Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Gall, Ken; Committee Chair: May, Gary S; Committee Member: Brand, Oliver; Committee Member: Degertekin, F Levent; Committee Member: Milor, Linda S. Part of the SMARTech Electronic Thesis and Dissertation Collection.

It is common knowledge that water is one of the basic needs for human beings. However, the consumption of contaminated water can lead to waterborne diseases and fatalities. It is, therefore imperative to constantly monitor the quality of potable water. There are numerous technologies used for water quality monitoring. These technologies are relatively effective however these tests are expensive and complex to use, which then require experienced technicians to operate them. Other tests are not rapid, making consumers of water susceptible to waterborne diseases. In this study, dye-doped, surface functionalized silica nanoparticles (SiNPs) and surface-functionalized magnetic nanocomposites (MNCs) were proposed as materials that can be applied in order to reduce the time taken to get results as well as to make the processes less complex and portable. The aim of this study was to synthesize and characterize surface functionalized dye-doped SiNPs and surface functionalized MNCs for detection and preconcentration of in water. Additionally, proof of concept had to be shown using the synthesized materials. SiNPs were the materials of choice due to their easily functionalized surfaces and their strong optical properties. SiNPs are photostable and they do not leach in solution due to the inert nature of the silica matrix in aqueous media. MNCs were chosen as materials of choice for preconcentration of E. coli in water because they are easy to synthesize and they can be applied in various biological applications due to their functional groups. SiNPs were synthesized using the water-in-oil microemulsion. The SiNPs were further functionalized with amine and carboxyl groups and avidin. Thereafter, they were bioconjugated with biotinylated anti-E. coli antibodies. The pure and surface functionalized SiNPs were characterized using ATR-FTIR spectroscopy, FE-SEM, HR-TEM, Zeta Sizer, UV-vis spectroscopy and spectrofluorometry. The application of the dye—doped surface functionalized SiNPs in E. coli detection was characterized using the fluorescence plate reader. The SiNPs were spherical and uniform in size. They increased in size as they were being functionalized, ranging from 21.20 nm to 75.06 nm. The SiNPs were successfully functionalized with amine and carboxyl groups as well as with avidin and antibodies. Two methods were investigated for carboxyl group attachment (direct and indirect attachment) and the direct attachment method yielded the best results with a surface charge of -31.9 mV compared to -23.3 mV of the indirect method. The dye loading was found to be 1% after particle synthesis. The optical properties of the Ru(Bpy) dye were enhanced 3 fold when they were encapsulated in the Si matrix. The SiNPs were binding to the E. coli cells and enabled detection. MNCs were synthesized through in-situ polymerization. The MNCs were characterized using ATR-FTIR spectroscopy, SEM, TEM and XRD. The MNCs were successfully functionalized with carboxyl groups. The increase in size of the nanocomposites as seen in SEM images proved that the Fe3O4 was successfully encapsulated in the polymer matrix. The MNCs were proven to be magnetic by a simple magnetism test whereby they were separated in an aqueous solution using an external magnetic field. The antibody-labelled MNCs were binding to the E. coli cells as shown in TEM images. E. coli cells were removed from water at varying concentrations of 1x106 CFU/mL to 1x109 CFU/mL at 10 mL volumes. This study has demonstrated that dye-doped SiNPs amplify the signal of E. coli cells using fluorescence. The study has also demonstrated that the MNCs can be applied in sample preconcentration and enrichment for E. coli detection. However, further studies should investigate and optimize the combination of the two techniques in a point of use device for water quality testing of 100 mL-samples as per the requirement of the SANS 241 standard. / Civil and Chemical Engineering / M. Tech. (Chemical Engineering)

abstract: Environmentally responsive hydrogels are one interesting class of soft materials. Due to their remarkable responsiveness to stimuli such as temperature, pH, or light, they have attracted widespread attention in many fields. However, certain functionality of these materials alone is often limited in comparison to other materials such as silicon; thus, there is a need to integrate soft and hard materials for the advancement of environmental-ly responsive materials. Conventional hydrogels lack good mechanical properties and have inherently slow response time, important characteristics which must be improved before the hydrogels can be integrated with silicon. In the present dissertation work, both these important attrib-utes of a temperature responsive hydrogel, poly(N-isopropylacrylamide) (PNIPAAm), were improved by adopting a low temperature polymerization process and adding a sili-cate compound, tetramethyl orthosilicate. Furthermore, the transition temperature was modulated by adjusting the media quality in which the hydrogels were equilibrated, e.g. by adding a co-solvent (methanol) or an anionic surfactant (sodium dodecyl