Detecção de dano em estruturas de engenharia de grandes dimensões através da análise de suas características dinâmicas envolve diversos campos de estudo. O primeiro deles trata da identificação dos parâmetros modais da estrutura, uma vez que executar testes de vibração livre em tais estruturas não é uma tarefa simples, necessita-se de um método robusto que seja capaz de identificar os parâmetros modais dessa estrutura a ações ambientais, campo esse chamado de análise modal operacional. Este trabalho trata do problema de detecção de dano em estruturas que possam ser representadas através de modelos em pórticos planos e vigas e que estejam submetidos à ação de vibrações ambientais. A localização do dano é determinada através de um algoritmo de otimização conhecido como Backtracking Search Algorithm (BSA) fazendo uso de uma função objetivo que utiliza as frequências naturais e modos de vibração identificados da estrutura. Simulações e testes são feitos a fim de verificar a concordância da metodologia para ambos os casos. Para as simulações, são utilizados casos mais gerais de carregamentos dinâmicos, e dois níveis de ruído (3% e 5%) são adicionados ao sinal de respostas para que esses ensaios se assemelhem aos ensaios experimentais, onde o ruído é inerente do processo. Já nos ensaios experimentais, apenas testes de vibração livre são executados. Diversos cenários de dano são propostos para as estruturas analisadas a fim de se verificar a robustez da rotina de detecção de dano. Os resultados mostram que a etapa de identificação modal estocástica através do método de identificação estocástica de subespaço (SSI) teve ótimos resultados, possibilitando, assim, a localização da região danificada da estrutura em todos os casos analisados. / Damage detection in large dimensions engineering structures through the analysis of their dynamic characteristics involves several fields. The first one deals with the structure modal identification parameter, since running free vibration tests in such structures is not a simple task, robust methods are needed in order to identify the modal parameters of this structure under ambient vibrations, this field is known as operational modal analysis. This work deals with the problem of damage detection in structures under ambient vibrations that can be represented by FEM using frame and beam elements. The damage location is determined through an optimization algorithm know as Backtracking Search Algorithm (BSA). It uses as objective function the identified natural frequencies and modes of vibration of the structure. Numerical and experimental tests are performed to assess the agreement of the methodology for both cases. For the numerical tests, more general cases of dynamic loads are used, and two noise levels (3% and 5%) are added to the response signal to assessing the robustness of the methodology close to the field conditions, in which noise is inherent of the process. In the experimental tests, only free vibration tests are performed. Several damage scenarios are proposed for the analyzed structures to check the robustness of the damage detection routine. The results show that the stochastic modal identification using the stochastic subspace identification (SSI) method had excellent results, thus allowing the location of the damaged region of the structure in all analyzed cases.

abstract: Movement disorders are becoming one of the leading causes of functional disability due to aging populations and extended life expectancy. Diagnosis, treatment, and rehabilitation currently depend on the behavior observed in a clinical environment. After the patient leaves the clinic, there is no standard approach to continuously monitor the patient and report potential problems. Furthermore, self-recording is inconvenient and unreliable. To address these challenges, wearable health monitoring is emerging as an effective way to augment clinical care for movement disorders. Wearable devices are being used in many health, fitness, and activity monitoring applications. However, their widespread adoption has been hindered by several adaptation and technical challenges. First, conventional rigid devices are uncomfortable to wear for long periods. Second, wearable devices must operate under very low-energy budgets due to their small battery capacities. Small batteries create a need for frequent recharging, which in turn leads users to stop using them. Third, the usefulness of wearable devices must be demonstrated through high impact applications such that users can get value out of them. This dissertation presents solutions to solving the challenges faced by wearable devices. First, it presents an open-source hardware/software platform for wearable health monitoring. The proposed platform uses flexible hybrid electronics to enable devices that conform to the shape of the user’s body. Second, it proposes an algorithm to enable recharge-free operation of wearable devices that harvest energy from the environment. The proposed solution maximizes the performance of the wearable device under minimum energy constraints. The results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline. Third, a comprehensive framework for human activity recognition (HAR), one of the first steps towards a solution for movement disorders is presented. It starts with an online learning framework for HAR. Experiments on a low power IoT device (TI-CC2650 MCU) with twenty-two users show 95% accuracy in identifying seven activities and their transitions with less than 12.5 mW power consumption. The online learning framework is accompanied by a transfer learning approach for HAR that determines the number of neural network layers to transfer among uses to enable efficient online learning. Next, a technique to co-optimize the accuracy and active time of wearable applications by utilizing multiple design points with different energy-accuracy trade-offs is presented. The proposed technique switches between the design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. Finally, we present the first ultra-low-energy hardware accelerator that makes it practical to perform HAR on energy harvested from wearable devices. The accelerator consumes 22.4 microjoules per operation using a commercial 65 nm technology. In summary, the solutions presented in this dissertation can enable the wider adoption of wearable devices. / Dissertation/Thesis / Human activity recognition dataset / Doctoral Dissertation Computer Engineering 2020

