Orientador: Fábio Roberto Chavarette / Co-orientador: Mara Lúcia Martins Lopes / Banca: Francisco Villarreal Alvarado / Banca: Ivan Rizzo Guilherme / Resumo: Nesta dissertação de mestrado apresentam-se duas metodologias para o desenvolvimento de sistemas de monitoramento de integridade de estruturas mecânicas e aeronáuticas, utilizando técnicas de computação inteligente, tais como as redes neurais artificiais e os sistemas imunológicos artificiais. Neste contexto, emprega-se uma rede neural artificial ARTMAP-Fuzzy e o algoritmo de seleção negativa. Ambas as técnicas são empregadas para realizar a análise, identificação e caracterização das falhas estruturais decorrentes da estrutura. A principal aplicação destes métodos é auxiliar no processo de inspeção de estruturas mecânicas e aeronáuticas, visando detectar e caracterizar falhas, bem como, a tomada de decisões, a fim de evitar catástrofes/acidentes. Com estas propostas busca-se a concepção de novos sistemas de monitoramento de integridade estrutural que possam ser modificados facilmente, para atender a permanente evolução das tecnologias e da indústria. Para avaliar as metodologias propostas, foram realizados experimentos em laboratório para gerar um banco de dados de sinais capturados em uma viga de alumínio. Os resultados obtidos pelos métodos são excelentes, apresentando robustez e precisão / Abstract: In this dissertation presents two methodologies to develop health monitoring of aircraft structures and mechanical systems, using intelligent computing techniques such as artificial neural networks and artificial immune systems. In this context, uses an ARTMAP-Fuzzy artificial neural network and the negative selection algorithm. Both techniques are used for the analysis, identification and characterization of structural failure due to the structure. The main application of these methods is to assist in the inspection of mechanical and aeronautical structures, to detect and characterize flaws as well, making decisions in order to avoid disasters/accidents. With these proposals one seeks to designing new systems for structural health monitoring that can be modified easily to cater to permanent evolution technologies and industry. To evaluate the proposed methodologies, experiments were performed in the laboratory to generate a database of captured signals in an aluminum beam. The results obtained by the methods are excellent, with robustness and accuracy / Mestre

Orientador: Samuel da Silva / Banca: Michael John Brennan / Banca: Roberto Gil Annes da Silva / Resumo: Esse trabalho apresenta uma abordagem baseada no processamento de séries temporais para tratar o problema de detecção e o prognóstico de danos em estruturas com sensores e atuadores piezelétricos acoplados considerando as possíveis variabilidades ambientais e operacionais. A primeira abordagem se baseia na identificação de um modelo autorregres- sivo de predição construído com um sinal temporal de resposta de referência. Métricas indicativas de danos são extraídas dos erros de predição e a separação de efeitos (carrega- mento ou danos) é feita por um algoritmo de agrupamento fuzzy. Esse procedimento é implementado em uma estrutura de material compósito ensaiada em um sistema para teste de materiais de modo a reproduzir condições de carregamento a fim de simular condições reais de operação da estrutura. Por outro lado, a segunda metodologia proposta emprega uma identificação de dois estágios. Primeiramente, um modelo autorregressivo é criado para o monitoramento estrutural como no procedimento anterior, porém, utilizando o controle estatístico de processos para detectar um dano progressivo. Em seguida, modelos autorregressivos com entradas exógenas são estimados para as condições de referência e de dano para acompanhar as variações de parâmetros e permitir a realização de um prognóstico sobre a condição estrutural futura da estrutura. Testes iniciais em uma placa de alumínio mostraram que este método é capaz de realizar um prognóstico razoável e predizer o comportamento dinâmico da estrutura associado com um nível específico de redução de massa. Ambos métodos e resultados são discutidos e comparados ao final do trabalho / Abstract: This work presents an approach based on time series processing to deal with the damage detection and prognosis issue in structures coupled with piezoelectric sensors and actuators considering eventual operational and environmental variabilities. The first approach is based on the identification of a predictive autoregressive model obtained with a reference time response. Damage indicative metrics are extracted from prediction errors and the separation of effects (loading or damage) is performed by a fuzzy clustering algorithm. This procedure is carried on a composite structure attached to a material test system to reproduce loading conditions in order to simulate real operational conditions. On the other hand, the second proposed methodology employs a two step identification. First, an autoregressive model is created for structural monitoring similarly to the previous procedure, but employing statistical process control to detect progressive damage. Next, autoregressive models with exogenous inputs are estimated for reference and damaged conditions in order to track variation of parameters, allowing the prognosis of the structure's future structural condition. Initial tests on an aluminum plate indicated that this method is capable of performing a reasonable prognosis and predicting structure's dynamic behavior associated to a specific level of mass reduction. Both methods and results are discussed and compared by the end of the work / Mestre