sulfate). In-terestingly, the results revealed that, based on the hydrogels’ porosity, there were appre-ciable differences when the PNIPAAm hydrogels interacted with the media molecules. Next, an adhesion mechanism was developed in order to transfer silicon thin film onto the hydrogel surface. This integration provided a means of mechanical buckling of the thin silicon film due to changes in environmental stimuli (e.g., temperature, pH). We also investigated how novel transfer printing techniques could be used to generate pat-terned deformation of silicon thin film when integrated on a planar hydrogel substrate. Furthermore, we explore multilayer hybrid hydrogel structures formed by the integration of different types of hydrogels that have tunable curvatures under the influence of differ-ent stimuli. Silicon thin film integration on such tunable curvature substrates reveal char-acteristic reversible buckling of the thin film in the presence of multiple stimuli. Finally, different approaches of incorporating visible light response in PNIPAAm are discussed. Specifically, a chemical chromophore- spirobenzopyran was synthesized and integrated through chemical cross-linking into the PNIPAAm hydrogels. Further, methods of improving the light response and mechanical properties were also demonstrat-ed. Interestingly, such a system was shown to have potential application as light modulated topography altering system / Dissertation/Thesis / Doctoral Dissertation Chemical Engineering 2015

Thesis (DTech (Mechanical Engineering))--Cape Peninsula University of Technology, 2015. / This dissertation addresses the development and the testing of a simulation tool to be used to predict the behaviour of smart material/structures. Along with the development of the simulation tool, a new form of the model describing the behaviour of shape-memory alloy was developed and implemented. The proposed model was developed based on the existing cosine model, conventionally used in literature, but it uses hyperbolic tangent functions. The hyperbolic tangent function was chosen so as to allow the simulation of any range of temperatures. Experiments were performed to obtain the parameters to be used in the simulation and to validate the numerical results. Two different simulations were performed: a one dimensional FEA analysis with a two dimensional orientation (NiTi SMA wire simulation) and a three dimensional FEA analysis (NiTi SMA plate) [Msomi and Oliver, 2015]. Alongside the FEA analysis, two experiments were performed with the purpose of obtaining the material parameters to be used in FEA analysis and to compare the FEA results to the experimental results. / Airbus Company

Les matériaux actifs sont des matériaux qui réagissent quand on leur applique un champ extérieur comme la température, la lumière, un champ magnétique ou un champ électrique. Ces champs changent les propriétés du matériau comme la longueur, la susceptibilité magnétique ou la permittivité électrique. Ces changements peuvent être utilisé pour faire du travail. Quelques exemples sont les matériaux piézoélectriques, qui changent de longueur quand on applique un champ électrique, les alliages à mémoire de forme qui changent leur longueur sous l’action de la température. Un matériau plus récent qu’on appelle les alliages mémoire de forme magnétique se de forme sous l’action d’un champ magnétique. Dans cette thèse, on utilise ce matériau pour Confectionner un actionneur. Pour ce faire, on utilise la thermodynamique des procédés irréversibles pour modéliser le matériau. La thermodynamique s’avère très versatile pour ce type de matériau car il permet de quantifier l’ échange et la transformation d’ énergie dans le matériau. Aussi, étant donné que le matériau se comporte d’une façon non-linéaire et hystérique, le cadre énergétique nous permets justement de prendre en compte ces non- linearités. Cette thèse utilise l’approche énergétique notamment les Hamiltonien à ports pour modéliser un actionneur à base d’alliage à mémoire de forme. Cette méthode nous permets aussi de concevoir des lois de commande pour contrôler le matériau. / Active materials are a class of material which react to an external stimulus such as temperature,photons, magnetic field or electric field. These stimuli cause some properties of the material tochange usually their length. Some examples are piezoelectric material which change their lengthunder the action of an electric field, Shape Memory alloys which alter their shape on applicationof heat, and more recently Magnetic Shape Memory Alloys (MSMA) which undergo a deformationon application of a magnetic field. Harnessing this property of MSMAs, we hereby present anactuator using this novel material. We extensively make use of an energy framework, namely thethermodynamics of irreversible processes to model the material. This framework has been provento be very versatile in modelling energy exchange and transformation as it occurs in the materialand also to incorporate hysteresis which arises naturally in such materials. Another advantage of thismethod is its ability to give us constitutive laws based on simple assumptions. Furthermore, usingan energy framework allows us to apply some energy based control. Port Hamiltonian Control is onesuch method and it is not limited only to linear models. This latter characteristic has proven veryuseful since MSMAs are very non-linear in nature.

