The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings. Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques. / <p>QC 20140910</p>

O objetivo deste trabalho é estudar métodos dinâmicos de detecção de dano em vigas, em especial os métodos baseados na variação da flexibilidade medida dinamicamente. Os métodos revisados formam parte das técnicas de Detecção de Dano Não Destrutivas (DDND). Nas técnicas DDND o dano é localizado por comparação entre o estado sadio e o danificado da estrutura. Neste trabalho, o problema de vibração inverso é apresentado e a matriz de flexibilidade estática da estrutura é determinada a partir de seus parâmetros modais. Com ajuda de um Modelo de Elementos Finitos (MEF) são mostrados os diferentes padrões de variação da matriz de flexibilidade produzidos pela presença do dano. Baseando-se nestes padrões é possível identificar a posição do dano dentro da estrutura, como indicado pelos diversos exemplos apresentados. / The purpose of this work is to study dynamic methods for damage detection in beam structures. The attention is devoted to the methods based on dynamically measured flexibility. The reviewed methods are part of Nondestructive Damage Detection techniques (NDD). In the NDD techniques the damage is determined through the comparison between the undamaged and damaged state of the structure. In this work the inverse vibration problem is presented and the structure\'s flexibility matrix calculated from his modal parameters. The Finite Elements Model (FEM) is employed to show that a clear pattern exist for the changes in the flexibility matrix produced due to the presence of damage. The flexibility matrix changes is used to identify and locate damage as indicated by the several examples presented.

Ce travail de thèse concerne la surveillance de l’état de santé de structures complexes en service. Elle est appliquée à des éléments d’une nacelle d’avion gros porteur. Ce travail est original et s’inscrit dans le cadre d’un projet, coordonné par AIRBUS Operations SAS et porté par AIRCELLE (Groupe SAFRAN). Les principales parties de la nacelle visées par notre démarche sont le capot de soufflante (fan cowl, composite monolithique) et la structure interne fixe du capot coulissant de l’inverseur de poussée (IFS, sandwich nid d’abeille). Ces structures réalisées en matériaux composites sont sujettes à de nombreux modes de dégradation(rupture de fibres, délaminage, fissures, etc…), qui peuvent impacter la durée de vie de la nacelle. De plus elles sont exposées à de nombreuses sollicitations environnementales dont des variations thermiques importantes (de -55 °C à +120°C). L’objectif de ce travail est la mise en place d’un système SHM visant à suivre l’état de santé de ces structures afin de détecter l’apparition de tels endommagements et de les localiser avant qu’ils ne conduisent à une dégradation de la structure; ceci de manière à permettre une maintenance prédictive. Des capteurs et actionneurs piézoélectriques (PZT) sont collés sur la structure et sont utilisés pour générer des ondes de Lamb et effectuer des mesures. La démarche SHM proposée s’appuie sur des mesures successives en partant d’un état initial considéré comme sain, puis en réalisant régulièrement des mesures de suivi. La différence entre des signaux mesurés pour deux états est analysée afin d’en extraire des caractéristiquessensibles à l’apparition de dommages. Après validation, des PZT ont été collés sur le fan cowl et l’IFS ainsi que sur des coupons et un banc d’essai approprié a été conçu afin de valider notre démarche. Du fait que l’on est amené à travailler sur des différences de signaux, des algorithmes de détection, basés sur les testsd’hypothèses statistiques et l’Analyse en Composantes Principales (ACP), ont dû être développés et validés. Ceci a d’abord été testé pour la détection de dommages contrôlés introduits d’abord dans des coupons, puis dans le fan cowl et dans l’IFS. Des algorithmes robustes (y compris aux variations de température) de localisation de ces dommages, basés sur l’extraction des temps de vol des ondes de Lamb, ont été développés et validés sur les structures étudiées. Une approche de quantification des incertitudes sur la localisation par inférence Bayésienne a été proposée en complément de la démarche déterministe implémentée. / This work aims at designing a Structural Health Monitoring (SHM) system for complex composite structures, with an application to elements of aeronautical nacelles. This work is original and is in the framework of a project, coordinated by AIRBUS Operations SAS and headed by AIRCELLE (SAFRAN Group). The main parts of the nacelle concerned with our approach are the fan cowl (composite monolithic) and the inner fixed structure (IFS, sandwich structure with honeycomb core) of the thrust reverser. These structures made from composite materials are subjected to many damages types which can affect nacelle’s useful life (fiber breaking, delamination, crack, etc…). Furthermore these structures are exposed to many environmental constraints which are for instance important thermal variations (from -55°C to +120°C). The objective of this work is to develop a SHM system aimed at detecting and localizing these damages, before the degradation of the whole structureoccurs. Piezoelectric (PZT) actuators and sensors are bonded on the structure and they are used to generate Lamb wave signals and perform measurements. The proposed SHM approach is based on successive measurements starting from an initial state, considered as healthy and regularly conducting follow-up. The difference in signals measured between two states is analyzed in order to extract some damages-sensitivesfeatures. After validation, PZT elements were glued to the fan cowl and to the IFS as well as on representative coupons and a suitable test bench is designed in order to validate our approach. Since one has to work on difference in signals, damage detection algorithms based on statistical hypothesis testing and PrincipalComponent Analysis (PCA) have been developed and validated. This was first tested for the detection of controlled damages introduced in coupons, and thereafter on the fan cowl and IFS. Robust damage localization algorithms (including with temperature variations) based on Time-of-flight (ToF) extraction from difference in signals, were developed and validated for these structures. A Bayesian approach for uncertainties quantification in the damage localization is also developed, leading to more accuracy in the damage localization results.

