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.

<div>Self-sensing materials are engineered to transduce mechanical effects like deformations and damages into detectable electrical changes. As such, they have received immense research attention in areas including aerospace, civil infrastructure, robotic skin, and biomedical devices. In structural health monitoring (SHM) and nondestructive evaluation (NDE) applications, damages in the material cause breakage in the conductive filler networks, resulting in changes in the material's conductivity. Most SHM and NDE applications of self-sensing materials have used direct current (DC) measurements. DC-based methods have shown advantages with regard to sensitivity to microscale damages compared to other SHM methods. Comparatively, alternating current (AC) measurement techniques have shown potential for improvement over existent DC methods. For example, using AC in conjunction with self-sensing materials has potential for benefits such as greater data density, higher sensitivity through electrodynamics effects (e.g., coupling the material with resonant circuitry), and lower power requirements. Despite these potential advantages, AC techniques have been vastly understudied compared to DC techniques. </div><div><br></div><div>To overcome this gap in the state of the art, this thesis presents two contributions: First, an experimental study is conducted to elucidate the effect of different damage types, numbers, and sizes on AC transport in a representative self-sensing composite. And second, experimental data is used to inform a computational study on using AC methods to improve damage detection via electrical impedance tomography (EIT) – a conductivity imaging modality commonly paired with self-sensing materials for damage localization. For the first contribution, uniaxial glass fiber specimens containing 0.75 wt.% of carbon nanofiber (CNF) are induced with five types of damage (varying the number of holes, size of holes, number of notches, size of notches, and number of impacts). Impedance magnitude and phase angle were measured after each permutation of damage to study the effect of the new damage on AC transport. It was observed that permutations of hole and notch damages show clear trends of increasing impedance magnitude with the increasing damage, particularly at low frequencies. These damages had little-to-no effect on phase angle, however. Increasing numbers of impacts on the specimens did not show any discernable trend in either impedance magnitude or phase angle, except at high frequencies. This shows that different AC frequencies can be more or less useful for finding particular damage types.</div><div><br></div><div>Regarding the second contribution, AC methods were also explored to improve damage detection in self-sensing materials via EIT. More specifically, the EIT technique could benefit from developing a baseline-free (i.e., not requiring a ‘healthy’ reference) formulations enabled by frequency-difference (fd) imaging. For this, AC conductivity measurements ranging from 100 Hz to 10 MHz were collected from various weight fractions of CNF-modified glass fiber/epoxy laminates. This experimental data was used to inform fdEIT simulations. In the fdEIT simulations, damage was simulated as a simple through-hole. Simulations used 16 electrodes with four equally spaced electrodes on each side of the domain. The EIT forward problem was used to predict voltage-current response on the damaged mesh, and a fdEIT inverse problem was formulated to reconstructs the damage state on an undamaged mesh. The reconstruction images showed the simulated damage clearly. Based on this preliminary study, this research shows that fdEIT does have potential to eliminate the need for a healthy baseline in NDE applications, which can potentially help proliferate the use of this technique in practice.</div>