Porous materials have garnered significant interest within scientific community mainly because of the possibility of engineering their pores for selective applications. Currently, much research has focused on improving the therapeutic indices of the active pharmaceutical ingredients engineered with nanoparticles. The main goal of this dissertation is to prepare targetable and biodegradable silica/organosilica nanoparticles for biomedical applications with a special focus on engineering particle pores. Herein, the design of biodegradable silica-iron oxide hybrid nanovectors with large mesopores for large protein delivery in cancer cells is described. The mesopores of the nanomaterials span 20 to 60 nm in diameter, and post-functionalization allowed the electrostatic immobilization of large proteins (e.g., mTFP-Ferritin, ~534 kDa). The presence of iron oxide nanophases allowed for the rapid biodegradation of the carrier in fetal bovine serum as well as magnetic responsiveness. The nanovectors released large protein cargos in aqueous solution under acidic pH or magnetic stimuli. The delivery of large proteins was then autonomously achieved in cancer cells via the silica-iron oxide nanovectors, which is thus promising for biomedical applications. Next, the influence of competing noncovalent interactions in the pore walls on the biodegradation of organosilica frameworks for drug delivery applications is studied. Enzymatically-degradable azo-bridged organosilica nanoparticles were prepared and then loaded with the anticancer drug doxorubicin (DOX). Controllable drug release was observed only upon the stimuli-mediated degradation of azo-bridged organosilica nanoparticles in the presence of azoreductase enzyme triggers or under hypoxia conditions. These results demonstrated that azo-bridged organosilica nanoparticles are biocompatible, biodegradable drug carriers and that cell specificity can be achieved both in vitro and in vivo. Overall, the results support the importance of studying self-assembly patterns in hybrid frameworks to better engineer the next generation of dynamic or “soft” porous materials.

As the largest consumer of electricity, the buildings sector accounts for about 76% of electricity use and 40% of all U.S. primary energy use and associated greenhouse gas (GHG) emissions. Research shows that a potential energy saving of 34.78% could be achieved by the smart buildings comparing to conventional buildings. Therefore, a smart management of building sectors becomes significantly important to achieve the optimal interior comfort with minimal energy expenditure. The ability of adaptation to the dynamic environments is considered the central aspect in smart building systems, which can be segmented into the passive adaptation and the active adaptation. The passive adaptation refers to the designs that do not change with the dynamic environment but improve the building overall performance by the integration of originally separated components, or by the application of advanced engineering materials. The active adaptation refers to the building management system (BMS) that actively responds and evolves with the changing environment, through the continuous monitoring of the surroundings via the sensor network, and the smart response through the controlling algorithms in the central controlling unit. This Ph.D. dissertation focuses on developing materials and systems for the smart building envelope, including a photovoltaic integrated roof with passive adaptation, and self-powered window systems with active responses environment. As the skin of a building, the building envelope provides the first level resistance towards air, water, heat, light and noise, which makes it the ideal target for the passive adaptation to the environments, as well as the perfect sensing location in the building management system for the active adaptation. This dissertation starts with a discussion of the building integrated photovoltaic thermal (BIPVT) roofing panel, including the fabrication, performance demonstration, and micromechanics-based theoretical modeling. The panel is structurally supported by a functionally graded material (FGM) panel made with high-density polyethylene as the matrix and aluminum particles as reinforcement. It prevents the heat from entering the building and directs the heat to the water tubes embedded inside the panel for the thermal energy harvesting, such that the overall energy efficiency is significantly improved. The design, fabrication and performance of the system is discussed, and an innovative non-destructive analysis method is developed to captures the authentic particle distribution of the FGM. As the main structural component, functionally graded material is comprehensively tested and modeled in elastic, thermoelastic, elastoplastic, and thermo-elastoplastic performance, based on the equivalent inclusion based method. An ensemble average approach was used to convert the particles’ interaction in the microscope to the averaged relation in the macroscope, such that both particle to matrix influence and particle to particle pair-wise interactions are characterized. The idea of the equivalent inclusion method extends to the plastic modeling of the FGM, by formulating an ensemble average form of the matrix stress norm in the macroscale that incorporate the local disturbance of particle reinforcement in the microscale. The accuracy of the proposed algorithm is verified and validated by comparing with another theory in homogeneous composite and experiments, respectively. To the best of the author’s knowledge, no prior theoretical algorithm has been proposed for the elastoplastic modeling of functionally graded materials. Therefore, the proposed algorithm can be used as a foundation and reference for further investigation and industry prediction of graded composites. Based on the theoretical modeling of the mechanical properties, a high order plate theory is also proposed in this dissertation to study for the thermo-mechanical performance of the FGM panel, to provide structural design guideline for the BIPVT panels. The shearing and bending behaviors are decomposed, solved independently, and combined to formulate the final solution. The shear strain components are assumed