Monitoramento da integridade estrutural (Structural health monitoring - SHM) está relacionado à implementação de alguma estratégia para a detecção de dano em estruturas de engenharia. Este estudo geralmente envolve a observação do sistema no tempo, utilizando amostras periódicas de medições da resposta dinâmica, a partir de um grupo de sensores, a fim de verificar alterações nos parâmetros modais, que podem indicar a presença do dano. Entretanto, especialmente para estruturas treliçadas, este processo tornase difícil principalmente porque nem todos os deslocamentos ou rotações nodais modelados numericamente podem ser medidos experimentalmente. Desta forma, o presente estudo tem por objetivo tratar algumas das ainda correntes questões dos sistemas de monitoramento da integridade estrutural baseados em registros de vibração. Primeiramente aborda-se um tema que, apesar de recentemente ter se mostrado importante, ainda apresenta muito poucos estudos: a influência da variação dos efeitos ambientais, especialmente a temperatura, sobre as características dinâmicas de estruturas. Com o intuito de verificar tal influência em pontes metálicas, os resultados apresentados por Ni et al. (2005) são utilizados para a realização de estudos de correlação, através de uma comparação entre equações de regressão linear e um modelo, proposto no presente trabalho, em Redes Neurais Artificiais (RNA). A seguir são estudados procedimentos de identificação estocástica de sistemas, passo fundamental para o monitoramento da integridade estrutural. Realiza-se uma revisão bibliográfica nesta área abordando a evolução dos métodos que utilizam apenas dados de resposta para a identificação. Enfoque principal é dado nos métodos de identificação estocástica de subespaço (SSI), pois se mostram os mais práticos e robustos para a determinação dos parâmetros modais da estrutura.Finalmente, o método dos vetores de localização de dano (Damage locating vector method- DLV), introduzido por Bernal (2002), é extensivamente discutido. Esta é umatécnica eficaz quando operando com um número arbitrário de sensores, modos truncados e em cenários de dano múltiplo, mantendo as operações numéricas simples. Além disto, a influência do ruído na precisão do método dos vetores de localização de dano é avaliada. Com o intuito de verificar o comportamento do método DLV perante diferentes intensidades de dano e, principalmente, na presença de ruído de medição, um estudo paramétrico é conduzido. Distintas excitações, como também diferentes cenários de dano, são numericamente testadas em uma treliça Warren contínua considerando um limitado conjunto de sensores, através de cinco níveis de ruído. Além disto, é proposto um caminho alternativo para determinar os vetores de localização de dano no procedimento do método DLV. A idéia é oferecer uma opção alternativa para a solução do problema utilizando um método algébrico amplamente difundido. A formulação original via decomposição em valores singulares é subsituída pela solução mais trivial de um problema de valores próprios. Isto é possível graças à relação algébrica entre a decomposição em valores singulares de uma matriz e a solução do problema de autovalores desta matriz pré-multiplicada por sua transposta. Os resultados finais mostraram que o método DLV, considerando a soluça alternativa, foi capaz de corretamente localizar as barras danificadas, utilizando dados somente de resposta da estrutura, mesmo considerando pequenas intesidades de dano e moderados níveis de ruído. / Structural health monitoring (SHM) refers to the implementation of some strategy for damage detection in engineering structures. This study generally involves the observation of a system over time using periodically sampled dynamic response measurements from a set of sensors in order to verify changes in modal parameters, which may indicate damage or degradation. However, especially for truss structures this process sounds difficulty mainly because not all nodal displacements or rotations in the numerical model can be experimentally measured. In this context, the present thesis aims to address some still current issues of the vibration-based structural health monitoring systems. Firstly it is introduced a subject that, although has recently shown important, still presents very few studies: the environmental effects, mainly temperature, on the structural modal properties. Seeking to address this influence on steel bridges, the results presented by Ni et al. (2005) are used to conduct correlations studies, comparing linear equation regression with an artificial neural network model (ANN), proposed in the present thesis. Procedures for stochastic systems identification are studied next, which is a fundamental phase for the SHM systems. A literature review in this field addressing the evolution of the methods that just use response data for identification is carried out. Main focus is given in the stochastic subspace identification methods (SSI), because they have been known as the most practical and robust methods to determine the structure’s modal parameters. Finally, the damage locating vector (DLV) method, introduced by Bernal (2002), is extensively discussed. This is a useful approach because is effective when operating with an arbitrary number of sensors, a truncated modal basis and multiple damage scenarios, while keeping the calculation at a low level. In addition, the noise influence on the accuracy of the damage locating vector method is evaluated. In order to verify the DLV behavior in front of different damages intensities and, mainly, in presence of measurement noise, a parametric study had been carried out. Different excitations as well as damagescenarios are numerically tested in a continuous Warren truss structure with a set of limited measurement sensors through five noise levels. Besides this, it is proposed another way to determine the damage locating vectors in the DLV procedure. The idea is to offer an alternative option to solve the problem with a more widespread algebraic method. The original formulation via singular value decomposition (SVD) is replaced by a common solution of an eigenvector and eigenvalue problem. This is possible thanks to the algebraic relationship between the singular value decomposition of a matrix and the eigenproblem solution of this matrix pre-multiplied by its transpose. The final results show that the DLV method, adopting the alternative, was able to correct locate the damaged bars, using an output-only system identification procedure, even considering small intensities of damage and moderate noise levels.

The present work introduces novel methodologies for damage detection and health monitoring of structural and mechanical systems. The new approach uses the angular velocity inside different mathematical forms, via a gyroscope, to detect, locate, and relatively quantify damage. This new approach has been shown to outperform the current state-of-the-art acceleration-based approach in detecting damage on structures. Additionally, the current approach has been shown to be less sensitive to environmental acoustic noises, which present major challenges to the acceleration-based approaches. Furthermore, the current approach has been demonstrated to work effectively on arch structures, which acceleration-based approaches have struggled to deal with. The efficacy of the new approach has been investigated through multiple forms of structural damage indices. The first methodology proposed a damage index that is based on the changes in the second spatial derivative (curvature) of the power spectral density (PSD) of the angular velocity during vibration. The proposed method is based on the output motion only and does not require information about the input forces/motions. The PSD of the angular velocity signal at different locations on structural beams was used to identify the frequencies where the beams show large magnitude of angular velocity. The curvature of the PSD of the angular velocity at these peak frequencies was then calculated. A damage index is presented that measures the differences between the PSD curvature of the angular velocity of a damaged structure and an artificial healthy baseline structure. The second methodology proposed a damage index that is used to detect and locate damage on straight and curved beams. The approach introduces the transmissibility and coherence functions of the output angular velocity between two points on a structure where damage may occur to calculate a damage index as a metric of the changes in the dynamic integrity of the structure. The damage index considers limited-frequency bands of the transmissibility function at frequencies where the coherence is high. The efficacy of the proposed angular-velocity damage-detection approach as compared to the traditional linear-acceleration damage-detection approach was tested on straight and curved beams with different chord heights. Numerical results showed the effectiveness of the angular-velocity approach in detecting damage of multiple levels. It was observed that the magnitude of the damage index increased with the magnitude of damage, indicating the sensitivity of the proposed method to damage intensity. The results on straight and curved beams showed that the proposed approach is superior to the linear-acceleration-based approach, especially when dealing with curved beams with increasing chord heights. The experimental results showed that the damage index of the angular-velocity approach outweighed that of the acceleration approach by multiple levels in terms of detecting damage. A third methodology for health-monitoring and updating of structure supports, which resemble bridges’ bearings, is introduced in this work. The proposed method models the resistance of the supports as rotational springs and uses the transmissibility and coherence functions of the output response of the angular velocity in the neighborhood of the bearings to detect changes in the support conditions. The proposed methodology generates a health-monitoring index that evaluates the level of deterioration in the support and a support-updating scheme to update the stiffness resistance of the supports. Numerical and experimental examples using beams with different support conditions are introduced to demonstrate the effectiveness of the proposed method. The results show that the proposed method detected changes in the state of the bearings and successfully updated the changes in the stiffness of the supports.

