Le paradigme de la surveillance de santé structurale repose sur l'introduction d'indicateurs fiables et robustes permettant de détecter, localiser, quantifier et prédire un endommagement de manière précoce. En effet, la détection d'une modification structurale susceptible de devenir critique peut éviter l'occurrence de dysfonctionnements majeurs associés à des conséquences sociales, économiques et environnementales très importantes.Ces dernières années, de nombreuses recherches se fait de l'évaluation dynamique un élément de diagnostic. La plupart des méthodes reposent sur une analyse temporelle ou fréquentielle des signaux pour en extraire une information compressée au travers de quelques caractéristiques modales ou d'indicateurs évolués construits sur ces caractéristiques. Ces indicateurs ont montré leur efficacité, mais le problème de leur sensibilité, de la nécessité de disposer d'un état de référence, et de leur fiabilité en terme de la probabilité de détection et de fausses alarmes, reste entier. De plus, le fait d'utiliser des mesures dynamiques (particulièrement si plusieurs voies de mesures sont considérées) mène au stockage de grands volumes de données.Dans ce contexte, il est important d'employer des techniques permettant d'utiliser autant des données brutes que les propriétés modales de manière pratique et pertinente. Pour cela, des représentations adaptées ont été développées pour améliorer la manipulation et le stockage des données. Ces représentations sont connues sous le nom de og données symboliques fg . Elles permettent de caractériser la variabilité et l'incertitude qui entachent chacune des variables. Le développement de nouvelles méthodes d'analyse adéquates pour traiter ces données est le but de l'Analyse de Données Symboliques (ADS).L'objectif de cette thèse est double : le premier consiste à utiliser différentes méthodes couplées à l'ADS pour détecter un endommagement structural. L'idée est d'appliquer des procédures de classification non supervisée (e.g. divisions hiérarchiques, agglomérations hiérarchiques et nuées dynamiques) et supervisée (e.g., arbres de décision Bayésiens, réseaux de neurones et machines à vecteurs supports) afin de discriminer les différents états de santé d'une structure. Dans le cadre de cette thèse, l'ADS est appliquée aux mesures dynamiques acquises emph{in situ} (accélérations) et aux paramètres modaux identifiés. Le deuxième objectif est la compréhension de l'impact des effets environnementaux, notamment de ceux liés à la variation thermique, sur les paramètres modaux. Pour cela, des techniques de régression des données sont proposées.Afin d'évaluer la pertinence des démarches proposées, des études de sensibilité sont menées sur des exemples numériques et des investigations expérimentales. Il est montré que le couplage de l'ADS aux méthodes de classification de données permet de discriminer des états structuraux avec un taux de réussite élevé. Par ailleurs, la démarche proposée permet de vérifier l'importance d'utiliser des techniques permettant de corriger les propriétés modales identifiées des effets thermiques, afin de produire un processus de détection d'endommagements efficace / The paradigm of structural health monitoring is based on the development of reliable and robust indicators able to detect, locate, quantify and predict damage. Studies related to damage detection in civil engineering structures have a noticeable interest for researchers in this area. Indeed, the detection of structural changes likely to become critical can avoid the occurrence of major dysfunctions associated with social, economic and environmental consequences.Recently, many researches have focused on dynamic assessment as part of structural diagnosis. Most of the studied techniques are based on time or frequency domain analyses to extract compressed information from modal characteristics or based on indicators built from these parameters. These indicators have shown their potentialities, but the problem of their sensitivity, the necessity of a reference state, and their reliability in terms of detection probability and false alarm, still remains. Moreover, the use of raw dynamic measurements (especially if several measurement channels are considered) leads to the storage of large datasets.In this context, it is important to use techniques capable of dealing not only with raw data but also modal parameters in a practical and relevant way. In order to give some insights to this problem, appropriate representations have been developed to improve both manipulation and storage of data. These representations are known as og symbolic data fg. They are used to characterize the variability and uncertainty that exists within each variable. The development of new methods capable of dealing with this type of data is the goal of Symbolic Data Analysis (SDA).This thesis has two main objectives: the first one is to use different methods coupled with the SDA to detect structural damage. The idea is to employ clustering procedures (e.g., hierarchy-divisive, hierarchy-agglomerative and dynamic clouds) and supervised classification methods (e.g., Bayesien decision trees, neural networks and support vector machines) to discriminate different structural states. In this thesis, SDA is applied to dynamic measurements obtained on site (accelerations) and to the identified modal parameters. The second goal is to study the impact of environmental effects, particularly those related to thermal variation over modal parameters. To this end, a couple of regression techniques are proposed.In order to attest the efficiency of the proposed approaches, several sensibility studies considering numerical applications and experimental investigations are carried out. It is shown that SDA coupled with classification methods is able to distinguish structural conditions with adequate rates. Furthermore, it is stressed the importance of using techniques capable of correcting modal parameters from thermal effects in order to build efficient procedures for damage detection