<div>This dissertation broadly focuses on autonomous condition assessment of civil infrastructures using vision-based methods, which present a plausible alternative to existing manual techniques. A region-based convolutional neural network (Faster R-CNN) is exploited for the detection of various earthquake-induced damages in reinforced concrete buildings. Four different damage categories are considered such as surface crack, spalling, spalling with exposed rebars, and severely buckled rebars. The performance of the model is evaluated on image data collected from buildings damaged under several past earthquakes taking place in different parts of the world. The proposed algorithm can be integrated with inspection drones or mobile robotic platforms for quick assessment of damaged buildings leading to expeditious planning of retrofit operations, minimization of damage cost, and timely restoration of essential services. </div><div><br></div><div> </div><div> Besides, a computer vision-based approach is presented to track the evolution of a damage over time by analysing historical visual inspection data. Once a defect is detected in a recent inspection data set, its spatial correspondences in the data collected during previous rounds of inspection are identified leveraging popular computer vision-based techniques. A single reconstructed view is then generated for each inspection round by synthesizing the candidate corresponding images. The chronology of damage thus established facilitates time-based quantification and lucid visual interpretation. This study is likely to enhance the efficiency structural inspection by introducing the time dimension into the autonomous condition assessment pipeline.</div><div><br></div><div> </div><div> Additionally, this dissertation incorporates depth fusion into a CNN-based semantic segmentation model. A 3D animation and visual effect software is exploited to generate a synthetic database of spatially aligned RGB and depth image pairs representing various damage categories which are commonly observed in reinforced concrete buildings. A number of encoding techniques are explored for representing the depth data. Besides, various schemes for fusion of RGB and depth data are investigated to identify the best fusion strategy. It was observed that depth fusion enhances the performance of deep learning-based damage segmentation algorithms significantly. Furthermore, strategies are proposed to manufacture depth information from corresponding RGB frame, which eliminates the need of depth sensing at the time of deployment without compromising on segmentation performance. Overall, the scientific research presented in this dissertation will be a stepping stone towards realizing a fully autonomous structural condition assessment pipeline.</div>

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Das Exzellenzcluster MERGE in Chemnitz entwickelte ein Fertigungsverfahren, welches ein Werkstoff herstellt. Dieser Werkstoff ist vielseitig einsetzbar. So kann er z.B. in einem Automobil als ein modernes Steuerungselement dienen. Ein zentraler Bestandteil dieses Werkstoffes ist eine Schicht aus Piezokristallen. Aus diesen Kristallen besteht ebenfalls ein Piezosensor. Da der Piezosensor im Bereich des Structural Health Monitorings (SHM) oft verwendet wird, soll der Werkstoff auf seine Einsetzbarkeit als Sensor im diesen Bereich überprüft werden. Das SHM beschreibt die periodische Begutachtung des Bauteils auf das Vorhandensein eines Schadens. Bestimmt wird der Schaden indem Signaleigenschaften des vom Sensor aufgenommenen Signals untersucht werden. Anhand der Veränderung dieser Signaleigenschaften kann der Zustand des Bauteils bestimmt werden. In dieser Arbeit wird eine rechteckige Metallplatte aus Aluminium als Bauteil untersucht. Dieses Bauteil wurde zur Verstärkung der Steifigkeit mit einer Schicht aus Glasfasern beklebt. Angeregt und Beschädigt wird das Bauteil durch eine Prüfmaschine. Die Beschädigung des Bauteils erfolgt durch die Erhöhung der Intensität der Kraft. Als Signaleigenschaften werden in der Arbeit die Kurzzeitenergie und die Intensität der Frequenzanteile des aufgenommenen Signals vom Werkstoff untersucht.

