Orientador: Samuel da Silva / Resumo: O principal objetivo deste trabalho é usar métodos de extrapolação em coeficientes de modelos autorregressivos (AR), para fornecer informações futuras de condições de estruturas na existência de mecanismo de danos pré-definidos. Os modelos AR são estimados considerando a predição de um passo à frente, verificados e validados a partir de dados de vibração de uma estrutura na condição não danificada. Os erros de predição são usados para extrair um indicador para classificar a condição do sistema. Então, um novo modelo é identificado se qualquer variação de índices de dano ocorrer, e seus coeficientes são comparados com os do modelo de referência. A extrapolação dos coeficientes de AR é realizada através das splines cúbicas por partes que evitam possíveis instabilidades e alterações indesejáveis dos polinômios, obtendo aproximações adequadas através de polinômios de baixa ordem. Uma curva de tendência para o indicador capaz de predizer o comportamento futuro pode ser obtida a partir da extrapolação direta dos coeficientes. Uma estrutura de três andares com um para-choque e uma coluna de alumínio colocada no centro do último andar são analisados com diferentes cenários de dano para ilustrar a abordagem. Os resultados indicam a possibilidade de estimar a condição futura do sistema a partir dos dados de vibração nas condições de danos iniciais. / Abstract: The main purpose of this work is to apply extrapolation methods upon coefficients of autoregressive models (AR), to provide future condition information of structures in the existence of predefined damage mechanism. The AR models are estimated considering one-step-ahead prediction, verified and validated from vibration data of a structure in the undamaged condition. The prediction errors are used to extract an indicator to classify the system state condition. Then, a new model is identified if any variation of damage indices occurs, and its coefficients are compared to the ones from the reference model. The extrapolation of the AR coefficients is performed through the piecewise cubic splines that avoid possible instabilities and undesirable changes of the polynomials, obtaining suitable approximations through low-order polynomials. A trending curve for the indicator capable of predicting future behavior can be obtained from direct coefficient extrapolation. A benchmark of a three-story building structure with a bumper and an aluminum column placed on the center of the top floor is analyzed with different damage scenarios to illustrate the approach. The results indicate the feasibility of estimating the future system state from the vibration data in the initial damage conditions. / Mestre

The future of the aerospace industry in large part relies on two factors: (i) development of advanced damage tolerant materials and (ii) development of advanced smart sensors with the ability to detect and evaluate defects at very early stages of component service life. Laminated composite materials, such as carbon fibre reinforced plastics (CFRP), have emerged as the materials of choice for increasing the performance and reducing the cost and weight of aircrafts, which leads to less fuel consumption and therefore lower CO2 emissions. However, it is well known that these materials exhibit fragile behaviour, poor resistance to impact damage caused by foreign objects and require a relatively slow and labour intensive manufacturing process. These factors prevent the rapid expansion of composite materials in several industrial sectors at the current time. Inspired by the use of rope throughout history and driven by the necessity of creating a lean manufacturing process for composites and enhancing their impact properties, the first part of this work has shown that enhanced damage tolerance and pseudo-ductile behaviour can be achieved with standard CFRP by creatively arranging the fibres into a 3D twisted helical configuration. Through an extensive experimental campaign a new method to arrange fibre reinforcement was presented and its effect investigated. The second part of this PhD work focused on developing a new smart sensor. A spiral passive electromagnetic sensor (SPES) for damage detection on CFRP and glass fibre reinforced plastics (GFRP) is presented in this work. A range of defect types in glass and carbon composite has been considered, such as delamination, perforated holes and cracks. Furthermore, throughout this work, the SPES has been exploited as a multi-sensing device allowing the ability to detect temperature and humidity variation, presence of ice and act as an anti/de-icing device.

Este trabalho apresenta o desenvolvimento de sistemas computacionais de apoio à aquisição, visualização e tratamento de dados provenientes de monitoração de estruturas de Engenharia Civil. Por meio dos sistemas construídos, torna-se possível adquirir, visualizar e filtrar informações com facilidade, ao mesmo tempo em que se permite avaliar a influência da temperatura, da retração e da fluência nos elementos estruturais monitorados. Buscando desenvolver as ferramentas computacionais em uma perspectiva alinhada com necessidades reais do setor, foram monitoradas duas pontes rodoviárias e uma estação metroviária, sendo utilizada instrumentação avançada baseada em sensores e equipamentos ópticos e elétricos, permitindo a medição de grandezas como deformação, temperatura, deslocamento e rotações. A experiência adquirida em campo encontra-se descrita, apresentando-se a estrutura e componentes dos modernos sistemas de monitoração e os principais resultados decorrentes da sua aplicação nas obras monitoradas. Nesse contexto, os sistemas computacionais construídos demonstraram ser úteis ferramentas, auxiliando nas diversas fases da monitoração estrutural, desde a aquisição até a interpretação dos resultados. / This work presents the development of computational systems to support acquisition, visualization, and treatment of data from health monitoring systems of civil structures. With these systems, it is possible to acquire, to visualize, and to filter monitored data. It is also possible to evaluate the influence of temperature, shrinkage and creep in structural elements. The goal is the development of tools to fulfill real needs. For that, two bridges and a subway station were monitored using advanced instrumentation based on optical and electrical sensors. The acquired experience in field is described, presenting the structure and the various components of modern structural health monitoring systems, as well as the main results of application in the monitored structures. In that context, the built computational systems demonstrated to be very useful, aiding in the several phases of the structural monitoring from the acquisition to the interpretation of the results.

