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

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

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

Structural Health Monitoring (SHM) is the technology of monitoring and assessing the condition of aerospace, civil, and mechanical infrastructures using a sensing system integrated into the structure. Among variety of SHM approaches, impedance-based method is efficient for local damage detection. This thesis focuses on system level concerns for impedance-based SHM. Two essential requirements are reached in the thesis: reduction of power consumption of wireless SHM sensor, and compensation of temperature dependency on impedance. The proposed design minimizes power by employing on-board signal processing, and by eliminating power hungry components such as ADC and DAC. The prototype implemented with MSP430 micro controller is verified to be able to handle SHM operation and wireless communication with extremely low-power: 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it can run for about 2.5 years with two AAA-size batteries. To compensate for temperature change, we proposed an algorithm to select a small subset of baseline profiles for some critical temperatures and to estimate the baseline profile for a given ambient temperature through interpolation. Experimental results show that our method reduces the number of baseline profiles to be stored by 45%, and estimates the baseline profile of a given temperature accurately. / Master of Science

Smart infrastructure has become increasingly prevalent in recent decades due to the emergence of sophisticated and affordable sensing technologies. As sensors are deployed more widely and higher sampling rates are feasible, managing the massive scale of real-time data collected by these systems has become fundamental to providing relevant and timely information to decision-makers. To address this task, a novel open-source framework has been developed to manage and intuitively present high-volume data in near real-time. This design is centered around the goals of making data accessible, supporting decision-making, and providing flexibility to modify and reuse this framework in the future. In this work, the framework is tailored to vibration-based structural health monitoring, which can be used in near real-time to screen building condition. To promote timely intervention, distributed computing technologies are employed to accelerate the processing, storage, and visualization of data. Vibration data is processed in parallel using a publish-subscribe messaging queue and then inserted into a NoSQL database that stores heterogeneous data across several nodes. A REST-based web application allows interaction with this stored data via customizable visualization interfaces. To illustrate the utility of this framework design, it has been implemented to support a frequency domain monitoring dashboard for a 5-story classroom building instrumented with 224 accelerometers. A simulated scenario is presented to capture how the dashboard can aid decisions about occupant safety and structural maintenance. / Master of Science / Advances in technology have made it affordable and accessible to collect information about the world around us using sensors. When sensors are used to aid decision-making about structures, it is frequently referred to as Structural Health Monitoring (SHM). SHM can be used to monitor long-term structural health, inform maintenance decisions, and rapidly screen structural conditions following extreme events. Accelerometers can be used in SHM to capture vibration data that give insight into deflection patterns and natural frequencies in a structure. The challenge with vibration-based SHM and many other applications that leverage sensors is that the amount of data collected has the potential to grow to massive scales. To communicate relevant information to decision-makers, data must be processed quickly and presented intuitively. To facilitate this process, a novel open-source framework was developed for processing, storing, and visualizing high-volume data in near real-time. This framework combines multiple computers to extend the processing and storage capacity of our system. Data is processed in parallel and stored in a database that supports efficient data retrieval. A web application enables interaction with stored data via customizable visualization interfaces. To demonstrate the framework functionality, it was implemented in a 5-story classroom building instrumented with 224 accelerometers. A frequency-domain dashboard was developed for the building, and a simulated scenario was conducted to capture how the dashboard can aid decisions about occupant safety and structural maintenance.

A self-sensing and self-healing bolted joint has been developed. This concept encompasses the areas of health monitoring, joint dynamics and smart materials. In order to detect looseness in a joint the impedance health monitoring method is used. A new method of making impedance measurements for health monitoring that greatly reduces the equipment cost and equipment size was developed. This facilitates implementation of the impedance technique in real-life field applications. Several proof of concept experiments are presented and compared to the traditional method of making impedance measurements. Investigations of bolted joint dynamics were conducted. A literature review of bolted joints and their diagnostics is presented. The application of the transfer impedance method is compared to standard modal tests on various bolt tensions. An investigation of damping in bolted joints was also made comparing a bolted and monolithic beam. Practical issues in adaptive bolted joints are investigated. This includes issues on activating/heating SMA actuators, connecting the actuators to the power source, size selection of SMA actuators and insulations. These issues are examined both experimentally and theoretically. / Master of Science

Condition assessment of civil engineering infrastructure systems is of growing importance as they face aging and degradation due to both human-made activities and environmental factors. Nevertheless, challenges persist in data collection, leading to "data scarcity", and the need for frequent site visits in inspections, presenting significant obstacles in the assessment of the civil infrastructure systems. This dissertation aims to overcome these challenges by exploring the potential of two emerging technologies: Generative Artificial Intelligence (AI) and Virtual Reality (VR). In tackling the issue of data scarcity, the research question revolves around how Generative AI can be utilized to mitigate data collection-related constraints and increase data availability, thus facilitating health monitoring applications of infrastructure systems. For that, using various Generative AI models, the dissertation works on acceleration response data generation, including data augmentation and domain translation applications on different structures. In addressing the site visit challenge, the dissertation focuses on the use of VR to bring the infrastructure to the experts in a single collaborative immersive environment and investigate its impact on decision-making in inspections. For that, using VR technology, the dissertation develops a Virtual Meeting Environment (VME) integrated with the infrastructure data and models presented through novel immersive visualization techniques. The dissertation further investigates the impact of VME on decision-making in infrastructure inspections through experimentation with engineers. These investigations of the use of Generative AI and VR demonstrate various contributions. Generative AI effectively tackles the need for vast datasets in data-intensive damage detection applications. It also demonstrates its potential in estimating representative response data for various structural conditions across dissimilar infrastructures. VME, on the other hand, offers an increased understanding of the material along with a safer, practical, and cost-effective complementing alternative to traditional in-person site visits. It further reveals how VME improves decision-making in infrastructure inspections.

Yes / Self-sensing concrete used in coating form for structural health monitoring of concrete structures has the merits of cost-effectiveness, offering protective effect on structural components, enabling electrical measurements unaffected by steel reinforcement and is also convenient to maintain and replace. This paper investigates the feasibility of using self-sensing cement mortar coating containing carbon nanotube-nano carbon black (CNT-NCB) composite fillers (CNCFs) for damage monitoring of concrete beams. The self-sensing cement mortar coated to concrete beams demonstrated outstanding electrical conductivity (resistivity ranging from 18 to 85 Ω·cm). Under monotonic flexural loadings, self-sensing cement mortar coating with 1.8 vol.% CNCFs featured sensitive self-sensing performance in terms of capturing the initiation of vertical cracks at pure bending span of concrete beams, with fractional change in resistivity (FCR) reaching up to 60.6%. Moreover, FCR variations of self-sensing cement mortar coating exhibited good synchronization and stability with the variation of mid-span deflections of concrete beams during cyclic flexural loadings irrespective of the contents of CNCFs and cyclic amplitudes. Remarkably, it was found that FCR of cement mortar coating basically showed a progressive upward tendency, representing irreversible increase in the resistance during cyclic loading. The irreversible residual FCR indicated the crack occurrence and damage accumulation of concrete beams. / National Science Foundation of China (52368031, 51978127 and 52178188) and the China Postdoctoral Science Foundation (2022M710973)