Although nondestructive evaluation techniques have been implemented in many industry fields and proved to be useful, they are generally expensive, time consuming, and the results may not always be reliable. To overcome these drawbacks, structural health monitoring (SHM) systems has received significant attention in the past two decades. As structural systems are becoming more complicated and new materials are being developed, new methodologies, theories, and approaches in SHM have been developed for damage detection, diagnosis, and prognosis. Among the methods developed, the guided Lamb wave based SHM can be a promising technique for damage evaluation since it provides reliable damage information through signals propagating over large distance with little loss of amplitude. While this method is effective for damage assessment, the guided Lamb wave contains complicated mode characteristics, i.e. an infinite number of wave modes exist and these modes are generally dispersive. For this reason, a minimum number of wave modes and various signal processing algorithms are implemented to obtain better signal interpretations. Phased array beamsteering is an effective means for damage detection in guided Lamb wave SHM systems. Using this method, the wave energy can be focused at localized directions or areas by controlled excitation time delay of each array element. In this research, two types of transducers are utilized as phased array elements to compare beamsteering characteristics. Monolithic piezoceramic (PZT) transducers are investigated for beamsteering by assuming omnidirectional point sources for each actuator. MacroFiber Composite (MFC) transducers with anisotropic actuation are also studied, considering the wave main lobe width, main lobe magnitude, and side lobe levels. Analysis results demonstrate that the MFC phased arrays perform better than the PZT phased arrays for a range of beamsteering angles and have reduced main lobe width and side lobe levels. Experiments using the PZT and MFC phased arrays on an aluminum plate are also performed and compared to the analysis results. A time-frequency signal processing algorithm coupled with a machine learning method can form a robust damage diagnostic system. Four types of such algorithms, i.e. short time Fourier transform, Wigner-Ville distribution, wavelet transform, and matching pursuit, are investigated to select an appropriate algorithm for damage classification, and a spectrogram based on short time Fourier transform is adopted for its suitability. A machine learning algorithm called Adaboost is chosen due to its effectiveness and high accuracy performance. The classification is preformed using spectrograms and Adaboost for crack and corrosion damages. Artificial cracks and corrosions are created in Abaqus® to obtain the training samples consist of spectrograms. Several beam experiments in laboratory and additional simulations are also performed to get the testing samples for Adaboost. The analysis results show that not only correct damage classification is possible, but the confidence levels of each sample are acquired. / Ph. D.

This work investigates the feasibility of developing a self-powered structural health monitoring (SHM) smart tire using piezoelectric materials. While this work is divided into two components: SHM and energy harvesting, the context of smart tire in this work is defined as the development of a SHM system that (i) has self-powering capabilities, and (ii) addresses the potential of embedding sensors. The use of impedance based SHM on a tire is severely limited due to the low stiffness and high damping characteristics of the tire. This work propose the use of a high voltage impedance analyzer, and the addition of electrical circuit to enhance the damage detection process. Experimental work was conducted on an aluminum beam and on a tire section with commercially available piezoelectric sensors. The use of a high voltage impedance analyzer was demonstrated to provide insight on damage type and damage location. Two sensors were connected in parallel as an effective sensory system, and was shown to reduce interrogation time, but reduce damage identification sensitivity. With added electrical circuits, a belt separation on the tire was successfully detected by the shift in electrical impedance signature. For the energy harvesting portion of this work, a bimorph piezoelectric energy harvester model was derived using extended Hamilton's principle and the linear constitutive relations of piezoelectric materials. Comparison of model with experimental data at increasing loading conditions demonstrated the monotonic increase in voltage output, with linear asymptotes at extreme loading conditions (short-circuit and open-circuit). It also demonstrated the existence of an optimal resistive load for maximum power output. To address the ability to embed sensors, an existing fabrication process to grow arrays of ZnO nanowires in carbon fiber reinforced polymer was used in this work. Comparison of power generation from a composite beam with ZnO nanowires with a composite beam without ZnO nanowires demonstrated the power generation capabilities of the nanowires. A maximum peak voltage of 8.91 mV and peak power of 33.3 pW was obtained. After the application of 10V DC, a maximum of 45 pW was obtained. However, subsequent application of 20V DC reduced the maximum peak power output to 2.5 pW. Several attempts to increase power generation including adding a tip mass and changing the geometry of the composite beam were conducted. Finally, the theoretical voltage frequency response function obtained from the theoretical piezoelectric constant and dielectric constant of a single ZnO nanowire were compared to the experimental voltage frequency response function. The discrepancies were discussed. / Master of Science