This work presents methodologies for the integration of field testing and Structural Health Monitoring SHM in the assessment of reinforced concrete bridges. The methodologies are demonstrated through the use of data collected during the testing of reinforced concrete railway bridges and long-term monitoring of a highway bridge. A probabilistic life-cycle prediction model based on sectional analysis is proposed for reinforced concrete structures. The updating of the model parameters is done using a Bayesian updating approach in which the problem is defined as a reliability one. An algorithm that uses subset simulation is used to sample points from the updated parameter distributions. Testing data from a reinforced concrete railway bridge is used to demonstrate the methodology and its results. The description of a SHM system that was installed in the Jaguari River Bridge is also presented. During the stages of preparation for the installation of this system the bridge was inspected, had NDT performed, and field testing was conducted using a test truck. The results of these tests are also presented. Analysis of the collected data from the live-load response of the Jaguari River Bridge is used to demonstrate methodologies for obtaining live-load response distributions from monitoring data. The use of this live-load response data is also used for the life-cycle analysis of one of the bridge\'s cross-sections. / Este trabalho apresenta metodologias de integração de ensaios estruturais e Structural Health Monitoring (SHM) para a avaliação de pontes de concreto armado. O SHM diz respeito a um conjunto de praticas com o objetivo de acompanhar o comportamento estrutural através de sensores com o objetivo de acompanhar o comportamento da estrutura e determinar ações de manutenção de maneira proativa. As metodologias são apresentadas através do uso de dados coletados durante ensaios de pontes ferroviárias em concreto armado e do monitoramento continuo de uma ponte rodoviária. Um modelo para o ciclo de vida de estruturas de concreto armado baseado no método das lamelas é proposto. Os parâmetros deste modelo, que são considerados de maneira probabilística, são atualizados através de um método Bayesiano. Dados de ensaios de uma ponte ferroviária são utilizados nesta analise. A descrição de um sistema de monitoramento contínuo instalado na Ponte do Rio Jaguari também é feita. Durante as etapas de desenvolvimento do sistema a ponte foi inspecionada, ensaios não destrutivos foram feitos e ensaios com um veículo teste foram conduzidos. Os resultados e analises destes também são apresentados. Os dados coletados por este sistema foram utilizados para demonstrar metodologias de caracterização dos modelos de resposta devido a cargas moveis. A utilização destes modelos na avaliação de confiabilidade ao longo do tempo de uma das seções da ponte também é apresentada.

A utilização de materiais piezoelétricos, na função de sensores e atuadores distribuídos, para o controle e monitoramento de vibrações estruturais tem um enorme potencial de aplicação nas indústrias aeronáutica, aeroespacial, automobilística e eletroeletrônica. O uso de sensores piezoelétricos integrados para monitoramento de integridade estrutural (ou detecção de falhas), em particular, tem evoluído bastante na última década. Por conseguinte, o número de técnicas utilizadas para esse fim são as mais variadas possíveis. Dentre elas estão às técnicas que avaliam o efeito dos danos em baixa freqüência usando parâmetros modais, em especial freqüências naturais e modos, ou em média-alta freqüência medindo-se a impedância/admitância eletromecânica. O objetivo dessa dissertação é desenvolver, com auxílio de um modelo 2D ANSYS em elementos finitos, uma análise de diferentes técnicas para detecção da posição e tamanho da delaminação em estruturas compósitas utilizando pastilhas piezoelétricas. Várias métricas e técnicas são avaliadas em termos de sua capacidade de identificar, com relativa acurácia, a presença, localização e severidade do dano. Os resultados mostram que ambas as técnicas modal e baseada na impedância são capazes de identificar a presença de danos do tipo delaminação, desde que as pastilhas piezoelétricas estejam próximas do dano. Também é mostrado que as técnicas baseadas na impedância parecem ser mais eficientes do que as modais para detecção da posição e tamanho da delaminação. / The use of piezoelectric materials in the function of distributed sensors and actuators for the control and monitoring of structural vibrations has enormous potential for application in the aeronautical, aerospace, automotive and electronics. The use of integrated piezoelectric sensors for structural health monitoring (or damage detection), in particular, has evolved greatly over the last decade. Consequently, the numbers of techniques used for this purpose are highly diverse. Among them are techniques that evaluate the effect of damages on low frequency modal parameters, especially natural frequencies and mode shapes, or on medium-high frequency measurements of electromechanical impedance/admittance. The objective of this dissertation is to perform, with the aid of a 2D ANSYS finite element model, an analysis of different techniques for the detection of position and size of a delamination in a composite structure using piezoelectric patches. Several metrics and techniques are evaluated in terms of their capability of identifying, with relative accuracy, the presence, location and severity of the damage. Results show that both modal and impedance-based techniques are able to identify the presence of the delamination-type damages, provided the piezoelectric patches are close enough to the damage. It is also shown that impedance-based techniques seem more effective than modal ones for the detection of delamination position and size.