Das vorgeschlagene SHM-System 'Adapted Metal Wire Based and fiber Oriented Sensor - AMBOS', basierend auf Drähten aus Metalllegierungen, ist vergleichsweise kostengünstig und verfügt über sehr gute Verarbeitungseigenschaften, insbesondere mit Eignung zur Integration in den Wickelprozess. Eine speziell entwickelte Abwickelvorrichtung erlaubt die direkte Verarbeitung der Drähte zusammen mit den Verstärkungsfasern und wärmehärtenden Harzsystemen im Wickelprozess. Insbesondere aufgrund der hohen Genauigkeit und der sehr niedrigen Material- und Verarbeitungskosten hat das beschriebene Verfahren ein großes Potenzial für den Einsatz in der automobilen Serienfertigung. Grundsätzlich sind die untersuchten Metalldrähte für eine solche Anwendung geeignet. Ein wesentlicher Vorteil ist die einfache Kompensation von thermischen Einflüssen. Weitere Untersuchungen zum Korrosionsschutz und zu Umwelteinflüssen stehen noch aus. / The proposed SHM system called “Adapted Metal wire Based and fiber Oriented Sensor – AMBOS”, based on wires from metal alloys, is comparatively inexpensive and has very good processing properties, in particular with suitability for integration into the winding process. A specially developed unwinding de-vice allows direct processing of the wires together with the reinforcing fibres and thermosetting resin systems in the winding process. Especially due to the high ac-curacy and the very low material and processing costs, the described process has great potential for use in automotive series production. In principle, the metal wires investigated are suitable for such an application. A significant advantage is the simple compensation of thermal influences. Further investigations on corrosion preservation and influences from the environment are still pending.

Dans le domaine de la construction, la détection automatisée et à distance de l’endommagementdes structures en béton est d’une importance capitale. En effet, lescontraintes économiques actuelles imposent une réduction des coûts de maintenancetandis que les impératifs en termes de sécurité et de qualité sont de plus en plus stricts.Dans le cadre de cette thèse, des transducteurs piézoélectriques intégrés sont utilisésafin de suivre en temps réel le comportement du béton. Ces transducteurs sont faitsde PZT, une céramique piézoélectrique particulièrement adaptée au suivi à l’aide d’ultrasonsde par ses faibles dimensions, son faible coût et la large bande de fréquenced’utilisation. Un système de monitoring ultrasonore à faible voltage et ultra rapide a étéentièrement conçu dans le cadre de cette thèse. Le système est basé sur des mesuresultrasonores bilatérales entre un émetteur et un récepteur. Le système d’acquisitiondes données développé permet d’atteindre jusqu’à 150 mesures par seconde et decalculer en temps réel un indice d’endommagement sur base des mesures effectuées.L’indice d’endommagement est basé sur la première partie de l’onde transmise (ondedirecte) plutôt que sur l’onde tardive. Le système a démontré qu’il est capable de détecterl’apparition de fissures dans le béton avant qu’elles ne soient visuellement apparenteset qu’il permet de suivre de suivre l’initiation de l’endommagement jusqu’à la rupturepour des mécanismes de fissuration très rapides, voire fragiles. Le fait d’intégrer lestransducteurs à l’intérieur de la structure permet potentiellement d’améliorer l’efficacitédes transducteurs ultrasonores à condition que les couches d’enrobage de l’élémentpiézoélectrique soient adéquatement choisis. Une partie importante du travail réaliséa été consacrée au développement d’une méthode innovante et fiable pour concevoirde nouveaux designs de transducteurs optimisés à la fois dans du béton frais ou durci.Nous avons choisi de tirer avantage du mode radial de vibration de disques piézoélectriquepeu coûteux au mode de vibration selon l’épaisseur. Ce dernier requiert en effetdes éléments plus épais ou des matériaux piézoélectriques composites plus coûteuxet dès lors peu appropriés à être intégré définitivement dans une structure en béton.Nous démontrons par ailleurs que les matériaux piézoélectriques composites à base dematériaux cimentaires qui sont abondamment étudiés semblent en réalité peu adaptés àêtre utilisés comme transducteurs ultrasonores, contrairement à des composites plusclassiques. Une attention particulière a été portée à comparer le fonctionnement destransducteurs externes et intégrés. Nous montrons par exemple que si les performancesdes transducteurs externes peuvent être améliorées sur base de la théorie d’adaptationde l’impédance acoustique, il en va tout autrement pour les transducteurs intégrés / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