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Orientador: Samuel da Silva / Abstract: Structural Health Monitoring (SHM) methodologies aim to develop techniques able to detect, localize, quantify and predict the progress of damages in civil, aerospatial and mechanical structures. In the hierarchical process, the damage detection is the first and most important step. Despite the existence of numerous methods of damage detection based on vibration signals, two main problems can complicate the application of classical approaches: the nonlinear phenomena and the uncertainties. This thesis demonstrates the importance of the use of a stochastic nonlinear model in the damage detection problem considering the intrinsically nonlinear behavior of mechanical structures and the measured data variation. A new stochastic version of the Volterra series combined with random Kautz functions is proposed to predict the behavior of nonlinear systems, considering the presence of uncertainties. The stochastic model proposed is used in the damage detection process based on hypothesis tests. Firstly, the method is applied in a simulated study assuming a random Duffing oscillator exposed to the presence of a breathing crack modeled as a bilinear oscillator. Then, an experimental application considering a nonlinear beam subjected to the presence of damage with linear characteristics (loss of mass in a bolted connection) is performed, with the direct comparison between the results obtained using a deterministic and a stochastic model. Finally, an experimental application considering a n... (Complete abstract click electronic access below) / Resumo: As metodologias de Monitoramento da Integridade Estrutural (SHM) visam desenvolver técnicas capazes de detectar, localizar, quantificar e prever o progresso de danos em estruturas civis, aeroespaciais e mecânicas. Nesse processo hierárquico, a detecção de danos é o primeiro e mais importante passo. Apesar da existência de inúmeros métodos de detecção de danos baseados em sinais de vibração, dois problemas principais podem complicar a aplicação de abordagens clássicas: os fenômenos não lineares e as incertezas. Esta tese demonstra a importância do uso de um modelo não linear estocástico no problema de detecção de danos, considerando o comportamento intrinsecamente não linear de estruturas mecânicas e a variação dos dados medidos. Uma nova versão estocástica das séries de Volterra, combinada com funções aleatórias de Kautz, é proposta para prever o comportamento de sistemas não lineares, considerando a presença de incertezas. O modelo estocástico proposto é utilizado no processo de detecção de danos com base em testes de hipótese. Primeiramente, o método é aplicado em um estudo simulado, assumindo um oscilador Duffing aleatório exposto à presença de uma trinca respiratória modelada como um oscilador bilinear. Em seguida, uma aplicação experimental é realizada considerando uma viga não linear sujeita à presença de um dano com características lineares (perda de massa em uma conexão parafusada), com a comparação direta entre os resultados obtidos utilizando um modelo determinístic... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