<div> <p>Fiber reinforced polymer composites are widely used in manufacturing advanced light weight structures for the aerospace, automotive, and energy sectors owing to their superior stiffness and strength. With the increasing use of composites, there is an increasing need to monitor the health of these structures during their lifetime. Currently, health monitoring in filament wound composites is facilitated by embedding piezoelectrics and optical fibers in the composite during the manufacturing process. However, the incorporation of these sensing elements introduces sites of stress concentration which could lead to progressive damage accumulation. In addition to introducing weak spots in the structure, they also make the manufacturing procedure difficult. </p> <p> </p> <p>Alternatively, nanofiller modification of the matrix imparts conductivity which can be leveraged for real time health monitoring with fewer changes to the manufacturing method. Well dispersed nanofillers act as an integrated sensing network. Damage or strain severs the well-connected nanofiller network thereby causing a local change in conductivity. The self-sensing capabilities of these modified composites can be combined with low cost, minimally invasive imaging modalities such as electrical impedance tomography (EIT) for damage detection. To date, however, EIT has exclusively been used for damage detection in planar coupons. These simple plate-like structures are not representative of real-world complex geometries. This thesis advances the state of the art in conductivity-based structural health monitoring (SHM) and nondestructive evaluation (NDE) by addressing this limitation of EIT. The current study will look into damage detection of a non-planar multiply connected domain – a filament-wound glass fiber/epoxy tube modified by carbon black (CB) filler. The results show that EIT is able to detect through holes as small as 7.94 mm in a tube with length-to-diameter ratio of 132.4 mm-to-66.2 mm (aspect ratio of 2:1). Further, the sensitivity of EIT to damage improved with decreasing tube aspect ratio. EIT was also successful in detecting sub-surface damage induced by low velocity impacts. These results indicate that EIT has much greater potential for composite SHM and NDE than prevailing work limited to planar geometries suggest.</p> </div> <br>

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.

"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."

Ce travail de thèse concerne le contrôle santé intégré (SHM : Structural Health Monitoring) de structures composites aéronautiques par ondes guidées avec des transducteurs piézoélectriques (PZT). La majorité des méthodes de détection classiques reposent sur la comparaison de signaux issus de la structure inspectée à l’état courant avec ceux mesurés dans un état sain (la baseline). La température altère significativement les signaux mesurés et le diagnostic associé si son influence n’est pas prise en compte dans la baseline. D’autre part, l’acquisition de la baseline est très contraignante en vue d’un déploiement des systèmes SHM en condition réelles. La première contribution de cette thèse est l’estimation du champ de température à partir des mesures des PZTs (décalage du spectre fréquentiel et capacité statique), qui permet de compenser l’effet de la température dans la baseline sans ajouter de capteurs dédiés. La seconde contribution concerne les méthodes sans état de référence (baseline free). Les performances de détection de quatre méthodes sont comparées (rupture de réciprocité, variation d’amplitude, analyse des modes de Lamb et baseline instantanée) sur un modèle numérique et des cas expérimentaux d’endommagement à différentes températures sur une plaque de composite fortement anisotrope. Les résultats obtenus démontrent que la décomposition des modes de Lamb dans les signaux mesurés par l’intermédiaire de dual PZTs (PZTs constitués de deux électrodes concentriques – un anneau et un disque – sur leur face supérieure) permet d’améliorer de façon significative les performances de détection de ces méthodes. Un processus de dimensionnement du réseau de dual PZTs est proposé pour le déploiement de ces méthodes sur des structures complexes et prenant en compte la forte anisotropie des matériaux. Ces résultats ouvrent des perspectives prometteuses contribuant potentiellement au transfert des technologies de SHM des laboratoires vers l’industrie. / This thesis work concerns the Structural Health Monitoring (SHM) of aeronautical composite structures by guided waves with piezoelectric transducers (PZT). Conventional detection methods are based on the comparison of signals from the inspected structure in the current state with those measured in a healthy state (the baseline). Temperature significantly alters the measured signals and the associated diagnosis if its influence is not considered in the baseline. Also, the acquisition of the baseline is very constraining for the deployment of SHM systems in real conditions. The first contribution of this thesis is the estimation of the temperature field from the PZT measurements (modal frequency shift and static capacity), which allows to compensate the effect of temperature in the baseline without adding dedicated sensors. The second contribution of this thesis concerns baseline free methods. The detection performance of four methods are compared (reciprocity principle, amplitude variation, Lamb mode analysis and instantaneous baseline) on a numerical model and experimental cases of damages at different temperatures on a highly anisotropic composite plate. The results obtained show that the decomposition of Lamb wave modes in signals measured via dual PZTs (PZTs consisting of two concentric electrodes - a ring and a disk - on their upper side) significantly improves the detection performance of these methods. A dimensioning process for the deployment of these methods on complex anisotropic structures is proposed. These results open up promising opportunities that potentially contribute to the transfer of SHM technologies from laboratories to industry.

