geometry of nanofiller appeared as a dominant factor in altering the percolation behavior. Small-sized, irregularly shaped silver nanoparticle aggregates can lower the percolation threshold by introducing anisotropy to the nanocomposite. In contrast, large-sized, irregularly shaped silver nanoparticle aggregates hinder the formation of the conductive network due to the number of aggregates decreasing. Lastly, the silver conductive nanocomposite-based structural health monitoring sensors were designed to detect the progress of chemical diffusion and material degradation as a function of time. A comparison study between the silver nanowire/epoxy sensor and silver nanoparticle/epoxy sensor was conducted to investigate the concentration and geometry of the silver nanomaterial's effect on acid penetration. It appeared that the structural health monitoring sensors' resistance decreased in three stages as the diffusion time progressed. When the volume percenta (open full item for complete abstract)

Committee: Khalid Lafdi Ph.D. (Committee Chair); Donald Klosterman Ph.D. (Committee Member); Erick Vasquez Ph.D. (Committee Member); Youssef Raffoul Ph.D. (Committee Member) Subjects: Chemical Engineering; Materials Science

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)

Committee: Abdollah Shafieezadeh (Advisor); Jieun Hur (Committee Member); Halil Sezen (Committee Member) Subjects: Engineering

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.

Committee: Kumar Singh (Advisor); James Chagdes (Committee Member); Mark Walsh (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Mechanical Engineering; Osteopathic Medicine

PhD, University of Cincinnati, 2019, Engineering and Applied Science: Civil Engineering

The current state of the art in structural health monitoring (SHM) is to compare a set of sensor values to previously established values, using some method to determine the most similar to positively identify the state. Some systems will merely establish that a change in the structure has occurred without positively identifying the system state. In order for most systems to identify a state, it must have been considered prior to the state (Helmicki et al. 2012). Those that do not require this previously determined state require intense computations following the detection of an abnormality, the solution space for damages is always large (He and Hwang, 2007). If multiple damage states are introduced, considering all possible combinations of them swiftly becomes untenable. This research examines the use of subdivided attractor artificial neural networks (SA-ANN) as a method for determining multiple damage states not considered prior to sensing. These networks receive signals from within themselves and from the other subnetworks, the subnetworks then are able to stabilize or destabilize each other depending on whether the physical states they represent are consistent with one another. This results in a new system that considers the state of a structure holistically, but is still able to discern multiple concurrent damage states. Test problems of two bridges are considered, a small pony truss bridge and a large cable stayed bridge. These bridges were divided into subsystems, and each individual damage state was modeled using SAP2000. Genetic algorithms (GA) were used to select strains to identify the individual damage states of the subsystems, and feedforward artificial neural networks (FF-ANN) were trained to identify damage to the subsystems based on these strains. These FF-ANN initialize the SA-ANN, which then converges to a state describing the physical state of the structure. Damage to multiple subsystems was also modeled; these were used to test the ability (open full item for complete abstract)

Committee: James Swanson Ph.D. (Committee Chair); Thomas Burns Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); Gian Andrea Rassati Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

Structural health monitoring (SHM) is a rapidly growing field focused on detecting damage in complex systems before catastrophic failure occurs. SHM systems provide the potential to improve safety and significantly reduced costs. Advanced sensor technologies are necessary to fully harness SHM in applications involving harsh or remote environments, life-critical systems, mass production vehicles, robotic systems, and others. Fiber Bragg grating (FBG) sensors are an attractive solution for in-situ health monitoring due to their low weight, resistance to electromagnetic noise, ability to be multiplexed, and accuracy for real-time measurements. However, effective embedment of FBG sensors into metal has proved challenging. Ultrasonic additive manufacturing (UAM) has been demonstrated for solid-state fabrication of 3D structures with embedded FBG sensors. In this thesis, UAM embedded FBG sensors for SHM applications are investigated. Embedment of a fiber using UAM was shown to have little effect on the tensile and fatigue properties of aluminum coupons. Furthermore, the ability of UAM embedded FBG sensors to detect and monitor crack growth in Compact Tension (CT) specimens is demonstrated. UAM embedded FBG sensors 3 mm from the initiation site were able to accurately detect cracks of length 0.286 ± 0.033 mm. UAM embedded FBG sensors are shown to accurately track crack growth until near failure. Furthermore, UAM embedded FBG sensors 3 mm, 6 mm, and 9 mm from the initiation site detected a crack that initiated to 0.350 mm. Finally, the potential for high temperature applications is also examined through elevated temperature testing. Fiber optics embedded into aluminum using UAM are shown to be more resilient to degradation at elevated temperatures than exposed fibers. UAM embedded FBG sensors are therefore shown to be an effective type of sensor for SHM applications.