The field of monitoring is one of rapid development. Advances in sensor technologies, in data communication paradigms and data processing algorithms all influence the possibilities of Structural Health Monitoring, damage detection, traffic monitoring and other implementations of monitoring systems. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining the serviceability and deterioration of bridges. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening work. Many bridges constitute a bottleneck in the transport network they serve with few or no alternative routes. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and three appended papers. The thesis presents advances in sensor technology, damage identification algorithms and Bridge Weigh-In-Motion techniques. Two case studies are carried out. In the first a bridge and traffic monitoring system is implemented in a highway suspension bridge to study the cause of unexpected wear in the bridge bearings. In the second a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. / QC 20111122

This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is currently extremely high. This licentiate thesis presents a two-stage model-free damage detection approach based on Machine Learning. The method is applied to data gathered in a numerical experiment using a three-dimensional finite element model of a railway bridge. The initial step in this study consists in collecting the structural dynamic response that is simulated during the passage of a train, considering the bridge in both healthy and damaged conditions. The first stage of the proposed algorithm consists in the design and unsupervised training of Artificial Neural Networks that, provided with input composed of measured accelerations in previous instants, are capable of predicting future output acceleration. In the second stage the prediction errors are used to fit a Gaussian Process that enables to perform a statistical analysis of the distribution of errors. Subsequently, the concept of Damage Index is introduced and the probabilities associated with false diagnosis are studied. Following the former steps Receiver Operating Characteristic curves are generated and the threshold of the detection system can be adjusted according to the trade-off between errors. Lastly, using the Bayes’ Theorem, a simplified method for the calculation of the expected cost of the strategy is proposed and exemplified. / <p>QC 20170420</p>

Structural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches. Effective use of traffic video synchronized with sensor measurements for decision-making is demonstrated. First, some of the computer vision methods and how they can be used for bridge monitoring are presented along with the most common issues and some practical solutions. Second, a conceptual damage index (Unit Influence Line) is formulated using synchronized computer images and sensor data for tracking the structural response under various load conditions. Third, a new index, Nd , is formulated and demonstrated to more effectively identify, localize and quantify damage. Commonly observed damage conditions on real bridges are simulated on a laboratory model for the demonstration of the computer vision method, UIL and the new index. This new method and the index, which are based on outlier detection from the UIL population, can very effectively handle large sets of monitoring data. The methods and techniques are demonstrated on the laboratory model for damage detection and all damage scenarios are identified successfully. Finally, the application of the proposed methods on a real life structure, which has a monitoring system, is presented. It is shown that these methods can be used efficiently for applications such as damage detection and load rating for decision-making. The results from this monitoring project on a movable bridge are demonstrated and presented along with the conclusions and recommendations for future work.

Existing methods of evaluating the structural system of a building after a seismic event consist of removing architectural elements such as drywall, cladding, insulation, and fireproofing. This method is destructive and costly in terms of downtime and repairs. This research focuses on removing the guesswork by using forced vibration testing (FVT) to experimentally determine the health of a building. The experimental structure is a one-story, steel, bridge-like structure with removable braces. An engaged brace represents a nominal and undamaged condition; a dis-engaged brace represents a brace that has ruptured thus changing the stiffness of the building. By testing a variety of brace configurations, a set of experimental data is collected that represents potential damage to the building after an earthquake. Additionally, several unknown parameters of the building’s substructure, lateral-force-resisting-system, and roof diaphragm are determined through FVT. A suite of computer models with different levels of damage are then developed. A quantitative analysis procedure compares experimental results to the computer models. Models that show high levels of correlation to experimental brace configurations identify the extent of damage in the experimental structure. No testing or instrumentation of the building is necessary before an earthquake to identify if, and where, damage has occurred.