The goal of this work is to investigate the piezoelectricity of composite laminates embedded with layers of zinc oxide (ZnO) nanowires. ZnO nanowire embedded composites have the potential to sense and actuate giving the potential for these smart composites to serve the function of being load bearing structures and monitoring the integrity of the structure. This work examines the piezoelectric characteristics of composite beams by investigating their electromechanical coupling in the form of vibration under the presence of electrical excitation. With the help of a mathematical model, piezoelectric constants are estimated for these samples. A layer of ZnO nanowires were grown on plane woven fiberglass fabric that was incorporated into a carbon fiber epoxy composite. The beam deflection velocity was measured as a varying voltage was applied to the composite. Using Hamilton's Principle and Galerkin's method of weighted residuals, a mathematical model was derived to estimate piezoelectric constants for the composites from the experimental data. Piezoelectric properties were determined using vibrational testing and a mathematical model. Piezoelectric constants h31, g31, and d31 were estimated to be 9.138 E7 V/m, 6.092 E-4 Vm/N, and 2.46 E-14 respectively. To demonstrate the electromechanical coupling, ZnO nanowire composites were bonded to Al beams that were progressively damaged to determine if a change in electrical impedance could be observed to correspond to the change in structural impedance of the host beam. Changes in impedance were detected by a change in root mean squared deviation damage metric M. A significant correlation was shown between increasing damage in the host beam and an increase in damage metric M. / Master of Science

The strain and damage sensing abilities of randomly oriented multi-walled carbon nanotubes (MWCNTs) dispersed in the polymer binder of energetic composites were experimentally investigated. Ammonium perchlorate (AP) crystals served as the inert energetic and atomized aluminum as the metallic fuel, both of which were combined to create a representative fuel-oxidizer filler often used for aerospace propulsive applications. MWCNTs were dispersed within an elastomer binder of polydimethylsiloxane (PDMS), and hybrid energetics were fabricated from it, with matrix material comprised of the identified fillers. The nanocomposites were characterized based on their stress-strain response under monotonic uniaxial compression to failure, allowing for the assessment of effects of MWCNTs and aluminum powder on average compressive elastic modulus, peak stress, and strain to failure. The piezoresistive response was measured as the change in impedance with applied monotonic strain in both the mesoscopic and microscopic strain regimes of mechanical loading for each material system, as well as under ten cycles of applied compressive loading within those same strain regimes. Gauge factors were calculated to quantify the magnitude of strain and damage sensing in MWCNT-enhanced material systems. Electrical response of single-cycle thermal loading was explored with epoxy in place of the elastomer binder of the previously discussed studies. Piezoresistive response due to microscale damage from thermal expansion was observed exclusively in material systems enhanced by MWCNTs. The results discussed herein validate structural health monitoring (SHM) applications for embedded carbon nanotube sensing networks in polymer-based energetics under unprecedented cyclic loads. / Master of Science / The ability to characterize both deformation and damage in real time within materials of high energetic content, such as solid rocket propellant, is of great interest in experimental mechanics. Common energetic ammonium perchlorate, in the fonn of crystal particles, was embedded in polymer binders (ie PDMS and epoxy) and investigated under a variety of me­chanical and thermal loads. Carbon nanotubes, conductive tube-shaped molecular structures of carbon atoms, have been demonstrated in prior proofs of concept to induce substantial electrical response change when dispersed in composites which are experiencing strain. With the introduction of carbon nanotubes in the energetic composites investigated herein, the electrical response of the material systems was measured as a change in impedance with applied strain. Elastomer-bonded energel.ks were t.esl.ed under monotonic compression and cyclic compression, and expanded exploration was done on these material systems with the additional particulate of aluminum powder, allowing for varied particulate sizes and conductivity enhancement of the overall composite. The magnitude of the resulting piezoresistive change due to strain and microscale damage was observed to increase dramatically in material systems enhanced by MWCNT networks. Local heating was used to explore thermal loading on epoxy-bonded energetic material systems, and sensing of permanent damage to the­ material through its CNT network was proven through a permanent change in the electrical response which was exclusive to the CNT-enhanced material systems. These results demon­strate valid structural health monitoring (SHM) applications for embedded carbon nanotube sensing networks in particulate energetic composites, under a variety of load cases.