Committee: Marcelo Dapino Dr. (Advisor); David Hoelzle Dr. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2019, Engineering and Applied Science: Mechanical Engineering

This research is an experimental study of sensitivity enhancement for multiwalled carbon nanotube based multifunctional composite materials for health monitoring of large structures. Multi Walled Carbon Nanotubes are studies by various groups around the academic research community for application in sensor development and structural health monitoring. They have been spun to create threads and rolled to create CNT sheets at University of Cincinnati Nanoworld Labs. Many research institutions are trying to commercialize CNT based sensors and electronics. At University of Cincinnati, CNT threads were used to create structural health monitoring sensors. The goal of this thesis is to explore the use of multiwalled CNT sheet sensors for structural health monitoring. This thesis presents an idea for capacitive and resistive sensing for structural health monitoring between large sheets of composite laminates. Two type of sensors were designed using CNT sheets – 1) CNT sheets were added as composite lamina 2) CNT sheets were separated by insulating material to design customized sensors to be added as a layer between composite laminate. Insulating material FGF-fiber glass woven fabric is used as lamina and multiwalled carbon nanotube sheets are electrodes to build the CNT based composite material. Circuits for resistance measurement across and between CNT terminals were created and resistance and capacitance change before and after impact test was measured using multimeter. The goal and focus of the thesis were sensitivity enhancement to estimate damage in composites due to impact.

Committee: Mark Schulz Ph.D. (Committee Chair); Vesselin Shanov Ph.D. (Committee Member); Matthew Steiner Ph.D. (Committee Member); Sarah Watzman Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2019, Engineering and Applied Science: Materials Science

Two limitations of polymeric fiber reinforced composite materials are their susceptibility to damage, and their reduced thermal conductivity compared to metals. This thesis proposes techniques to address these limitations and to make composite materials more multi-functional and reliable like metals. Laminated composites are prone to damage due to impacts which cause localized delamination and microcracking. It is challenging to detect these types of damages because non-destructive inspection takes the structure out of service, inspection is slow and expensive on large structures, and the damage may not be obvious on the outer surface. Left undetected, the damage can propagate due to loading during normal use and lead to reduced performance or failure of a component or the entire structure. In most applications, detection of these internal damages is critical to maintain the performance and structural integrity of components, and to assure the safety of the aircraft or wind turbine. Composites are used because they are lightweight and corrosion resistant, but must be over-designed to account for possible undetected damage which increases cost and weight. Structural Health Monitoring (SHM) is an approach to continuously monitor structures for damage while they are in operation. The complexity and cost of monitoring large structures where damage can occur anywhere on the structure has so far restricted the application of SHM systems. This thesis presents a method to monitor large composite structures for damage using a simple and reliable approach. Carbon Nanotube (CNT) sheets are used as sensors to detect internal damages in composites. Two CNT sheets separated by a dielectric form the sensor. Impact damage can short circuit the two CNT layers indicating that damage has occurred. Since CNTs have good electrical and thermal conductivity, they will improve the thermal conductivity of composites. Through the thickness thermal conductivity of CNT sheet is characterize (open full item for complete abstract)

Committee: Mark Schulz Ph.D. (Committee Chair); Jude Iroh Ph.D. (Committee Member); Ashley Paz y Puente Ph.D. (Committee Member) Subjects: Materials Science