The PhD thesis evaluates the mechanical properties and damage tolerance of aerospace carbon/epoxy laminates containing long, narrow interlaminar galleries. The term 'galleries' refers to thin and long holes in a laminate used for the installation of small measuring devices, such as structural health monitoring (SHM) sensors. The galleries considered in this study are similar to those used in a novel SHM system known as 'Comparative Vacuum Monitoring (CVM)'. CVM was developed by the Australian company - Structural Monitoring Systems (SMS) - for damage detection in aircraft structures. CVM is a SHM system that utilises pressure differentials between a parallel series of galleries at atmospheric or low pressure to detect damage initiation and propagation. Thus far, CVM has been used for the monitoring of surface cracks in metallic structures using surface mounted sensors. Recent research has also demonstrated that it may be possible to monitor damage along the bond- line of both metallic and composite joints using CVM. The ability of CVM sensors to detect delamination damage inside composite structures is less well understood. It is envisaged that CVM can be used for the through-life health monitoring of composite aircraft structures prone to delamination damage. However, a major concern with applying CVM to composite laminates is the open-hole design of the galleries that may initiate damage growth under external loading. Material property data, structural tests, and models for predicting the properties of laminates containing galleries is needed before CVM technology can be certified for use in aircraft composite structures. The primary objectives of this PhD thesis are the development of an optimum process method for introducing multiple interlaminar CVM galleries in composite laminates; the development of a validated model for calculating changes to the mechanical properties of laminates containing CVM galleries; and the determination of optimum CVM gallery shape, size and orientation combinations for minimising the effect of the galleries on the mechanical properties of laminates. The effects of the shape, size and orientation of CVM galleries on the mechanical properties of carbon/epoxy laminates are evaluated by an extensive experimental research program, and the results are presented in the thesis. The properties investigated include the in-plane tensile and compressive properties, tensile and compressive fatigue life, through-thickness tensile strength, interlaminar shear strength, mode I and mode II interlaminar fracture toughness, and impact damage resistance. The results from tensile tests on lap-joints and T-joints containing CVM galleries are also presented.

The use of composite structures in engineering applications has proliferated over the past few decades due to its distinct advantages namely: high structural performance, corrosion resistance, and high strength/weight ratio. However, they also come with a set of disadvantages, i.e. they are prone to fibre breakage, matrix cracking and delaminations. These types of damage are often invisible and if undetected, could lead to catastrophic failures of structures. Although there are systems to detect such damage, the criticality assessment and prognosis of the damage is often much more difficult to achieve. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2D polymeric composite T-joint, used in maritime structures. The SHM system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all t he delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. This SHM system necessitated the development of a novel pre-processing algorithm - Damage Relativity Assessment Technique (DRAT) along with a pattern recognition tool, Artificial Neural Network (ANN), to predict and estimate the damage. Another program developed - the Global Neural network Algorithm for Sequential Processing of Internal sub Networks (GNAISPIN) uses multiple ANNs to render the SHM system independent to variations in structural loading and capable of estimating multiple delaminations in composite T-joint structures. Upto 82% improvement in detection accuracy was observed when GNAISPIN was invoked. The Finite Element Analysis (FEA) was also conducted by placing delaminations of different sizes at various locations in two structures, a composite beam and a T-joint. Glass Fibre Reinforced Polymer T-joints were then manufactured and tested, thereby verifying the accuracy of the FEA results experimentally. The resulting strain distribution from the FEA was pre-processed by the DRAT and used to trai n the ANN to predict and estimate damage in the structures. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this study.