This master thesis is the result of a collaborative work between EMBRAER and the Escola Politécnica da USP for the study of structural health monitoring (SHM) techniques using sensors applied to aircraft structures. The goal was to develop classification techniques to discriminate between different events arising in the aircraft structure during tests; in the short term, improving the current SHM system used by EMBRAER, based on acoustic emission and, in the long term, fostering the development of a fully distributed system. As a result of studying classification methods for immediate use, we developed two techniques: the Spectral Similarity and a Support Vector Machines (SVM) classifier. Both are unsupervised solutions, due to the unlabeled nature of the data provided. The two solutions were delivered as a final product to EMBRAER for prompt use in the existing SHM system. By studying distributed solutions for future implementations, we developed a detection algorithm based on adaptive techniques. The main result was a special initialization for a maximum likelihood (ML) detector that yields an exponential decay rate in the error probability to a nonzero steady state, using adaptive diffusion estimation in a distributed sensor network. The nodes that compose the network must decide, locally, between two concurrent hypotheses concerning the environment state where they are inserted, using local measurements and shared estimates coming from their neighbors. The exponential performance does not depend on the adaptation step size value, provided it is sufficiently small. The results concerning this distributed detector were published in the journal IEEE Signal Processing Letters. / Esta dissertação de mestrado é o resultado de um trabalho colaborativo entre a EMBRAER e a Escola Politécnica da USP no estudo de técnicas de monitoramento do estado de saúde de estruturas (Structural Health Monitoring - SHM) utilizando sensores em estruturas aeronáuticas. O objetivo foi desenvolver técnicas de classificação para discriminar entre diferentes eventos que surgem em estruturas aeronáuticas durante testes; para o curto prazo, aperfeiçoando o atual sistema de SHM utilizado pela EMBRAER, baseado em emissão acústica e, no longo prazo, fomentando o desenvolvimento de um sistema completamente distribuído. Como resultado do estudo de métodos de classificação para uso imediato, desenvolvemos duas técnicas: a Similaridade Espectral e um classificador que utiliza Support Vector Machines (SMV). Ambas as técnicas são soluções não-supervisionadas, devido a natureza não rotulada dos dados fornecidos. As duas soluções foram entregues como um produto final para a EMBRAER para pronta utilização em seu atual sistema de SHM. Ao estudar soluções completamente distribuídas para futuras implementações, desenvolvemos um algoritmo de detecção baseado em técnicas adaptativas. O principal resultado foi uma inicialização especial para um detector de máxima verossimilhança (maximum likelihood - ML) que possui uma taxa de decaimento exponencial na probabilidade de erro até um valor não nulo em regime estacionário, utilizando estimação adaptativa em uma rede distribuída. Os nós que compõem a rede devem decidir, localmente, entre duas hipóteses concorrentes com relação ao estado do ambiente onde eles estão inseridos, utilizando medidas locais e estimativas compartilhadas vindas de nós vizinhos. O desempenho exponencial não depende do valor do passo de adaptação, se este for suficientemente pequeno. Os resultas referentes a este detector distribuído foram publicados na revista internacional IEEE Signal Processing Letters.