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Materials Engineering

In this study, multiple approaches were explored for building advanced nanocomposite sensors intended for use in fiber reinforced organic matrix composite structures. One expected application of such technology is sensing of chemical penetration in the walls of large chemical tanks. The work described herein involved development and characterization of various novel conductive nanocomposites from polymeric feedstocks as well as carbon nanoparticles. The first approach consisted of using pitch based, liquid crystal molecular additives to polyacrylonitrile (PAN) to create novel electrospun carbon nanofibers. Raman spectroscopy confirmed the increase of an ordered structure in PAN/pitch based carbon nanofibers by analyzing the sharpness of the G band. As a result, the addition of pitch increased the degree of graphene alignment because of the high amount of liquid crystal present in the pitch. This structure led to enhanced physical properties of the carbon nanofibers. The second approach used a conductive network of conjugated polymer (polyaniline, PAni) nanoparticles dispersed in a blend of polyvinylpyrrolidone (PVP) and polyurethane (PU). PAni was synthesized using an in situ polymerization method which resulted in colloidal PAni or PAni nanowires. PAni nanowires self-assembled into scattered fractal networks. After adding PU, a concentrated PAni/PVP phase occurred. Such a phenomenon was attributed to the balance between blocking force and van der Waals force. When the surface tension is the determining factor in the 'island', the round shaped phase separation occurs. The surface tension and van der Waals force were two determining factors in the formation of bi-continuous phase separation. When the forces were in equilibrium, a fractal network structure was formed and the polymer blends were very stable. A flexible conductive fabric was successfully prepared by coating the conductive ternary mixture onto a non-woven fabric. The last approach uses carbon na (open full item for complete abstract)

Committee: Khalid Lafdi (Committee Chair); Donald Klosterman (Committee Member); Erick Vasquez (Committee Member); Vikram Kuppa (Committee Member) Subjects: Materials Science; Nanotechnology; Polymers

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

Cable-stayed bridges have received significant attention in recent years due to ease of construction, reduced bending moments applied on the deck, being stiffer in comparison with other types of long span bridges, and their aesthetic value. In a cable-stayed bridge, the dead load (weight of the bridge) and the live load (the traffic load) are directly transferred to the towers through stay cables. Wind/rain induced vibrations, fatigue, and galvanic corrosion can cause cable deterioration. Deterioration of stay cables can cause the reduction of cable load capacity; thus, continuous health monitoring of stay cables is strongly suggested. There are different condition assessment methods proposed to monitor the stay cables in cable structures such as traditional visual inspection methods, dissection of stay cables, ultrasonic testing, thermography, impulse radar, and radiography. Consistency of cable tension over time is also considered as a health indicator for both cables and super structure of cable structures. Cable tension can be measured directly (using load sensors) or it can be estimated by measuring different parameters of the cable such as stress, strain, or natural frequencies. The methods that use cable natural frequencies to estimate the cable tension are called vibration-based tension estimation methods. The main objective of this dissertation is to propose a general framework for vibration-based cable tension estimation so that it can be used along with various cable models and system identification methods to estimate the cable tension in cable structures. System identification methods will be used to identify the natural frequencies of the stay cables and cable models will be employed to create an error function representative of the difference between experimentally measured cable natural frequencies and analytical cable natural frequencies. Employing different cable models, the proposed framework will be evaluated using the experimental data measured (open full item for complete abstract)

Committee: Randall Allemang Ph.D. (Committee Chair); David Brown Ph.D. (Committee Member); Arthur Helmicki Ph.D. (Committee Member); Victor Hunt Ph.D. (Committee Member); Allyn Phillips Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2017, Engineering and Applied Science: Civil Engineering

