Asphalt composites are used to construct 90% of roads in the United States. These composites consist of asphalt binder, which is a product of the refinery process of oil, aggregates, and air voids. Fatigue cracking is one of the most important distresses that causes damage in asphalt pavements. However, there is still a gap in the understanding of the fatigue process of asphalt composites, such as the influence of material properties on this phenomenon and how the material microstructure changes as a result of fatigue damage. This study focuses on the results of two experiments that were performed on asphalt composites to better understand phenomena related to fatigue cracking: nano-mechanical characterization of the properties of the asphalt composite material and X-ray Computed Tomography nondestructive imaging of damage in the microstructure. These experimental measurements were performed on specimens that are first damaged in the Dynamic Mechanical Analyzer (DMA). The DMA is a tool commonly used for the characterization of fatigue cracking. This test method applies cyclic loads on asphalt composites, damaging them, and in the process determines the viscoelastic properties of the composite throughout the test. The nano-mechanical characterization experiment gives valuable results of the elastic modulus and hardness of the aggregate, binder, and the aggregate-binder interface that can be used to characterize different binder and aggregate combinations. The nanoindentation experiment successfully measured interface properties in the mix. The interface has elastic modulus and hardness values greater than the binder but smaller than the aggregate. This demonstrates that an interaction between these two phases creates a dissimilar phase between the two. The second experiment using X-ray CT gives measurements that are indicative of the influences of fatigue damage on micro-level changes in the material microstructure. The results of this experiment revealed important changes regarding the nature of fatigue damage and its relationship to changes in the geometry of air voids and cracks in asphalt composites. The X-ray CT experiment measured size and shape parameters of air voids at 20 microns/pixel resolution at different damage levels. These results illustrated that reduction in bonding strength in the binder is involved in failure in the mix and thus fatigue cracking is not solely responsible for failure. This conclusion is made based on the results not showing a statistically significant change in air void shape and size parameters with increased damage. This is illustrated by viewing changes in the air void structure within the mix, there is no evidence of crack propagation, or drastic changes in the shape, size, or volume of air voids within the mix.

Impedance-based Non-Destructive Evaluation for Additive Manufacturing (INDEAM) is rooted in the field of Structural Health Monitoring (SHM). INDEAM generalizes the structure-to-itself comparisons characteristic of the SHM process through introduction of inter-part comparisons: instead of comparing a structure to itself over time, potentially-damaged structures are compared to known-healthy reference structures. The purpose of INDEAM is to provide an alternative to conventional nondestructive evaluation (NDE) techniques for additively manufactured (AM) parts. In essence, the geometrical complexity characteristic of AM processes combined with a phase-change of the feedstock during fabrication complicate the application of conventional NDE techniques by limiting direct access for measurement probes to surfaces and permitting the introduction of internal defects that are not present in the feedstock, respectively. NDE approaches that are capable of surmounting these challenges are typically highly expensive. In the first portion of this work, the procedure for impedance-based NDE is examined in the context of INDEAM. In consideration of the additional variability inherent in inter-part comparisons - as opposed to part-to-itself comparisons - the metrics used to quantify damage or change to a structure are evaluated. Novel methods of assessing damage through impedance-based evaluation are proposed and compared to existing techniques. In the second portion of this work, the INDEAM process is applied to a wide variety of test objects. This portion considers how the sensitivity of the INDEAM process is affected by defect type, defect size, defect location, part material, and excitation frequency. Additionally, a procedure for studying the variance introduced during the process of instrumenting a structure is presented and demonstrated. / Doctor of Philosophy / Impedance-based Non-Destructive Evaluation for Additive Manufacturing (INDEAM) is a quality control approach for detecting defects in structures. As indicated by the name, impedance-based evaluation is discussed in this work in the context of qualifying additively manufactured (3D printed) structures. INDEAM fills a niche in the wider world of nondestructive evaluation techniques by providing a less expensive means to qualify structures with complex geometry. Complex geometry complicates inspection by preventing direct, physical access to all the surfaces of a part. Inspection approaches for parts with complex geometry suffuse a structure with energy and measure how the energy propagates through the structure. A prominent technique in this space is CT scanning, which measures how a structure attentuates x-rays passing through it. INDEAM uses piezoelectric materials to both vibrate a structure and measure its response, not unlike listening for the dull tone of a cracked bell. By applying voltage across a piezoelectric patch glued to a structure, the piezoelectric deforms itself and the bonded structure. By monitoring the electrical current needed to produce that voltage, the ratio of applied voltage to current draw---impedance---can be calculated, which can be thought of as a measure of how a system stores and dissipates energy. When the applied voltage oscillates near a resonant frequency of a structure (the pitch of a rung bell, for example) the structure vibrates much more intensely, and that additional movement dissipates more energy due to viscosity, friction, and transmitting sound into the air. This phenomenon is reflected in the measured impedance, so by calculating the impedance value over a large range of frequencies, it is possible to identify many resonances of the structure. So, the impedance value is tied to the vibrational properties of the structure, and the vibration of the structure is tied to its geometry and material properties. One application of this relationship is called impedance-based structural health monitoring: taking measurements of a structure when it is first built as a reference, then measuring it again later to watch for changes that indicate emerging damage. In this work, the reference measurement is established by measuring a group of control structures that are known to be free of defects. Then, every time a new part is fabricated, its impedance measurements will be compared to the reference. If it matches closely enough, it is assumed good. In both cases, impedance values don't indicate what the change is, just that there was a change. A large portion of this work is devoted to determining the types and sizes of defects that can be reliably detected through INDEAM, what effect the part material plays, and how and where the piezoelectric should be mounted to the part. The remainder of this work discusses new methods for conducting impedance-based evaluation. In particular, overcoming the extra uncertainty introduced by moving from part-to-itself structural health monitoring comparisons to the part-to-part quality control comparisons discussed in this work. A new method for mathematically comparing impedance values is introduced which involves extracting the resonant properties of the structure rather than using statistical tools on the raw impedance values. Additionally, a new method for assessing the influence of piezoelectric mounting conditions on the measured impedance values is demonstrated.