"This dissertation proposes a vibration-based approach to detect and monitor structural damage by tracking the instantaneous modal parameters. A change in the instantaneous modal parameters indicates change in the structural health condition. In contrast to many existing structural health monitoring schemes, the proposed approach is less model dependent and works well for both sudden and evolving damage, general loading conditions and complex structures. The instantaneous modal parameters, including modal frequency, mode shape vector and modal damping ratio, are introduced as a bridge between the system properties and time varying vibration modes. The theoretical background of the time-varying vibration modes is developed. It has been shown that for slowly time-varying systems such modes exist and the instantaneous modal parameters have a clear physical interpretation and can be identified from free and forced vibration responses. A set of known techniques are used in an innovative way to identify the instantaneous modal parameters. Applicability of the identification techniques depends on the nature and availability of measurement data. Wavelet ridge method is used to identify the instantaneous modal frequencies and normalized instantaneous mode shape vectors from free vibration data. Wavelet packet sifting technique in conjunction with Hilbert transform and confidence index is proposed to identify the normalized instantaneous mode shape vector from both free and forced vibration data. Time-varying Kalman filter is integrated with the wavelet packet sifting technique to identify the instantaneous modal frequencies and the instantaneous modal damping ratios from free and forced vibration data. The proposed approach has been validated using both simulation and experimental data. The simulation data is obtained from a multi-degree-of-freedom system with time varying stiffness under different loading conditions. Experimental data include both impact testing data from the ASCE benchmark study and shaking-table test data of a full-size two-story wooden building structure, conducted at DPRI, Kyoto University, Japan. It has been shown that the proposed approach can successfully detect and monitor damage and, therefore, has great potential for real applications."

In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.

"Health monitoring of structures by experimental modal analysis is typically performed with piezoelectric based transducers. These transducers are usually heavy, large in size, and require high power to operate, all of which reduce their versatility and applicability to small components and structures. The advanced developments of microfabrication and microelectromechanical systems (MEMS) have lead to progressive designs of small footprint, low dynamic mass and actuation power, and high-resolution inertial sensors. Because of their small dimensions and masses, MEMS inertial sensors could potentially replace the piezoelectric transducers for experimental modal analysis of small components and structures. To transfer data from MEMS inertial sensors to signal analyzers, traditional wiring methods may be utilized. Such methods provide reliable data transfer and are simple to integrate. However, in order to study complex structures, multiple inertial sensors, attached to different locations on a structure, are required. In such cases, using wires increases complexity and eliminates possibility of achieving long distance monitoring. Therefore, there is a need to implement wireless communications capabilities to MEMS sensors. In this thesis, two different wireless communication systems have been developed to achieve wireless health monitoring of structures using MEMS inertial sensors. One of the systems is designed to transmit analog signals, while the other transmits digital signals. The analog wireless system is characterized by a linear frequency response function in the range of 400 Hz to 16 kHz, which covers the frequency bandwidth of the MEMS inertial sensors. This system is used to perform modal analysis of a test structure by applying multiple sensors to the structure. To verify the results obtained with MEMS inertial sensors, noninvasive, laser optoelectronic holography (OEH) methodology is utilized to determine modal characteristics of the structure. The structure is also modeled with analytical and computational methods for correlation of and verification with the experimental measurements. Results indicate that attachment of MEMS inertial sensors, in spite of their small mass, has measurable effects on the modal characteristics of the structure being considered, verifying their applicability in health monitoring of structures. The digital wireless system is used to perform high resolution tilt and rotation measurements of an object subjected to angular and linear accelerations. Since the system has been developed based on a microcontroller, programs have been developed to interface the output signals of the sensors to the microcontroller and RF components. The system is calibrated using the actual driving electronics of the MEMS sensors, and it has achieved an angular resolution of 1.8 mrad. The results show viability of the wireless MEMS inertial sensors in applications requiring accurate tilt and rotation measurements. Additional results presented included application of a MEMS gyroscope and microcontroller to perform angular rate measurements. Since the MEMS gyroscope only generates analog output signals, an analog to digital conversion circuit was developed. Also, a program has been developed to perform analog to digital conversion with two decimal places of accuracy. The experimental results demonstrate feasibility of using the microcontroller and the gyroscope to perform wireless angular rate measurements."

"In this work an innovative wavelet packet based sifting process for signal decomposition has been developed and its application for health monitoring of time-varying structures is presented. With the proposed sifting process, a signal can be decomposed into its mono-frequency components by examining the energy content in the wavelet packet components of a signal, and imposing certain decomposition criteria. The method is illustrated for simulation data of a linear three degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring by monitoring instantaneous modal parameters of the structure for both, cases of abrupt structural stiffness loss and progressive stiffness degradation. The effectiveness of this method for practical application is evaluated by applying the methodology for experimental data and the results obtained matched with the field observations. The proposed methodology has shown better results in a comparison study which is done to evaluate performance of the proposed approach with other available SHM techniques, namely EMD technique and Continuous Wavelet Transform (CWT) method, for cases characterized by different damage scenarios and noise conditions."