From 2012 - 2015 a foundation for future research in Goodwin Hall was designed, tested,developed, and implemented through an instrumentation project supported by the College of Engineering at Virginia Polytechnic Institute and State University. This required the design and implementation of a distributed, networked, and synchronized data acquisition system along with supporting hardware and software capable of measuring 227 accelerometers placed in 129 locations throughout the building. This system will provide a platform for research into a variety of topics, including structural health monitoring, building dynamics, human motion, and energy harvesting. Additionally, the system will be incorporated into the education curriculum by providing real-world data and hardware for students to interact with. This thesis covers the contributions of the author to the project. / Master of Science

Defected wheel are one the major reasons endangered state of railroad vehicles safety statue, due to vehicle derailment and worsen the quality of freight and passenger transportation. Therefore, timely defect detection for monitoring and detecting the state of defects is highly critical. This thesis presents a passive non-contact acoustic structural health monitoring approach using ultrasonic acoustic emissions (UAE) to detect certain defects on different structures, as well as, classifying the type of the defect on them. The acoustic emission signals used in this study are in the ultrasonic range (18-120 kHz), which is significantly higher than the majority of the research in this area thus far. For the proposed method, an impulse excitation, such as a hammer strike, is applied to the structure. In addition, ultrasound techniques have higher sensitivity to both surface and subsurface defects, which make the defect detection more accurate. Three structures considered for this study are: 1) a longitudinal beam, 2) a lifting weight, 3) an actual rail-wheel. A longitudinal beam was used at the first step for a better understanding of physics of the ultrasound propagation from the defect, as well, develop a method for extracting the signature response of the defect. Besides, the inherent directionality of the ultrasound microphone increases the signal to noise ratio (SNR) and could be useful in the noisy areas. Next, by considering the ultimate goal of the project, lifting weight was chosen, due to its similarity to the ultimate goal of this project that is a rail-wheel. A detection method and metric were developed by using the lifting weight and two type of synthetic defects were classified on this structure. Also, by using same extracted features, the same types of defects were detected and classified on an actual rail-wheel. / Master of Science