Cette thèse a pour but didentifier de changements dans le comportement dynamique dun système mécanique par le développement des techniques didentification, de détection et de recalage de modèle. Un endommagement ou une activation de non - linéarité est considéré responsable du changement. Une bien connue classification pour la détection dendommagement dans la littérature est utilisée dans la thèse, qui définit quatre niveaux dans lordre croissant de complexité: - Niveau 1 : Détection dendommagement : inspection de la présence dendommagement dans la structure. - Niveau 2 : Localisation dendommagement : détermination de position de lendommagement. - Niveau 3 : Evaluation de la gravité de lendommagement. - Niveau 4 : Prévision de la durée restant de vie de la structure. Selon la classification décrite ci-dessus, le problème de diagnostic dans cette thèse est abordé pour les trois premiers niveaux, i.e. détection, localisation et évaluation. Lidentification dendommagements ou dactivation de non - linéarité est basée sur une comparaison entre un état actuel et létat de référence (en normal condition). La thèse est organisée comme suit : Chapitre 1 présente une étude bibliographique sur des méthodes dindentification modale et de détection. Cette partie décrit quelques caractéristiques principales de systèmes non - linéaires et également des défis que présente la non - linéarité. Les problèmes de localisation et dévaluation sont discutés ensuite. Chapitres 2, 3 et 4 se concentrent sur la détection de défaut, par exemple lactivation de non linéarité ou lapparition dendommagement par trois méthodes respectivement : la Transformée en ondelettes (Wavelet transform), la Séparation aveugle au second ordre (Second-Order Blind Identification) et lAnalyse en composantes principales à noyau (Kernel Principal component Analysis). Seuls des signaux de sortie sont utilisés pour le traitement. Les deux premières méthodes réalisent la surveillance structurale par un processus didentification modale, tandis que la dernière méthode exerce directement dans des espaces caractéristiques déterminés par une fonction de noyau choisie. La détection peut être réalisée au moyen du concept dangle entre sous-espaces ou basée sur des statistiques. La robustesse des méthodes est illustrée sur une structure de poutre avec une non -linéarité géométrique à un bout ; ce modèle a été étudié dans le cadre dun projet de recherche européenne COST F3. Des autres exemples sont également considérés, tels quune maquette davion avec différents niveaux dendommagement et deux applications industrielles avec le but de contrôle de qualité sur un set dappareils électro - mécaniques et sur des joints de soudure. Chapitre 5 vise à la localisation dendommagement basée sur lanalyse de sensibilité des résultats de lAnalyse en composantes principales dans le domaine fréquentiel. La localisation est exécutée par la comparaison des sensibilités de composantes principales entre létat de référence (saine) et un état endommagé. Seules les réponses mesurées, e.g. réponses en fréquence (FRFs) sont nécessaires pour cet objectif. Après lanalyse de sensibilité au Chapitre 5, Chapitre 6 sadresse à lévaluation de paramètres, par exemple estimation dendommagements. Pour ce but, une procédure de recalage de modèle est exécutée. Cette procédure demande de construire un modèle analytique de la structure. Lanalyse de sensibilité pour la localisation dendommagement est illustrée par des données numériques et expérimentales dans des systèmes de masse - ressort et dans des structures de poutre. Une réelle structure, i.e. le pont I-40 à New Mexico qui a été détruite en 1993 est aussi examinée. Enfin, des conclusions sont retirées basées sur le travail réalisé et quelques perspectives sont proposées pour la continuation de cette recherche.