The behavior of a structure can be documented by implementation of a Structural Health Monitoring (SHM) system to collect long-term measurement data. SHM makes it possible to detect changes of structural responses and, in some cases, correlate the changes of structural responses with changes in material and geometric properties. The ability of the SHM system to predict the responses is directly correlated to the reliability and effectiveness of the selected network of sensors. Therefore, an effective SHM system is developed based upon the selection of the critical points of a structure that have to be inspected and monitored. This thesis concentrates on the methodology for determining and validating the critical points of the Ironton-Russell Bridge, a cable-stayed bridge in Ironton, OH and Russell, KY. A set of finite element models (i.e., both SAP2000 and MIDAS) were employed to determine load responses used in the design and construction of the bridge. Member capacities were calculated using RESPONSE 2000. Capacity and load responses were used to calculate the rating factors for towers, edge girders, and piers. Based on this information, the critical locations in the bridge were determined and used as the basis for SHM sensor locations, which had been selected for installation during construction of the bridge. The differences in the design drawings and the as-built structure were studied especially for their effects on the rating factors and, thus, the locations of the sensors. Future scope of the project is to conduct truck load tests at predetermined locations based on the influence line diagrams, and to compare the measured data to the simulation data.

Committee: Bahram| Shahrooz (Committee Chair); Arthur Helmicki (Committee Member); Richard Miller (Committee Member) Subjects: Civil Engineering

MS, University of Cincinnati, 2017, Engineering and Applied Science: Electrical Engineering

In a time where Structural Health Monitoring (SHM) is a topic of vital importance for safety and maintenance of critical structures such as bridges, detecting damages or anomalies as well as analyzing the normal behavior of structures has also gained significance in recent years. Data models have been increasingly used in recent years for tracking normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long-term measurement data of structures. Structural Health Monitoring (SHM) of civil infrastructure like highway bridges, during construction or in-service use is executed at University of Cincinnati Infrastructure Institute (UCII), thus giving access to the actual measurement data of the bridges. The essence of this SHM system lies in the processing of data, where it is able to detect anomalies in the data. The current system utilizes linear regression method to detect outliers in the bridge data. This study introduces a novel anomaly detection method employing one-class Support Vector Machines (SVM) and compares the performance of SVMs with traditional regression model. This method is implemented on the measurement data of Ironton-Russell Bridge monitored by UCII, which was in-service use, and its results are compared with linear regression as a case study. The method is further implemented on Ironton-Russell Replacement Bridge which UCII has been monitoring since the construction stage. The actual construction events of the Ironton-Russell Replacement Bridge are being used as validation for the comparison. The aim is to show advantages of employing SVMs due to their abilities to classify damages even with minimum training data. The results show that using SVMs will improve the detectability and also the (open full item for complete abstract)

Committee: Arthur Helmicki Ph.D. (Committee Chair); Victor Hunt Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science, The Ohio State University, 2017, Mechanical Engineering

Fiber Bragg Grating (FBG) sensorsare optical fibers that detect in-situ strain through deviation of a reflected wavelength of light to detect in-situ strain. These sensors are immune to electromagnetic interference, and the inclusion of multiple FBGs on the same fiber allows for a seamlessly integrated sensing network. FBGs are attractive for embedded sensing in aerospace applications due to their small noninvasive size and prospect of constant, real-time nondestructive evaluation. FBGs are typically used in composite laminate type applications due to difficulties in building them into metallic structures. Additive manufacturing, also referred to as 3D printing, can allow for the inclusion of sensors inside of structural entities by the building of material around the sensor to be embedded. In this study, FBG sensors are embedded into aluminum 6061 via ultrasonic additive manufacturing (UAM), a rapid prototyping process that uses high power ultrasonic vibrations to weld similar and dissimilar metal foils together. UAM was chosen due to the desire to embed FBG sensors at low temperatures, a requirement that excludes other additive processes such as selective laser sintering or fusion deposition modeling. This study demonstrated the feasibility of embedding FBGs in aluminum 6061 via UAM. Further, the sensors were characterized in terms of birefringence losses, post embedding strain shifts, consolidation quality, and strain sensing performance. Sensors embedded into an ASTM test piece were compared against an exterior surface mounted foil strain gage at both room and elevated temperatures using cyclic tensile tests. The effects of metal embedment at temperatures above the melting point of the protective coating (160 degrees Celsius) of the FBG sensors were explored, and the hermetic sealing of the fiber within the metal matrix was used to eplain the coating survival. In-situ FBG sensors were also used to monitor the UAM process itself. Lastly, an example app (open full item for complete abstract)