A method that uses machine learning to detect and localize damage in railway bridges under various environmental conditions is proposed and validated in this work. The developed algorithm uses vertical and lateral deck accelerations as damage- sensitive features. Indeed, an Artificial Neural Network (ANN) is trained to predict deck accelerations in undamaged condition given: previous vibration data, air temperature and characteristics of the train crossing the bridge (speed, load position and load magnitude). After an appropriate training period, the comparison between ANN-predicted and measured accelerations allows to compute prediction errors. A Gaussian Process is then used to stochastically characterize prediction errors in undamaged conditions using train speed as independent variable. Recorded vibration data leading to abnormal prediction errors are flagged as damage. The method is validated both on a simple numerical example and on data recorded on a real structure. In the latter case, an appropriate algorithm was developed with the aim of extracting vehicles characteristics from the acceleration time histories. Together with this part of the algorithm for the pre-processing of recorded accelerations, the novelty of the developed method is the addition of air temperature to the input. It allows separating between structure responses that can be flagged as damage from those only affected by environmental conditions.

Structural Health Monitoring (SHM) is a promising tool for condition assessment of bridge structures. SHM of bridges can be performed for different purposes in long or short-term. A few aspects of short- and long-term monitoring of highway bridges are addressed in this research. Without quantifying environmental effects, applying vibration-based damage detection techniques may result in false damage identification. As part of a long-term monitoring project, the effect of temperature on vibrational characteristics of two continuously monitored bridges are studied. Natural frequencies of the structures are identified from ambient vibration data using the Natural Excitation Technique (NExT) along with the Eigen System Realization (ERA) algorithm. Variability of identified natural frequencies is investigated based on statistical properties of identified frequencies. Different statistical models are tested and the most accurate model is selected to remove the effect of temperature from the identified frequencies. After removing temperature effects, different damage cases are simulated on calibrated finite-element models. Comparing the effect of simulated damages on natural frequencies showed what levels of damage could be detected with this method. Evaluating traffic loads can be helpful to different areas including bridge design and assessment, pavement design and maintenance, fatigue analysis, economic studies and enforcement of legal weight limits. In this study, feasibility of using a single-span bridge as a weigh-in-motion tool to quantify the gross vehicle weights (GVW) of trucks is studied. As part of a short-term monitoring project, this bridge was subjected to four sets of high speed, live-load tests. Measured strain data are used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison is made between calculated and static weights, and furthermore, between supposed speeds and estimated speeds of the trucks. Vibration-based techniques that use finite-element (FE) model updating for SHM of bridges are common for infrastructure applications. This study presents the application of both static and dynamic-based FE model updating of a full scale bridge. Both dynamic and live-load testing were conducted on this bridge and vibration, strain, and deflections were measured at different locations. A FE model is calibrated using different error functions. This model could capture both global and local response of the structure and the performance of the updated model is validated with part of the collected measurements that were not included in the calibration process.