abstract: All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2016

Orientador: Vicente Lopes Junior / Co-orientador: Michael John Brennan / Banca: Fabricio Cesar Lobato de Almeida / Banca: José Roberto de França Arruda / Resumo: Pesquisas em propagação de ondas para aplicação de monitoramento de integridade estrutural (SHM) tem tido um incremento considerável recentemente. Este procedimento permite detectar danos nas fases iniciais. Esta dissertação descreve um estudo teórico de propagação de ondas para o propósito de detecção e quantificação de dano em uma viga. De particular interesse é a maneira que as ondas interagem com o dano, considerado simétrico com respeito ao eixo neutro. Uma análise de uma estrutura unidimensional de ondas guiadas incorporando o atuador e sensores piezelétricos em configuração pitch-catch e pulse eco é apresentada. O modelo é desenvolvido no domínio da frequência e posteriormente transformado no domínio do tempo através da transformada de Fourier inversa. Isto permite que o efeito do dano entre o atuador e o sensor seja estudado no domínio do tempo e da frequência. Os comprimentos do atuador e do sensor e a profundidade do dano são estudados em uma viga de alumínio delgada. Mostra-se que uma abordagem no domínio do tempo é preferível em relação a abordagem no domínio da frequência para detecção e quantificação de danos na estrutura. Os resultados mostraram que ondas longitudinais são mais sensíveis a variação da espessura para um sistema simétrico e é melhor medir ondas refletidas que as transmitidas. Além disto, verificou-se que devido à natureza dispersiva das ondas de flexão é possível que em algumas situações a amplitude da onda refletida seja diminuída em vez de aumentar quando a espessura da viga é reduzida / Abstract: Wave propagation research for Structural Health Monitoring (SHM) has been increasing recently. It allows the detection of damage at its early stages of development. This dissertation describes a theoretical study of wave propagation for the purpose of detection and quantification of damage in a beam structure. Of particular interest is the way in which waves interact with damage that is symmetrical with respect to the neutral axis. An analysis of a one-dimensional structural waveguide incorporating a piezoelectric actuator and sensors in a pitch-catch and pulse-echo configuration is presented. The model is developed in the frequency domain, which is then transformed into the time domain using the inverse Fourier transform. This enables the effect of damage on wave propagation between the actuator and the sensor to be investigated in both the time and the frequency domains. The size of the actuator and the sensor, and the size of damage are investigated for a thin aluminum beam. It is shown that the time-domain approach is preferable to a frequency domain approach for damage detection in this kind of structure. It is found that longitudinal waves are more sensitive to a change in thickness for a symmetrical system and it is better to measure reflected rather than transmitted waves. Further, it is found that due to the dispersive nature of bending waves, it is possible in some situations for the reflected wave amplitude to decrease rather than increase as the beam thickness is reduced / Mestre

Orientador: Vicente Lopes Júnior / Co-orientador: Marcelo Ornaghi Orlandi / Banca: Michael John Brennan / Banca: Carlos de Marqui Junior / Resumo: Com o objetivo de assegurar a integridade estrutural de estruturas aeronáuticas, diversas técnicas de monitoramento da integridade estrutural têm sido estudadas. Uma das técnicas mais recentes é a utilização de sensores contínuos, constituídos de filmes delgados de nanocompósitos. A vantagem em se utilizar estes materiais é a possibilidade de se aplicar tais sensores em superfícies complexas, cobrindo uma grande área e utilizando poucos terminais de aquisição. Este tipo de material permite o controle das suas características mecânicas e elétricas, possibilitando a criação de um sensor customizado para cada situação. O nanocompósito mais encontrado na literatura para fins de detecção de falhas é o compósito de nanotubos de carbono (CNT), sendo que a matriz utilizada varia de acordo com cada caso. Este trabalho propõe a utilização de materiais alternativos como os nanofios de ITO (Indium tin oxide ou óxido de índio dopado com estanho) inseridos em matriz de PMMA (polimetil- metacrilato) para o revestimento da superfície a ser monitorada. Afim de verificar a efetividade destes sensores, diversos testes foram propostos. Estes testes consistem em monitorar o comportamento dos nancompósitos quando afetados por algum tipo de dano, os quais podem ser uma adição de massa, excesso de cargas tensoras ou uma trinca. Para isto, o intuito é medir as resistências elétricas entre dois pontos e verificar a influência do dano no valor medido. Em geral as resistências obtidas variaram entre 1kΩ e 10kΩ, sendo que com o aparecimento do dano, as medições apresentaram variações de mais de 30% tanto no caminho com o dano como nos caminhos em sua proximidade. Testes com o sensor em uma câmara ambiental, com controle de temperatura e umidade, mostraram que o sensor é sensível a altas temperatura e a altas umidades. Quanto à sensibilidade às tensões de tração aplicadas na placa, o sensor só mostrou alterações em sua... / Abstract: With the objective of structural health monitoring (SHM) in aerospace structures, several monitoring techniques have been studied. One of the most recent techniques is based on the use of continuous sensors, made of thin films of nanocomposites. The advantage of using such materials is the possibility to apply them on complex surfaces, covering larger areas and using few acquisition terminals. Moreover, by using the nanocomposites, it is possible to control its mechanical and electrical properties, making it possible to create a customized sensor for each case. The nanocomposite found most commonly in the literature for damage detection are the carbon nanotubes (CNT) composites, while the matrix depends on each case. This work proposes the use of alternative materials such as the ITO (Indium tin oxide) nanowires inserted at PMMA (Poly(methyl methacrylate)) to be used as coatings for the monitored structure. In order to verify the effectiveness of this sensor, several tests were proposed. These tests consists on monitoring the nanocomposite's behavior when affected by some kind of damage, in which can be simulated by a mass addition, excess of load or a crack. The principle for the damage detection is to measure the electrical resistance between two points at the film sensor and verify the variations caused by the damage to these measurements. In general, the obtained resistances varied from 1kΩ to 10kΩ, while with the damage appearance the measurements varied more than 30% at the path with the damage and at the paths nearby. Tests at the environmental chamber, with temperature and humidity control, showed that the sensor is sensitive to high temperatures and humidity levels. Regarding the sensibility to stress applied to the plate, the sensor showed changes at the resistances only when the plate started to deform plastically. The obtained results are promising and indicate that this method is effective for damage detection / Mestre