The research presented in this thesis is based on improving current structural health monitoring (SHM) technology. Structural health monitoring is a damage detection technique that involves placing intelligent sensors on a structure, periodically recording data from the sensors, and using statistical methods to analyze the data in order to assess the condition of the structure. This work focuses on improving two areas of SHM; baseline management and energy supplies. Several successful SHM methods have been developed in which prerecorded baseline measurements are compared to current measurements in order to identify damage. The need to compare new data to a prerecorded baseline can present several complications including data management issues and difficulty in controlling the effects of varying environmental conditions on the data. Another potential area for improvement in SHM systems deals with their energy supplies. Many SHM systems currently require wired power supplies or batteries to operate. Practical SHM applications often require inexpensive, stand alone sensors, data acquisition, and processing hardware that does not require maintenance. To address the issue of baseline management, a novel SHM technique is developed. This new method accomplishes instantaneous baseline measurements by deploying an array of piezoelectric sensors/actuators used for Lamb wave propagation-based SHM such that data recorded from equidistant sensor-actuator paths can be used to instantaneously identify several common features of undamaged paths. Once identified, features from these undamaged paths can be used to form a baseline for real-time damage detection. This method utilizes the concept of sensor diagnostics, a recently developed technique that minimizes false damage identification and measurement distortion caused by faulty sensors. Several aspects of the instantaneous baseline damage detection method are explored in this work including the implementation of sensor diagnostics, determination of the features best used to identify damage, development of signal processing algorithms used to analyze data, and the comparison of two sensor/actuator deployment schemes. The ultimate goal in the development of practical SHM systems is to create autonomous damage detection systems. A limiting factor in current SHM technology is the energy supply required to operate the system. Many existing SHM systems utilize wired power supplies or batteries to power sensors, data transmission, data acquisition, and data processing hardware. Although batteries eliminate the need to run wires to SHM hardware, their periodic replacement requires components to be placed in easily accessible locations which is not always practical, especially in embedded applications. Additionally, there is a high cost associated with battery monitoring and replacement. In an effort to eliminate replaceable energy supplies in SHM systems, the concept of energy harvesting is investigated. Energy harvesting devices are designed to capture surrounding ambient energy and convert it into usable electrical energy. Several types of energy harvesting exist, including vibration, thermal, and solar harvesting. A solar energy harvesting system is developed for use in powering SHM hardware. Integrating energy harvesting technology into SHM systems can provide autonomous health monitoring of structures. / Master of Science

In recent years, composite materials have been widely used in several applications due to their superior mechanical properties including high strength, high stiffness, and low density. Despite the remarkable advancements in theoretical and computational methods for analyzing composites, investigating the effect of lamina properties and lay-up configurations on the strength of composites still remains an active field of research. Finite Element Method (FEM) and Extended Finite Element Method (XFEM) are powerful tools for solving the boundary value problems. One of the objectives of this work is to employ XFEM as a defect identification tool for predicting the crack initiation and propagation in composites. Another major objective of this study is to investigate the damage development in hyperelastic materials. Two Finite Element models are adopted to study this phenomenon: multiscale modeling of the cord-rubber composites in tires and modeling of intelligent tires for evaluating the feasibility of the proposed defect detection technique. A new three-dimensional finite element approach based on the multiscale progressive failure analysis is employed to provide the theoretical predictions for damage development in the cord-rubber composites in tires. This new three-dimensional model of the cord-rubber composite is proposed to predict the different types of damage including matrix cracking, delamination, and fiber failure based on the micro-scale analysis. This process is iterative and data is shared between the finite element and multiscale progressive failure analysis. It is shown that the proposed cord-rubber composite model solves the problems corresponding to embedding the rebar elements to the solid elements and also increases the fidelity of numerical analysis of composite parts since the laminate characteristic variables are determined from the microscopic parameters. A tire rolling analysis is then conducted to evaluate the effects of different variables corresponding to the cord-rubber composite on the performance of tires. Tires operate on the principle of safe life and are the only parts of the vehicle which are in contact with the road surface. Establishing a computational method for defect detection in tire structures will help manufacturers to fix and develop more reliable tire designs. A Finite Element model of a tire with a tri-axial accelerometer attached to its inner-liner was developed and the effects of changing the normal load, longitudinal velocity and tire-road contact friction on the acceleration signal were investigated. Additionally, using the model, the acceleration signals obtained from several accelerometers placed in different locations around the inner-liner of the intelligent tire were analyzed and the defected areas were successfully identified. Using the new intelligent tire model, the lengths, locations, and the minimum number of accelerometers in damage detection in tires are determined. Comparing the acceleration signals obtained from the damaged and original tire models results in detecting defects in tire structures. / PHD