to follow a parabolic variation across the thickness, while the bending components follow the solution from classical plate theory. Closed-form solutions for the circular panel under different loadings are provided, with verification by comparing to other models and validation to experiments. Two smart window systems are proposed and demonstrated in this dissertation to actively monitor the building environment with active responses, and energy harvesting techniques are investigated to harvest energy from ambient environment the eternal power supply to the system. The thermoelectric powered wireless sensor network (TPWSN) platform is first demonstrated and discussed, where the energy is harvested from the temperature difference across the window frame. The TPWSN sits completely inside the window/façade frame with no compromise of the outlook and continuously monitors the building environment for the optimal control of the building energy consumption and indoor comfort. The energy harvesting technique grants eternal battery lifetime and significantly simplifies the installation and maintenance of the system with considerable saving of time and cost. In addition, the platform provides energy to various types of sensors for different kinds of sensing needs and store the data to the Google cloud for permanent storage and advanced analytics. The thermoelectric powered system works well for the sensors and microcontrollers but fails to provide enough power to the actuators. A novel sun-powered smart window blinds (SPSWB) system is designed, prototyped, and tested in this dissertation with solar energy harvesting on window blinds which provides enough power for the actuators. The thin-film photovoltaic cells are attached on one side of slats for energy harvesting and a PVdF-HFP coating is attached on the other side for the passive cooling. The voltage regulation and battery management systems are designed and tested, where a stable 55% energy efficiency from the PV into the battery has been achieved. The automatic control of the window blinds is accomplished with the help of sensors and a microcontroller. The energy equilibrium analysis is proposed and demonstrated with the local solar data to incorporate the influence of local weather conditions and solar zenith angle, from which we demonstrated that much more power than needed can be harvested. The abundant energy harvested validates the feasibility and the robustness of the system and proves its wide application potentials to various sensors and applications. In conclusion, both passive and active adaptations to the environment are investigated to build up the next generation of smart building envelope systems. The building integrated photovoltaic thermal roof is designed, fabricated, tested, and modeled in detail, which provides structural support to the external loads and improves the energy efficiency of buildings. The smart window/façade systems serve as a platform for various sensors and actuators via the energy harvesting from the ambient environment, and could significantly improve the energy expenditure with minimal impact of internal comfort.

<div>Sensing of mechanical state is critical in diverse fields including biomedical implants, intelligent robotics, consumer technology interfaces, and integrated structural health monitoring among many others. Recently, materials that are self-sensing via the piezoresistive effect (i.e. having deformation-dependent electrical conductivity) have received much attention due to their potential to enable intrinsic, material-level strain sensing with lesser dependence on external/ad hoc sensor arrays. In order to effectively use piezoresistive materials for strain-sensing, however, it is necessary to understand the deformation-resistivity change relationship. To that end, many studies have been conducted to model the piezoresistive effect, particularly in nanocomposites which have been modified with high aspect-ratio carbonaceous fillers such as carbon nanotubes or carbon nanofibers. However, prevailing piezoresistivity models have important limitations such as being limited to microscales and therefore being computationally prohibitive for macroscale analyses, considering only simple deformations, and having limited accuracy. These are important issues because small errors or delays due to these challenges can substantially mitigate the effectiveness of strain-sensing via piezoresistivity. Therefore, the first objective of this thesis is to develop a conceptual framework for a piezoresistive tensorial relation that is amenable to arbitrary deformation, macroscale analyses, and a wide range of piezoresistive material systems. This was achieved by postulating a general higher-order resistivity-strain relation and fitting the general model to experimental data for carbon nanofiber-modified epoxy (as a representative piezoresistive material with non-linear resistivity-strain relations) through the determination of piezoresistive constants. Lastly, the proposed relation was validated experimentally against discrete resistance changes collected over a complex shape and spatially distributed resistivity changes imaged via electrical impedance tomography (EIT) with very good correspondence. Because of the generality of the proposed higher-order tensorial relation, it can be applied to a wide variety of material systems (e.g. piezoresistive polymers, cementitious, and ceramic composites) thereby lending significant potential for broader impacts to this work. </div><div><br></div><div>Despite the expansive body of work on direct current (DC) transport, DC-based methods have important limitations which can be overcome via alternating current (AC)-based self-sensing. Unfortunately, comparatively little work has been done on AC transport-deformation modeling in self-sensing materials. Therefore, the second objective of this thesis is to establish a conceptual framework for the macroscale modeling of AC conductivity-strain coupling in piezoresistive materials. For this, the universal dielectric response (UDR) as described by Joncsher's power law for AC conductivity was fit to AC conductivity versus strain data for CNF/epoxy (again serving as a representative self-sensing material). It was found that this power law does indeed accurately describe deformation-dependent AC conductivity and power-law fitting constants are non-linear in both normal and shear strain. Curiously, a piezoresistive switching behavior was also observed during this testing. That is, positive piezoresistivity (i.e. decreasing AC conductivity with increasing tensile strain) was observed at low frequencies and negative piezoresistivity (i.e. increasing AC conductivity with increasing tensile strain) was observed at high frequencies. Consequently, there exists a point of zero piezoresistivity (i.e. frequency at which AC conductivity does not change with deformation) between these behaviors. Via microscale computational modeling, it was discovered that changing inter-filler tunneling resistance acting in parallel with inter-filler capacitance is the physical mechanism of this switching behavior.</div>