Les structures composites sont souvent exposées à des ambiances dynamiques plus oumoins sévères. Ces vibrations peuvent développer différentes formes d’endommagement(fracture des fibres, délamination, fissuration de la matrice. . . ). Les défauts locaux sepropagent et affectent les propriétés mécaniques de la structure modifiant ainsi soncomportement dynamique global. Ces changements peuvent induire une dégradationrapide de la structure et une réduction de sa durée de vie. La thèse a pour objectif lamise en oeuvre de modèles de comportement pour le dimensionnement de structurescomplexes intégrant des sous-structures composites susceptibles d’être endommagées.La méthode des éléments finis est utilisée pour modéliser le comportement vibratoirelinéaire et non-linéaire de ces structures et l’endommagement est introduit via un modèlebilatéral, dans un premier temps. Durant le processus de résolution, une des difficultésrencontrées est le coût de calcul très élevé. Ainsi, un méta-modèle a été développé basésur les réseaux de neurones artificiels couplé avec la méthode de condensation par sousstructurationde Craig-Bampton. Les réseaux de neurones artificiels permettent d’estimer,à moindre cout numérique, le niveau d’endommagement sans avoir recours au calculexact. Le modèle d’endommagement bilatéral n’est pas adapté au cas de chargementsalternés ou périodiques. Par conséquent, la deuxième partie de la thèse est orientée versle développement d’un modèle d’endommagement unilatéral qui donne une meilleuredescription du comportement mécanique lorsque les micro-fissures sont fermées. De plus,dans plusieurs applications industrielles, les structures composites utilisées sont de faibleépaisseur. Par conséquent, elles peuvent avoir naturellement un comportement vibratoirenon-linéaire de type grands déplacements. Le modèle de comportement dynamique engrands déplacements et en présence de la non-linéarité matérielle d’endommagement estdéveloppé et validé. A l’issue de ces travaux de thèse, un outil numérique implémentésur MATLAB® a été développé intégrant deux modèles d’endommagement, bilatéralet unilatéral et une méta-modélisation permettant la localisation et l’estimation del’endommagement ainsi que la prédiction de la réponse dynamique des structures composites, totalement ou localement, endommagées. Le méta-modèle proposé permet deréduire significativement le coût de calcul tout en assurant une bonne précision en termesde localisation et d’estimation du niveau d’endommagement. Cet outil peut s’avérer utilepour diverses applications dans le domaine de surveillance de l’état de santé des structurescomposites. / Composite structures are often exposed to more or less severe dynamic perturbations.These vibrations can develop different forms of damage (fiber fracture, delamination,cracking of the matrix, etc.). Local defects propagate and affect the mechanical propertiesof the structure resulting to modify its global dynamic behavior. These changes can leadto the degradation of the structure and the reduction in its lifetime. This thesis focuseson the implementation of behavior models for the dimensioning of complex structuresintegrating damaged composite sub-structures. The finite element method is used tomodel the linear and nonlinear vibration behavior of these structures where the damageis introduced, initially, via a bilateral model. Since the high computational costs duringthe solving process, a meta-model was developed based on artificial neural networkscoupled with the condensation method of Craig-Bampton. Artificial neural networkspermit to estimate the damage severity at a lower numerical cost without resorting toexact calculation. The bilateral damage model is not adapted to the case of periodic loads.Consequently, the second part of the thesis is oriented towards the development of aunilateral damage model which gives a better description of the mechanical behaviorwhen the micro-cracks are closed. Moreover, in several industrial applications, the usedcomposite structures have small thickness. Therefore, they can naturally have a geometricnon-linear dynamic behavior. The model of dynamic behavior in large displacements andin the presence of material non-linearity of damage is developed and validated. At theend of this thesis, a numerical tool implemented on MATLAB® software was developedintegrating two models of damage, bilateral and unilateral, and a meta-modeling allowingthe localization and the estimation of the damage as well as the prediction of the linear andnon-linear dynamic responses of composite structures, totally or locally, damaged. Theproposed meta-model reduces significantly the computational cost and ensuring a goodaccuracy in terms of localization and estimation of the damage severity. Thereby, thistool can be useful in life-time estimation and monitoring strategies of composite structures.Thèse de