Crack, a typical term most people know, is a common form of distress or damage in road pavements and railway sleepers. It poses significant challenges to their structural integrity, safety, and longevity. Over the years, researchers have developed various data-driven technologies for image-based crack detection in road and sleeper applications. The image-based crack detection has become a promising field.  Many researchers use ensemble learning to win the Road Damage Detection Challenge. The challenge provides a street view dataset from several countries from different perspectives. The version of the dataset is 2020, which contains images from Japan, India, and Czech. Thus, the dataset inherits a domain shift problem. Current solutions use ensemble learning to deal with such a problem. Those solutions require much computational power and challenge adaptability in real-time applications. To mitigate the problem, the thesis experiments with various data augmentation techniques that could improve the base model performance. The main focuses are erasing a crack from an image using generative AI (Erase), implementing road segmentation by using the Panoptic Segmentation (RS) and injecting a perspective-aware synthetic crack (InjectPa) into the segmented road surface in the image. The results show that compared to the base model, the Erase + RS techniques improve the model's F1 score when trained only on Japan in the dataset rather than when trained on three countries simultaneously. Moreover, the InjectPa technique does not help improve the base model in both scenarios. Then, the experiment moved to the SBB dataset containing close-up images of sleepers from cameras mounted in front of the diagnostic vehicle. This section follows the same techniques but changes the segmentation model to the Segment Anything Model (SAM) because the previous segmentation model was trained on a street view dataset, making it vulnerable to close-up images. The Erase + SAM techniques show improvement in bbox/AP and validation loss. Nevertheless, it does not improve the F1 score significantly compared to the base model.  This thesis also applies the explainable AI name D-RISE to determine which feature most influences the model decision. D-RISE shows that the augmentation model can pay attention to the damage type pothole for road pavements and defect type spalling for sleepers than other types. Finally, the thesis discusses the results and suggests a strategy for future study. / Sprickor, en typisk term som de flesta känner till, är en vänlig form av skador i vägbeläggningar och järnvägsslipers. Det innebär betydande utmaningar för strukturella integritet, säkerhet och livslängd. Under årens lopp har olika datadrivna tekniker utvecklats för bildbaserade sprickdetektering i vägbeläggningar och järnvägsslipers applikationer. Den bildbaserade sprickdetekteringen har blivit ett lovande område. Många forskare använder ensembleinlärningsmodeller för att vinna den Road Damage Detection Challenge (Vägbeläggningar Detektering Utmaning). Utmaningen ger en Gatuvy dataset från flera länder från olika perspektiv. Versionen av datasetet är 2020 som innehåller bilder från Japan, Indien och Tjeckien. Därför ärver datasetet  ett domänskiftproblem. Nuvarande lösningar använder ensembleinlärning för att hantera ett sådant problem. Dessa lösningar kräver mycket datorkraft och utmanar anpassningsförmågan i realtidsapplikationer. För att mildra problemet, denna avhandling prover många tekniker för dataökningar som kan förbättra basmodellens prestanda. Huvudfokusen är att radera en spricka från en bild via en generativ AI (Erase), implementera vägyta segmentering via den Panoptic Segmentation (RS), lägga en persective-aware syntetik spricka (InjectPa) till segmenterade vögytan in bilden. Resultaten visar att den Erase + RS ökningsteknikerna förbättrar modellens F1 score när den tränas på Japan i datasetet i stället för att tränas alla länder samtidigt. Dessutom förbättrar den InjectPa tekniken inte basmodellen på båda fallen.  Därefter flyttades experimentet till SBB-datasetet som innehåller närbilder av järnvägsslipers från kameror monterades framför ett diagnosfordon. Denna section följer de samma teknikerna men ändra segmentering modellen till den Segment Anything Model (SAM) eftersom förra segmentering modellen tränades på en Gatuvy dataset vilket gör den sårbar för närbilder. Den Erase + SAM ökningsteknikerna visar förbättringar på bbox/AP och validering. Ändå förbättrade den inte F1 score avsevört jämfört med basmodellen.  Denna avhandling tillämpar också Förklarbar AI-namnet D-RISE för att avgöra vilken funktion som mest påverkar modellbeslutet. D-RISE visar att modellen som har dataökning kan uppmärksamma skadetypen potthål för vägbeläggningar och defekttypen spjälkning för järnvägsslipers än andra typer. Slutligen diskuterar avhandlingen resultaten och föreslår en strategi för framtida arbetsinsatser.