Biological organisms, organs and tissues have evolved through natural selection diverse functional and structural traits to accomplish complex tasks. For example, small insects with tiny thermal capacitance have developed tailored spectral properties and behavioral tactics to mitigate rapid changes of body temperatures caused by environmental electromagnetic radiations; neural networks in the brain, through changing the efficacy of synapses, can recognize hidden patterns and correlations in raw data, cluster and classify them, and continuously learn and improve over time. These biological systems are a rich source of bio-inspiration for developing solutions to address engineering challenges. My thesis work focuses on the intersection between photonics and biology and explores three unique biological systems and their technological implications. Beginning with the investigation of butterfly wings, we observed that the wings contain a matrix of living structures, including mechanical and thermal sensory neural cells, hemocytes, pheromone producing organs, , and even “wing hearts”, and that these living structures carry out their specific functions over the entire life span of butterflies but are vulnerable to sustained high temperatures. We discovered that butterflies have evolved heterogeneously thickened wing cuticles and special nanostructured wing scales to locally enhance thermal emissivity so that the regions of the wings containing living structures can better dissipate heat through thermal radiation. Furthermore, we discovered that butterfly wings almost always possess enhanced reflectivity in the near-infrared, which can significantly reduce heating caused by solar radiation. This enhanced near-infrared reflectivity is found to originate from optical scattering at the porous wing scales, especially pale-colored scales underneath the surface layer of colorful ones. Besides these structural adaptations, our bioassays showed that butterflies utilize a number of behavioral strategies to avoid overheating or overcooling of their wings. We found that butterflies can use their wings as a fast and sensitive temperature monitor to detect the direction and strength of sunlight or artificial light applied onto the wings; as such, they can adapt the most suitable postures to minimize overheating of the wings if the illumination is too strong and to warm up the wings when ambident temperatures are insufficient for taking flight. Drawing inspiration from the multi-layered wing scales, which impart coloration to the wings while maintaining their high near-infrared reflectivity, we developed a double-layered, radiative-cooling coating that is able to minimize solar heating while still stay colorful. The second part of my thesis work explored nanostructured fibers and textiles as a novel solution for radiative cooling. The work was motivated by our discovery that the silk fibers produced by the caterpillars of the Madagascan moon moth (Argema mittrei) contain a high density of filamentary air voids, which enable individual fibers of the moth to strongly reflect light over the solar spectrum. This, in combination with natural polymers’ intrinsic high mid-infrared emissivity, provides the cocoons of the moth with remarkable passive radiative-cooling properties. We developed fabrication platforms to produce synthetic fibers with filamentary air voids by modifying both wet spinning and melt extrusion techniques. The melt extrusion approach, in particular, is implemented in an industry-scale fiber extrusion machine for high-throughput, high-yield production. The fabricated nanostructured fibers reproduce the prominent solar reflectivity of the Madagascan moon moth silk fibers and possess high emissivity due to the variety of chemical bonds in the synthetic polymers used. The melt-extruded fibers were twisted into yarns, which were subsequently woven and knitted into fabrics. The finished fabric samples were demonstrated to perform as effective radiative cooling devices compared to conventional white fabrics. Lastly, inspired by how neural networks in the brain form the basis of learning and motivated by how artificial neural networks are implemented in computers, we develop a novel platform of optical neural computing, a smart glass, for object recognition. Our optical neural network takes advantage of strong light-matter interactions with sub-wavelength resolutions in metasurfaces to emulate the layered computations in a biological or artificial neural network. In the simplest implementation of a single-layer smart glass, a metasurface was trained to provide 2D phase modulations that can transform the complex optical wave scattered from an input object into a characteristic intensity distribution pattern on the output plane corresponding to the identity of the object. We experimentally demonstrated the recognition of handwritten numerical digits and letters with different fonts with high accuracies using the smart glass and explored the capability of a polarization-multiplexing smart glass based on birefringent metasurfaces for performing distinct recognition tasks at orthogonal incident polarizations. This optical neural computing platform represents a new paradigm of computation operating at the speed of light with no power consumption and this physical-wave-based computation guarantees data security beyond digital encryption.