Detection, characterization and prognosis of damage in civil, aerospace and mechanical structures, known as structural health monitoring (SHM), have been a growing area of research over the last few decades. As several in-service civil, mechanical and aerospace structures are approaching or even exceeding their design life, the implementation of SHM systems is becoming a necessity. SHM is the key for transforming schedule-driven inspection and maintenance into condition-based maintenance, which promises enhanced safety and overall life-cycle cost reduction. While damage detection and characterization can be achieved, among other techniques, by analyzing the dynamic response of the structure under test, damage prognosis requires the additional knowledge of loading patterns acting on the structure. Accurate, nondestructive, and reference-free measurement of the state-of-stress in structural components has been a long standing challenge without a fully-satisfactory outcome. In light of this, the main goal of this research effort is to advance the current state of the art of structural health and loading monitoring, with focus being cast on impedance-based SHM and acoustoelastic-based stress measurement techniques. While impedance-based SHM has been successfully implemented as a damage detection technique, the utilization of electromechanical impedance measurements for damage characterization imposes several challenges. These challenges are mainly stemming from the high-frequency nature of impedance measurements. Current acoustoelastic-based practices, on the other hand, are hindered by their poor sensitivity and the need for calibration at a known state of stress. Addressing these challenges by developing and integrating theoretical models, numerical algorithms and experimental techniques defines the main objectives of this work. A key enabler for both health and loading monitoring techniques is the utilization of piezoelectric transducers to excite the structure and measure its response. For this purpose, a new three-layer spectral element for piezoelectric-structure interaction has been developed in this work, where the adhesive bonding layer has been explicitly modeled. Using this model, the dynamic response of piezoelectric-augmented structures has been investigated. A thorough parametric study has been conducted to provide a better understanding of bonding layer impact on the response of the coupled structure. A procedure for piezoelectric material characterization utilizing its free electromechanical impedance signature has been also developed. Furthermore, impedance-based damage characterization has been investigated, where a novel optimization-based damage identification approach has been developed. This approach exploits the capabilities of spectral element method, along with the periodic nature of impedance peaks shifts with respect to damage location, to solve the ill-posed damage identification problem in a computationally efficient manner. The second part of this work investigates acoustoelastic-based stress measurements, where model-based technique that is capable of analyzing dispersive waves to calculate the state of stress has been developed. A criterion for optimal selection of excitation waveforms has been proposed in this work, taking into consideration the sensitivity to the state of stress, the robustness against material and geometric uncertainties, and the ability to obtain a reflections-free response at desired measurement locations. The impact of material- and geometry-related uncertainties on the performance of the stress measurement algorithm has also been investigated through a comprehensive sensitivity analysis. The developed technique has been experimentally validated, where true reference-free, uncalibrated, acoustoelastic-based stress measurements have been successfully conducted. Finally, the applicability of the aforementioned health and loading monitoring techniques to railroad track components has been investigated. Extensive in-lab experiments have been carried out to evaluate the performance of these techniques on lab-scale and full-scale rail joints. Furthermore, in-field experiments have been conducted, in collaboration with Norfolk Southern and the Transportation Technology Center Inc., to further investigate the performance of these techniques under real life operating and environmental conditions. / Ph. D.

The research presented in this manuscript introduces an independent structural health monitoring (SHM) system capable of performing impedance-based testing and detecting shifts in resonant frequencies. This independent structural health monitoring system incorporates a low power wireless transmitter that sends a warning signal when damage is detected in a structure. Two damage detection techniques were implemented on the SHM system and successfully used for evaluating structural damage. The first impedance-based technique is used to detect a gouge introduced to a composite plate. The second technique is a modal parameter technique that analyzes shifts in natural frequency; this technique was used to detect structural changes in an aluminum cantilever beam. In additional to the above test structures, an aircraft rib provided by the United States Air Force was also tested. This test was performed using the HP 4192A impedance analyzer so that the advantage of high frequency impedance-based tested could be demonstrated. Insight is given into the power characteristics of SHM systems and the need to incorporate power harvesting into these SHM devices is addressed. Also, a comparison between digital signal processors and microprocessors is included in this document. / Master of Science