Committee: Marcelo Dapino (Advisor); Mo-How Shen (Committee Member) Subjects: Mechanical Engineering

Master of Science (M.S.), University of Dayton, 2016, Mechanical Engineering

In order to more effectively monitor the health of composite structures, a fuzzy fiber strain sensor was created. The fuzzy fiber is a bundle of glass fibers with carbon nanotubes or nanofibers grown on the surface using a novel chemical vapor deposition process. The nanotube coating makes the fiber bundle conductive while the small conductive path increases sensitivity. The fuzzy fiber sensor can replace conventional metal foil strain gauges in composite applications. The sensor was first characterized by use of a micro-tension test to generate load vs. resistance plots to demonstrate the feasibility of the sensor. The fibers were then cast into epoxy dogbone specimens to enable testing with an extensometer to quantify its strain sensitivity. Sensors were then embedded in carbon fiber prepreg panels. Specimens were prepared to demonstrate their performance in a composite laminate typical of aerospace structures. A multi-axial specimen was constructed to test sensor response to longitudinal, transverse and off-axis loading cases. Cyclic tests were performed to check for hysteresis or non-reversible changes to the sensor. A finite element model was created to compare the experimental results to the expected behavior based on the Poisson effect.

Committee: Khalid Lafdi (Committee Chair); Thomas Whitney (Committee Member); Vinod Jain (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2015, Engineering and Applied Science: Electrical Engineering

The University of Cincinnati Infrastructure Institute has been dedicated to Structural Health Monitoring for about 20 years. UCII establishes a whole set of monitoring system including sensors, data acquisition equipment and a customer website for each bridge that is to be monitored. The Ironton-Russell Bridge Replacement is the first bridge that UCII has monitored since the bridge's construction stage. At the heart of UCII's monitoring system is the ability to detect any anomalies; among these anomalies might be damages caused by structural changes due to creep, shrinkage, crack and so forth. The existing anomaly detection algorithm assumes a linear relationship between strain and temperature. To complement the anomaly detection, an Autoregressive Model based algorithm is proposed which doesn't rely on the relationship between strain and temperature. Also proposed is a probabilistic approach which employs t-distribution to identify anomalies, moreover, this approach is promising in discerning anomalies that are caused by temperature change from those not related to temperature. These two approaches are proved to be applicable for both in-construction and in-service bridges.

Committee: Arthur Helmicki Ph.D. (Committee Chair); H. Howard Fan Ph.D. (Committee Member); Victor Hunt Ph.D. (Committee Member) Subjects: Engineering

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

In this dissertation, long-term measurement data that is being collected from the Jeremiah Morrow Bridge will be used to quantify annual variation in data and establish boundaries for detecting abnormal behaviors including anomalies from univariate trends or multivariate correlational trajectories. Long-term measurement data from the US Grant Bridge will also be used for calibrating an autoregressive integrated moving average model and distinguishing maintenance events. First, the monitoring system that has been used for the two bridges under evaluation will be overviewed. Second, sensory data will be analyzed as a univariate time series and transformed to a simple regression model using temperature data as exogenous inputs. Third, correlation between temperature and sensory data will be analyzed and abnormal changes or outliers within the bivariate time series will be identified. We will try to identify how temperature trends change over time and use the dynamic trends to probabilistically classify temperature-caused events. Fourth, load responses of a bridge will be used to define load signatures; whenever a lane load exists on a bridge (e.g., halted or slowed traffic), the sensory network responds in a certain way that can be quantified by correlation of measured values. Using the identified signature, we should be able to distinguish lane loads from thermal responses. Finally, combining univariate time series outlier detection, variable correlational coefficients (Principle components), extreme thermal response signature, and the load response signature, an integrated monitoring system will be proposed and the results will be compared with previously implemented systems by UCII for these structures.

Committee: Victor Hunt Ph.D. (Committee Chair); Douglas Nims Ph.D. (Committee Member); Arthur Helmicki Ph.D. (Committee Member); Ali Minai Ph.D. (Committee Member); William Wee Ph.D. (Committee Member) Subjects: Electrical Engineering