abstract: Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focusses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors. / Dissertation/Thesis / Ph.D. Mechanical Engineering 2012

abstract: Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage. / Dissertation/Thesis / Ph.D. Electrical Engineering 2013

Sound can propagate for long distances with a low loss of intensity in objects whose geometry acts as a guide for the sound waves; a phenomenon that can be utilised for long range testing of structures. The guided sound waves can be used to conduct materials evaluation or to detect flaws, which can be done for a relatively large region of coverage from a relatively small region of access. In particular this technology can be used to inspect or monitor large engineering structures whose structural integrity is critical for safety and the environment, such as wind turbine towers, ship hulls, and pipelines. The use of guided waves for structural inspection is complicated by the existence of many wave modes. In this thesis, the Macro Fiber Composite (MFC) is characterised for its frequency, wavelength, wave mode and direction dependent sensitivity. These devices are flexible, light and thin, and, here have been shown to have wave mode sensitivity characteristics that are favourable for some applications. The MFC is a piezoelectric actuator that can be used to excite and sense in-plane vibrations at a structures surface. The surface area of an MFC is significantly large with respect to typical wavelengths used in ultrasonic guided wave applications, which combined with their in-plane extensional nature gives rise to a significantly wave mode, frequency and direction dependent sensitivity. This can limit their application, but can also potentially be exploited for greater wave mode control. A method for simulating the output from hypothesised transducer behaviour is shown and validated for the MFC. This allows their behaviour to be predicted for new structures. It is shown that their frequency response can depend on the waveguide and can vary with direction, which can lead to wave mode transmission and reception characteristics that may be advantageous for some methods of application and detrimental to others. A novel method of adapting a flexible transducer, such as the MFC, has been developed and its characterisation is given. It is shown that through the use of a decoupling membrane, an MFC can be caused to have very different wave mode sensitivity characteristics whilst retaining their light and flexible nature. These altered characteristics are favourable for applications where shear horizontal wave modes are required. Both fully coupled MFC transducers and the adapted MFC transducers are considered for application to pipeline testing. Fully coupled MFC transducers are used for inspection using longitudinal waves, whilst the adapted MFC transducers are used with torsional waves. These arrays are compared to a current commercial tool.

A large-area electronics (LAE) strain sensor, termed soft elastomeric capacitor (SEC), has shown great promise in fatigue crack monitoring. The SEC is capable to monitor strain changes over a large structural surface and undergo large deformations under cracking. Previous tests verified that the SEC can detect and localize fatigue cracks under low-cycle fatigue loading. In this paper, we further investigate the SEC's capability for monitoring high-cycle fatigue cracks, which are commonly seen in steel bridges. The peak-to-peak amplitude (pk-pk amplitude) of the SEC measurement is proposed as an indicator of crack growth. This technique is is robust and insensitive to long-term capacitance drift. To overcome the difficulty of identifying the pk-pk amplitude in time series due to high signal-to-noise ratio, a signal processing method is established. This method converts the measured SEC capacitance and applied load to power spectral densities (PSD) in the frequency domain, such that the pk-pk amplitudes of the measurements can be accurately extracted. Finally, the performance of this method is validated using a fatigue test of a compact steel specimen equipped with a SEC. Results show that the crack growth under high-cycle fatigue loading can be successfully monitored using the proposed signal processing method.

The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

This thesis describes the development of a measurement system for monitoring dynamic tests of civil engineering structures using long exposure motion blurred images, named LEMBI monitoring. Photogrammetry has in the past been used to monitor the static properties of laboratory samples and full-scale structures using multiple image sensors. Detecting vibrations during dynamic structural tests conventionally depends on high-speed cameras, often resulting in lower image resolutions and reduced accuracy. To overcome this limitation, the novel and radically different approach presented in this thesis has been established to take measurements from blurred images in long-exposure photos. The motion of the structure is captured in an individual motion-blurred image, alleviating the dependence on imaging speed. A bespoke algorithm is devised to determine the motion amplitude and direction of each measurement point. Utilising photogrammetric techniques, a model structure s motion with respect to different excitations is captured and its vibration envelope recreated in 3D, using the methodology developed in this thesis. The approach is tested and used to identify changes in the model s vibration response, which in turn can be related to the presence of damage or any other structural modification. The approach is also demonstrated by recording the vibration envelope of larger case studies in 2D, which includes a full-scale bridge structure, confirming the relevance of the proposed measurement approach to real civil engineering case studies. This thesis then assesses the accuracy of the measurement approach in controlled motion tests. Considerations in the design of a survey using the LEMBI approach are discussed and limitations are described. The implications of the newly developed monitoring approach to structural testing are reviewed.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.