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La presencia inadvertida de daños en elementos estructurales representa un aspecto crítico en la seguridad de los mismos. Tales fallas pueden causar irregularidades de funcionamiento e incluso conducir al colapso catastrófico. Por ese motivo, la detección de daños en forma temprana es de fundamental importancia. Un tipo de falla que puede pasar peligrosamente inadvertida es aquélla producida por fatiga, ya que resulta muy difícil de detectar a simple vista. Si bien existen técnicas adecuadas para la detección localizada, la aplicación de las mismas puede resultar impráctica en base a la dificultad en la revisión de estructuras complejas, que inclusive pueden pre-sentar sectores inaccesibles. En consecuencia, se han investi-gado otro tipo de procedimientos de carácter más global, entre los que ha adquirido gran importancia la identificación basada en la respuesta dinámica. Esencialmente, este método se basa en la comparación entre valores teóricos y experimenta-les de parámetros dinámicos de estructuras dañadas. Las incógnitas del mencionado problema corresponden a las magni-tudes asociadas al daño (localización, intensidad). Por lo tan-to, a los valores más próximos a los reales les corresponderá una mínima desviación entre los valores teóricos y experimen-tales. Este tipo de técnica inversa hace uso de un modelo teó-rico de la estructura dañada. En esta tesis se presenta un nuevo modelo unidimensional que permite reproducir el comportamiento estructural de vigas de pared delgada con presencia de fisuras debidas a fatiga. En dicho modelo, se admite la posibilidad de considerar el efecto no lineal de cerra-miento parcial de fisura o breathing. El modelo asocia un enfoque de daño estructural por fisura basado en conceptos de mecánica de fracturas, que se desarrolla íntegramente en este trabajo, con una teoría de vigas de pared delgada recien-temente desarrollada para el caso de vigas intactas. Esta teo-ría debe ser extendida para considerar la presencia de fisuras. La premisa global de la teoría es que la presencia de una fisu-ra genera una reducción localizada en la rigidez de la viga. El modelo unidimensional de viga de pared delgada dañada es aplicable a vigas construidas con materiales isótropos, ortótro-pos y compuestos con laminación cross-ply simétrica o espe-cialmente ortótropa. Considera además flexibilidad por corte debido a flexión y alabeo. Los parámetros asociados a las fisuras son identificados mediante la minimización de una fun-ción objetivo, que se define en términos de diferencias norma-lizadas entre valores de indicadores de fisura obtenidos experi-mentalmente y calculados con el modelo viga. Como indica-dores de daño estructural se consideran los desplazamientos producidos por excitación forzada y también las frecuencias de vibración natural. En orden de resolver el problema inverso, se emplea el algoritmo de optimización Evolución Diferencial. / The unnoticed presence of damage in structural elements represents a critical issue in their security. Such flaws may generate malfunctions and even leading to catastrophic collapse. Thus, early detection of damage represents a topic of fundamental importance. A kind of flaw that can be dange-rously unnoticed is that one produced by fatigue, in terms of the difficulty to detect it with the naked eye. Although there are adequate techniques for local detection, their application may result impractical based on the difficulty represented by complex structures, which can have inaccessible parts. Consequently, other procedures of more global character have been topic of research. Among them, identification ba-sed on dynamic response has acquired major importance. Essentially, this method is based on comparisons among theo-retical and experimental values of dynamic parameters related to damaged structures. The unknowns of the mentioned problem correspond to the magnitudes associated to damage (location, severity). Thus, a minimal deviation among theore-tical and experimental results will correspond to the values closer to real ones. This kind of inverse technique makes use of a theoretical model of the damaged structure. In this the-sis, a new one-dimensional model is presented, which allows replicating the structural behavior of thin-walled beams with the presence of cracks generated by fatigue. In this model, the possibility of considering the nonlinear effect of crack partial closure or breathing is allowed. The model associates a structural damage approach based on fracture mechanics concepts, developed entirely in this work, with a recently developed thin-walled beam theory for the case of intact beams. This theory must be extended in order to consider the presence of cracks. The global premise of the theory is that the presence of a crack generates a localized reduction on the beam stiffness. The one-dimensional thin-walled da-maged beam model is applicable to beams made of isotropic material beams, orthotropic materials and composite materials with symmetric cross-ply or specially orthotropic stacking sequences. In addition, shear flexibility debt to flexure and warping is considered. Damage parameters associated to cracks are identified by means of the minimization of a target function, defined in terms of normalized differences among values of damage indicators obtained experimentally and calculated with the beam model. As indicators of structural damage, displacements debt to forced excitation and also frequencies of natural vibration are considered. In order to solve the inverse problem, the optimization algorithm Differential Evolution is employed.

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.

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.

Real-time structural health monitoring is a well established tool for post-earthquake damage estimation. A key component in the monitoring campaign is the approach used for processing the data from the structural health monitoring system. There is a large body of literature on signal processing approaches aimed at identifying ground-motion induced damage in civil engineering structures. This dissertation expands on a specific subgroup of processing approaches dealing with the identification of damage induced high-frequency transients in the monitoring data. The underlying intuition guiding the current research can be formulated in the following hypothesis - the time difference between the occurrence of a high-frequency transient and the closest deformation extremum forward in time is proportional to the degree of damage. A mathematical deduction is provided in support of the above hypothesis followed by a set of shaking table tests. For the purposes of this research two shaking table tests of reinforced concrete bridge piers were performed. Data from a shaking table test performed by another research group was also analyzed. The cases in which the proposed procedure could find a practical application are examined along with the present limitations.

Recent years have seen increasing research interest in structural health monitoring (SHM). Among the many advances in SHM research, “smart” wireless sensors capable of embedded computing and wireless communication have been highly attractive. Wireless communication in SHM systems was originally proposed to significantly reduce the monetary and time cost for installing lengthy cables in an SHM system. Besides wireless sensing, the next revolution in sensor networks has been predicted to be mobile sensor networks that implant mobility into traditional wireless sensor networks. This research explores decentralized structural model updating and damage detection using mobile and wireless sensors. In the first stage of this research, mobile sensing nodes (MSNs) are developed for SHM purposes. The MSNs can maneuver upon structures built with ferromagnetic/steel materials, conduct measurement, and communicate with pears or remote servers wirelessly. The performance of the MSNs is validated through laboratory and field experiments. To further investigate the mobile sensing strategy, a decentralized structural damage detection procedure is proposed herein for the MSNs using transmissibility functions. Laboratory experiments are conducted on a steel portal frame where various structure damage scenarios are emulated. Besides experiments with MSNs, this study also investigates the nature of transmissibility functions for damage detection in an analytical manner based on a general multi-DOF spring-mass-damper system. Finally, this research also explores substructure model updating through minimization of modal dynamic residuals, which can best benefit from dense mobile or wireless sensor data concentrated in one area. Craig-Bampton transform is adopted to condense the structural model, and minimization of the modal dynamic residuals is determined as the optimization objective. An iterative linearization procedure is adopted for efficiently solving the optimization problem. The presented substructure updating method is validated through a few numerical examples. For comparison, a conventional approach minimizing modal property differences is also applied, and shows worse updating accuracy than the proposed approach. The performance of the proposed substructure model updating approach is further investigated on the effects of substructure location and size.