Orientador: Gilberto Pechoto de Melo / Resumo: As metodologias de monitoramento da integridade estrutural baseadas em observadores de estado, em sua grande maioria, utilizam o resíduo obtido a partir da diferença entre a medida e a estimativa de dada resposta dinâmica do sistema para o processo de detecção de danos. Contudo, em determinadas situações, tem-se interesse em realizar o monitoramento através de certa resposta dinâmica que não pode ser medida diretamente. Desta forma, a principal contribuição deste trabalho é propor uma metodologia de detecção de danos para sistemas mecânicos, cujo resíduo é obtido a partir da diferença entre as estimativas do comportamento dinâmico de determinada região do sistema. Estas estimativas são geradas por dois observadores de estado de ordem plena em paralelo, ambos projetados a partir do modelo físico-matemático do sistema em monitoramento sem danos, cujos os ganhos ótimos são determinados pelo método LQR, do inglês Linear Quadratic Regulator. A diferença entre os observadores consiste em serem baseados em conjuntos de medidas distintos. Simulações computacionais são apresentadas para demonstrar a aplicação desta metodologia, de maneira que são discutidas as vantagens e desvantagens em monitorar o sistema utilizando diferentes tipos de força de excitação. Os resultados obtidos são satisfatórios para a detecção dos tipos de dano considerados neste trabalho. / Abstract: Structural health monitoring methodologies based on state observers, for the most part, use the residual obtained from the di erence between the measurement and the estimate of the given dynamic response of the system to the damage detection process. However, in certain situations, it is interesting to carry out the monitoring through a certain dynamic response that can not be measured directly. In this way, the main contribution of this work is to propose a methodology of damage detection for mechanical systems, whose residue is obtained from the di erence between the estimates of the dynamic behavior of a certain region of the system. These estimates are generated by two parallel full-order state observers, both designed from the physical-mathematical model of the monitoring system without damages, whose optimal gains are determined by the LQR (Linear Quadratic Regulator) method. The di erence between observers is that they are based on di erent sets of measures. Computational simulations are presented to demonstrate the application of this methodology, so that the advantages and disadvantages of monitoring the system using di erent types of excitation force are discussed. The results obtained are satisfactory for the detection of the types of damage considered in this work. / Mestre

North American civil infrastructures are aging. According to recent (2016) Canadian infrastructure report card, 33% of the Canadian municipal infrastructures are either in fair or below fair condition. The current deficit of replacing fair and poor municipal bridges (covers 26% of bridges) is 13 billion dollars. According to the latest report (2017) by American Society of Civil Engineers, the entire American infrastructure have been given a D+ condition rating. This includes some of the structural elements of infrastructures that pose a significant risk and there is an urgent need for frequent and effective inspection to ensure the safety of people. Visual inspection is a commonly used technique to detect and identify surface defects in bridge structures as it has been considered the most feasible method for decades. However, this currently used methodology is inadequate and unreliable as it is highly dependent on subjective human judgment. This labor-intensive approach for inspection requires huge investment in terms of an arrangement of temporary scaffoldings/permanent platforms, ladders, snooper trucks, and sometimes helicopters. To address these issues associated with visual inspection, the completed research suggests three innovative methods; 1) Combined use of Fuzzy logic and Image Processing Algorithm to quantify surface defects, 2) Unmanned Aerial Vehicle (UAV)-assisted American Association of State Highway and Transportation Officials (AASHTO) guideline-based damage assessment technique, and 3) Patent-pending multi-device robotics-based sensor data acquisition system for mapping and assessing defects in civil structures. To detect and quantify subsurface defects such as voids and delamination using a UAV system, another patent-pending UAV-based acoustic method is developed. It is a novel inspection apparatus that comprises of an acoustic signal generator coupled to a UAV. The acoustic signal generator includes a hammer to produce an acoustic signal in a structure using a UAV. An outcome of this innovative research is the development of a model to refine multiple commercially available NDT techniques’ data to detect and quantify subsurface defects. To achieve this, a total of nine 1800 mm × 460 mm reinforced concrete slabs with varying thicknesses of 100 mm, 150 mm and 200 mm are prepared. These slabs are designed to have artificially simulated defects like voids, debonding, honeycombing, and corrosion. To determine the performance of five NDT techniques, more than 300 data points are considered for each test. The experimental research shows that utilizing multiple techniques on a single structure to evaluate the defects, significantly lowers error and increases accuracy compared to that from a standalone test. To visualize the NDT data, two-dimensional NDT data maps are developed. This work presents an innovative method to interpret NDT data correctly as it compares the individual data points of slabs with no defects to slabs with simulated damage. For the refinement of NDT data, significance factor and logical sequential determination factor are proposed. / Graduate / 2020-09-06