<p dir="ltr">The demand and utilization of fiber-reinforced composites are increasing in various sectors, including aerospace, civil engineering, and automotive industries. Non-destructive methods are necessary for monitoring fiber-reinforced composites due to their complex and often visually undetectable failure modes. An emerging method for monitoring composite structures is through the integration of self-sensing capabilities. Self-sensing in nanocomposites can be achieved through nanofiller modifications, which involve introducing an adequate amount of nanofillers into the matrix, such as carbon nanotubes (CNTs) and carbon nanofillers (CNFs). These fillers form an electrically well-connected network that allows the electrical current to travel through conductive pathways. The disruption of connectivity of these pathways, caused by mechanical deformations or damages, results in a change in the overall conductivity of the material, thereby enabling intrinsic self-sensing.</p><p dir="ltr">Currently, the majority of predictive modeling attempts in the field of self-sensing nanocomposites have been dedicated to microscale piezoresistivity. There has been a lack of research conducted on the modeling of strain-induced resistivity changes in macroscale fiber-matrix material systems. As a matter of fact, no analytical macroscale model that addresses the impact of continuous fiber reinforcement in nanocomposites has been presented in the literature. This gap is significant because it is impossible to make meaningful structural condition predictions without models relating observed resistivity changes to the mechanical condition of the composite. Accordingly, this dissertation presents a set of three research contributions. The overall objective of these contributions is to address this knowledge gap by developing and validating an analytical model. In addition to advancing our theoretical understanding, this model provides a practical methodology for predicting the piezoresistive properties of continuous fiber-reinforced composites with integrated nanofillers.</p><p dir="ltr">To bridge the above-mentioned research gap, three scholarly contributions are presented in this dissertation. The first contribution proposes an analytical model that aims to predict the variations in resistivity within a material system comprising a nanofiller-modified polymer and continuous fiber reinforcement, specifically in response to axial strain. The fundamental principle underlying our methodology involves the novel use of the concentric cylindrical assembly (CCA) homogenization technique to model piezoresistivity. The initial step involves the establishment of a domain consisting of concentric cylinders that represent a continuous reinforcing fiber phase wrapped around by a nanofiller-modified matrix phase. Subsequently, the system undergoes homogenization to facilitate the prediction of changes in the axial and transverse resistivity of the concentric cylinder as a consequence of longitudinal deformations. The second contribution investigates the effect of radial deformations on piezoresistivity. Here, we demonstrate yet another novel application of the CCA homogenization technique to determine piezoresistivity. This contribution concludes by presenting closed-form analytical relations that describe changes in axial and transverse resistivity as functions of externally applied radial strain. The third contribution involves computationally analyzing piezoresistivity in fiber-reinforced laminae by using three-dimensional representative volume elements (RVE) with a CNF/epoxy matrix. By comparing the single-fiber-based analytical model with the computational model, we can investigate the impact of interactions between multiple adjacent fibers on the piezoresistive properties of the material. The study revealed that the differences between the single-fiber CCA analytical model and the computational model are quite small, particularly for composites with low- to moderate-fiber volume fractions that undergo relatively minor deformations. This means that the analytical methods herein derived can be used to make accurate predictions without resorting to much more laborious computational methods.</p><p dir="ltr">In summary, the impact of this dissertation work lies in the development of novel analytical closed-form nonlinear piezoresistive relations. These relations relate the electrical conductivity/resistivity changes induced by axial or lateral mechanical deformations in directions parallel and perpendicular to the reinforcing continuous fibers within fiber-reinforced nanocomposites and are validated against in-depth computational analyses. Therefore, these models provide an important and first-ever bridge between simply observing electrical changes in a self-sensing fiber-reinforced composite and relating such observations to the mechanical state of the material.</p>

<div>Laser-to-composite interactions are becoming increasingly common in diverse applications such as diagnostics, fabrication and machining, and weapons systems. Lasers are capable of not only performing non-contact diagnostics, but also inducing seemingly imperceptible structural damage to materials. In safety-critical venues like aerospace, automotive, and civil infrastructure where composites are playing an increasingly prominent role, it is desirable to have means of sensing laser exposure on a composite material. Self-sensing materials may be a powerful method of addressing this need. Herein, we present an exploratory study on the potential of using changes in electrical measurements as a way of detecting laser exposure to a carbon nanofiber (CNF)-modified glass fiber/epoxy laminate. CNFs were dispersed in liquid epoxy resin prior to laminate fabrication via hand layup. The dispersed CNFs form a three-dimensional conductive network which allows for electrical measurements to be taken from the traditionally insulating glass fiber/epoxy material system. It is expected that damage to the network will disrupt the electrical pathways, thereby causing the material to exhibit slightly higher resistance. To test laser sensing capabilities, a resistance baseline of the CNF-modified glass fiber/epoxy specimens was first established before laser exposure. These specimens were then exposed to an infra-red laser operating at 1064 nm, 35 kHz, and pulse duration of 8 ns. The specimens were irradiated for a total of 20 seconds (4 exposures each at 5 seconds). The resistances of the specimens were then measured again post-ablation. In this study, it was found that for 1.0 wt.% CNF by weight the average resistance increased by about 18 percent. However, this values varied for specimens with different weight fractions. This established that the laser was indeed causing damage to the specimen sufficient to evoke a change in electrical properties. In order to expand on this result, electrical impedance tomography (EIT) was employed for localization of laser exposures of 1, 3, and 5 seconds on a larger specimen, a 3.25” square plate. EIT was used to measure the changes in conductivity after each exposure. EIT was not only successful in detecting damage that was virtually imperceptible to the human-eye, but it also accurately localized the exposure sites. The post-ablation conductivity of the exposure sites decreased in a manner that was comparable to the resistance increase obtained during prior testing. Based on this preliminary study, this research could lead to the development of a real-time exposure detection and tracking system for the measurement, fabrication, and defense industries.</div>