<div>Structural health monitoring (SHM) has immense potential to improve the safety of aerospace, mechanical, and civil structures because it allows for continuous, real-time damage prognostication. However, conventional SHM methodologies are limited by factors such as the need for extensive external sensor arrays, inadequate sensitivity to small-sized damage, and poor spatial damage localization. As such, widespread implementation of SHM in engineering structures has been severely restricted. These limitations can be overcome through the use of multi-functional materials with intrinsic self-sensing capabilities. In this area, composite materials with nanofiller-modified polymer matrices have received considerable research interest. The electrical conductivity of these materials is affected by mechanical stimuli such as strain and damage. This is known as the piezoresistive effect and it has been leveraged extensively for SHM in self-sensing materials. However, prevailing conductivity-based SHM modalities suffer from two critical limitations. The first limitation is that the mechanical state of the structure must be indirectly inferred from conductivity changes. Since conductivity is not a structurally relevant property, it would be much more beneficial to know the displacements, strains, and stresses as these can be used to predict the onset of damage and failure. The second limitation is that the precise shape and size of damage cannot be accurately determined from conductivity changes. From a SHM point of view, knowing the precise shape and size of damage would greatly aid in-service inspection and nondestructive evaluation (NDE) of safety-critical structures. The underlying cause of these limitations is that recovering precise mechanics from conductivity presents an under determined and multi-modal inverse problem. Therefore, commonly used inversion schemes such as gradient-based optimization methods fail to produce physically meaningful solutions. Instead, metaheuristic search algorithms must be used in conjunction with physics-based damage models and realistic constraints on the solution search space. To that end, the overarching goal of this research is to address the limitations of conductivity-based SHM by developing metaheuristic algorithm-enabled methodologies for recovering precise mechanics from conductivity changes in self-sensing composites.</div><div><div><br></div><div>Three major scholarly contributions are made in this thesis. First, a piezoresistive inversion methodology is developed for recovering displacements, strains, and stresses in an elastically deformed self-sensing composite based on observed conductivity changes. For this, a genetic algorithm (GA) is integrated with an analytical piezoresistivity model and physics-based constraints on the search space. Using a simple stress based failure criterion, it is demonstrated that this approach can be used to accurately predict material failure. Second, the feasibility of using other widely used metaheuristic algorithms for piezoresistive inversion is explored. Specifically, simulated annealing (SA) and particle swarm optimization (PSO) are used and their performances are compared to the performance of the GA. It is concluded that while SA and PSO can certainly be used to solve the piezoresistive inversion problem, the GA is the best algorithm based on solution accuracy, consistency, and efficiency. Third, a novel methodology is developed for precisely determining damage shape and size from observed conductivity changes in self-sensing composites. For this, a GA is integrated with physics-based geometric models for damage and suitable constraints on the search space. By considering two specific damage modes —through-holes and delaminations —it is shown that this method can be used to precisely reconstruct the shape and size of damage. </div><div><br></div><div>In achieving these goals, this thesis advances the state of the art by addressing critical limitations of conductivity-based SHM. The methodologies developed herein can enable unprecedented NDE capabilities by providing real-time information about the precise mechanical state (displacements, strains, and stresses) and damage shape in self-sensing composites. This has incredible potential to improve the safety of structures in a myriad of engineering venues.</div></div>