abstract: The main objective of this study is to investigate the mechanical behaviour of cementitious based composites subjected dynamic tensile loading, with effects of strain rate, temperature, addition of short fibres etc. Fabric pullout model and tension stiffening model based on finite difference model, previously developed at Arizona State University were used to help study the bonding mechanism between fibre and matrix, and the phenomenon of tension stiffening due to the addition of fibres and textiles. Uniaxial tension tests were conducted on strain-hardening cement-based composites (SHCC), textile reinforced concrete (TRC) with and without addition of short fibres, at the strain rates ranging from 25 s-1 to 100 s-1. Historical data on quasi-static tests of same materials were used to demonstrate the effects including increases in average tensile strength, strain capacity, work-to-fracture due to high strain rate. Polyvinyl alcohol (PVA), glass, polypropylene were employed as reinforcements of concrete. A state-of-the-art phantom v7 high speed camera was setup to record the video at frame rate of 10,000 fps. Random speckle pattern of texture style was made on the surface of specimens for image analysis. An optical non-contacting deformation measurement technique referred to as digital image correlation (DIC) method was used to conduct the image analysis by means of tracking the displacement field through comparison between the reference image and deformed images. DIC successfully obtained full-filed strain distribution, strain versus time responses, demonstrated the bonding mechanism from perspective of strain field, and corrected the stress-strain responses. / Dissertation/Thesis / M.S. Civil Engineering 2013

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.

Many cases have evinced the importance of having structural health monitoring (SHM) strategies that can allow the detection of the structural health of infrastructures or buildings, in order to prevent the potential economic or human losses. Nanocomposite material like the Carbon nanofiller-modified composites have great potential for SHM because these materials are piezoresistive. So, it is possible to determine the damage status of the material by studying the conductivity change distribution, and this is essential for detecting the damage on the position that can-not be observed by eye, for example, the inner layer in the aerofoil. By now, many researchers have studied how damage influences the conductivity of nanocomposite material and the electrical impedance tomography (EIT) method has been applied widely to detect the damage-induced conductivity changes. However, only knowing how to calculate the conductivity change from damage is not enough to SHM, it is more valuable to SHM to know how to determine the mechanical damage that results in the observed conductivity changes. In this article, we apply the machine learning methods to determine the damage status, more specifically, the number, radius and the center position of broken holes on the material specimens by studying the conductivity change data generated by the EIT method. Our results demonstrate that the machine learning methods can accurately and efficiently detect the damage on material specimens by analysing the conductivity change data, this conclusion is important to the field of the SHM and will speed up the damage detection process for industries like the aviation industry and mechanical engineering.

Ultrasonic guided wave imaging methods offer a cost-effective mechanism to perform in situ structural health monitoring (SHM) of large plate-like structures, such as commercial aircraft skins, ship hulls, storage tanks, and civil structures. However, current limits in imaging quality, environmental sensitivities, and implementation costs, among other things, are preventing widespread commercial adoption. The research presented here significantly advances state of the art guided wave imaging techniques using inexpensive, spatially distributed arrays of piezoelectric transducers. Novel adaptive imaging techniques are combined with in situ estimation and compensation of propagation parameters; e.g., dispersion curves and transducer transfer functions, to reduce sensitivity to unavoidable measurement inaccuracies and significantly improve resolution and reduce artifacts in guided wave images. The techniques can be used not only to detect and locate defects or damage, but also to characterize the type of damage. The improved ability to detect, locate, and now characterize defects or damage using a sparse array of ultrasonic transducers is intended to assist in the establishment of in situ guided wave imaging as a technically and economically viable tool for long-term monitoring of plate-like engineering structures.

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