An automated method in damage state assessment of reinforced concrete columns for the purpose of establishing a rapid and quantitative post-earthquake safety and structural evaluation procedure is proposed. Several techniques from the fields of computer vision and image processing are employed in order to develop a set of methods capable of automatically detecting spalled regions on the surface of reinforced concrete columns as well as the properties of cracks and spalled regions on these surfaces. The resulting properties of the observed visible damage on the reinforced concrete column surfaces are then utilized to automatically estimate the existing condition and safety of the column. The damage state is quantified according to the maximum drift capacity of the column. The methods proposed in this research were implemented in a Microsoft Visual Studio .NET environment, and tested on real images of damaged columns. The test results indicated that the methods could automatically detect spalled regions and retrieve the properties of spalling and cracks on reinforced concrete column surfaces in images or video frames, and further, that this retrieved information could be accurately translate to a meaningful assessment of the column's existing damage state in the form of the maximum drift capacity.

In this study, a combined genetic algorithm and non-linear optimisation system is designed and used in the identification of structural damage of a cantilever isotropic beam regarding its location and severity. The vibration-based features, both natural frequencies (i.e. eigenvalues) and displacement mode shapes (i.e. eigenvectors) of the structure in the first two out of plane bending modes, are selected as damage features for various types of damage comprising saw-cut and impact. For this purpose, commercial finite element modelling (FEM) and analysis software Msc. Patran/Nastran&reg / is used to obtain the aforementioned features from intact and damaged structures. Various damage scenarios are obtained regarding saw-cut type damage which is modelled as change in the element thicknesses and impact type damage which is modelled as a reduction of the elastic modulus of the elements in the finite element models. These models are generated by using both 1-D bar elements and 2-D shell type elements in Msc. Patran&reg / and then normal mode analyses are performed in order to extract element stiffness and mass matrices by using Msc. Nastran&reg / . Sensitivity matrices are then created by changing the related properties (i.e. reduction in elastic modulus and thickness) of the individual elements via successive normal mode analyses. The obtained sensitivity matrices are used as coefficients of element stiffness and/or mass matrices to construct global stiffness and/or mass matrices respectively. Following this, the residual force vectors obtained for different damage scenarios are minimised via a combined genetic algorithm and non-linear optimisation system to identify damage location and severity. This minimisation procedure is performed in two steps. First, the algorithm tries to minimise residual force vector (RFV) by only changing element stiffness matrices by aiming to detect impact type damage, as elastic modulus change is directly related to stiffness matrix. Secondly, it performs a minimisation over RFV by changing both element stiffness and mass matrices which aims to detect saw-cut type damage where thickness change is a function of both stiffness and mass matrices. The prediction of the damage type is then made by comparing the objective function value of these two steps. The lowest value (i.e. the fittest) indicates the damage type. The results of the minimisation also provide value of intactness where one representing intact and any value lower than one representing damage severity. The element related to that particular intactness value indicates the location of the damage on the structure. In case of having intactness values which are lower than one in value at various locations shows the existence of multi damage cases and provides their corresponding severities. The performance of the proposed combined genetic algorithm and non-linear optimisation system is tested on various damage scenarios created at different locations with different severities for both single and multi damage cases. The results indicate that the method used in this study is an effective one in the determination of type, severity and location of the damage in beam-like structures.