Railroad bridges are the most important connection parts of railroad networks. These bridges are exposed to heavier train loads compared to highway bridges as well as various detrimental ambient conditions during their life span. The railroad bridges in Turkey are mostly constructed during the late Ottoman and first periods of the Turkish Republic / therefore, they are generally close to about 100 years of age / their inspection and maintenance works are essential. Structural health monitoring (SHM) techniques are widely used around the world in order to increase the effectiveness of the inspection and maintenance works and also evaluate structural reliability. Application of SHM methods on railway bridges by static and dynamic measurements over short and long durations give important structural information about bridge members&rsquo / load level and overall bridge structure in terms of vibration frequencies, deflections, etc. Structural Reliability analysis provides further information about the safety of a structural system and becomes even more efficient when combined with the SHM studies. In this study, computer modeling and SHM techniques are used for identifying structural condition of a steel truss railroad bridge in Usak, Turkey, which is composed of six spans with 30 m length each. The first two spans of the bridge were rebuilt about 50 years ago, which had construction plans and are selected as pilot case for SHM and evaluation studies in this thesis. Natural frequencies are obtained by using 4 accelerometers and a dynamic data acquisition system (DAS). Furthermore, mid span vertical deflection member strains and bridge accelerations are obtained using a DAS permanently left on site and then compared with the computer model analyses results. SHM system is programmed for triggering by the rail load sensors developed at METU and an LVDT to collect mid span deflection high speed data from all sensors during train passage. The DAS is also programmed to collect slow speed data (once at every 15 minutes) for determination of average ambient conditions such as temperature and humidity and all bridge sensors during long term monitoring. Structural capacity and reliability indices for stress levels of bridge members are determined for the measured and simulated train loads to determine structural condition of bridge members and connections. Earthquake analyses and design checks for bridge members are also conducted within the scope of this study.

Nearly one third of the 604,426 bridges in the United-States are either structurally deficient or functionally obsolete. Monitoring these bridges is essential to avoid catastrophic accidents. In steel bridges fatigue induced crack/rupture, which is one of the most common modes of failure, can be avoided if the crack is detected at the early stages of its formation. Cracks usually originate at stress concentration areas but their precise origin is random. Such strain concentration can be monitored with traditional strain gages, but their installation requires lengthy wires and equipment, which are expensive and labor intensive. Therefore wireless sensors are being developed to cope with these problems. In this work, a passive wireless strain sensor based on RFID technology is described. The sensor is a patch antenna that resonates at a certain frequency, which shifts in presence of strain. The relation between the resonance frequency and the strain is approximately linear. The slope of the relation is called sensitivity. The behavior of the sensor's sensitivity is studied using experimental work and simulations that couple electromagnetism and mechanics. The sensitivity measured in experiments and in simulations in presence of uniform strain is different. This difference is lower for the sensitivity in presence of a crack, probably due to a parameter variation that is currently not accurately modeled in the simulations.

Ziel der vorliegenden Arbeit war die Entwicklung eines Sensors zur gleichzeitigen Bestimmung von Spannungsintensitätsfaktoren (K-Faktoren) und der Risslage. Im Gegensatz zum Stand der Technik werden zwei Aufgaben zur Charakterisierung der Risse (Detektierung von Risslage und Größe und Quantifizierung des Beanspruchungszustandes an der Rissspitze) mit Hilfe nur eines Sensors ermöglicht. Es wurde effiziente, kostengünstige und leicht zu applizierende Technik zur experimentellen bruchmechanischen Beanspruchungsanalyse entwickelt. Eine Struktur mit Riss wird großflächig mit einer piezoelektrischen Polyvinylidenfluorid-Folie (PVDF-Folie) beklebt und eine ausreichende Anzahl von Elektroden befindet sich im Bereich des möglichen/vorhandenen Risses. Die an den Elektroden abgegriffenen elektrischen Potentiale bzw. Ladungen sind ein Maß für die Verzerrungen an der Oberfläche des Bauteils und ermöglichen die Berechnung der bruchmechanischen Größen am Riss. Dabei beschränkte man sich auf die Anwendung des Sensorkonzeptes im Rahmen der Scheiben- und Plattentheorie.