Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces

Assisting digital volume correlation with mechanical image-based modeling: application to the measurement of kinematic fields at the architecture scale in cellular materials

La mesure de champs de déplacement et de déformation aux petites échelles dans des microstructures complexes représente encore un défi majeur dans le monde de la mécanique expérimentale. Ceci est en partie dû aux acquisitions d'images et à la pauvreté de la texture à ces échelles. C'est notamment le cas pour les matériaux cellulaires lorsqu'ils sont imagés avec des micro-tomographes conventionnels et qu'ils peuvent être sujets à des mécanismes de déformation complexes. Comme la validation de modèles numériques et l'identification des propriétés mécaniques de matériaux se base sur des mesures précises de déplacements et de déformations, la conception et l'implémentation d'algorithmes robustes et fiables de corrélation d'images semble nécessaire. Lorsque l'on s'intéresse à l'utilisation de la corrélation d'images volumiques (DVC) pour les matériaux cellulaires, on est confronté à un paradoxe: l'absence de texture à l'échelle du constituant conduit à considérer l'architecture comme marqueur pour la corrélation. Ceci conduit à l'échec des techniques ordinaires de DVC à mesurer des cinématiques aux échelles subcellulaires en lien avec des comportements mécaniques locaux complexes tels que la flexion ou le flambement de travées. L'objectif de cette thèse est la conception d'une technique de DVC pour la mesure de champs de déplacement dans des matériaux cellulaires à l'échelle de leurs architectures. Cette technique assiste la corrélation d'images par une régularisation élastique faible en utilisant un modèle mécanique généré automatiquement et basé sur les images. La méthode suggérée introduit une séparation d'échelles au dessus desquelles la DVC est dominante et en dessous desquelles elle est assistée par le modèle mécanique basé sur l'image. Une première étude numérique consistant à comparer différentes techniques de construction de modèles mécaniques basés sur les images est conduite. L'accent est mis sur deux méthodes de calcul particulières: la méthode des éléments finis (FEM) et la méthode des cellules finies (FCM) qui consiste à immerger la géométrie complexe dans une grille régulière de haut ordre sans utiliser de mailleurs. Si la FCM évite une première phase délicate de discrétisation, plusieurs paramètres restent néanmoins délicats à fixer. Dans ce travail, ces paramètres sont ajustés afin d'obtenir (a) la meilleure précision (bornée par les erreurs de pixellisation) tout en (b) assurant une complexité minimale. Pour l'aspect mesure par corrélation d'images régularisée, plusieurs expérimentations virtuelles à partir de différentes simulations numériques (en élasticité, en plasticité et en non-linéarité géométrique) sont d'abord réalisées afin d'analyser l'influence des paramètres de régularisation introduits. Les erreurs de mesures peuvent dans ce cas être quantifiées à l'aide des solutions de référence éléments finis. La capacité de la méthode à mesurer des cinématiques complexes en absence de texture est démontrée pour des régimes non-linéaires tels que le flambement. Finalement, le travail proposé est généralisé à la corrélation volumique des différents états de déformation du matériau et à la construction automatique de la micro-architecture cellulaire en utilisant soit une grille B-spline d'ordre arbitraire (FCM) soit un maillage éléments finis (FEM). Une mise en évidence expérimentale de l'efficacité et de la justesse de l'approche proposée est effectuée à travers de la mesure de cinématiques complexes dans une mousse polyuréthane sollicitée en compression lors d'un essai in situ.Measuring displacement and strain fields at low observable scales in complex microstructures still remains a challenge in experimental mechanics often because of the combination of low definition images with poor texture at this scale. The problem is particularly acute in the case of cellular materials, when imaged by conventional micro-tomographs, for which complex highly non-linear local phenomena can occur. As the validation of numerical models and the identification of mechanical properties of materials must rely on accurate measurements of displacement and strain fields, the design and implementation of robust and faithful image correlation algorithms must be conducted. With cellular materials, the use of digital volume correlation (DVC) faces a paradox: in the absence of markings of exploitable texture on/or in the struts or cell walls, the available speckle will be formed by the material architecture itself. This leads to the inability of classical DVC codes to measure kinematics at the cellular and a fortiori sub-cellular scales, precisely because the interpolation basis of the displacement field cannot account for the complexity of the underlying kinematics, especially when bending or buckling of beams or walls occurs. The objective of the thesis is to develop a DVC technique for the measurement of displacement fields in cellular materials at the scale of their architecture. The proposed solution consists in assisting DVC by a weak elastic regularization using an automatic image-based mechanical model. The proposed method introduces a separation of scales above which DVC is dominant and below which it is assisted by image-based modeling. First, a numerical investigation and comparison of different techniques for building automatically a geometric and mechanical model from tomographic images is conducted. Two particular methods are considered: the finite element method (FEM) and the finite-cell method (FCM). The FCM is a fictitious domain method that consists in immersing the complex geometry in a high order structured grid and does not require meshing. In this context, various discretization parameters appear delicate to choose. In this work, these parameters are adjusted to obtain (a) the best possible accuracy (bounded by pixelation errors) while (b) ensuring minimal complexity. Concerning the ability of the mechanical image-based models to regularize DIC, several virtual experimentations are performed in two-dimensions in order to finely analyze the influence of the introduced regularization lengths for different input mechanical behaviors (elastic, elasto-plastic and geometrically non-linear) and in comparison with ground truth. We show that the method can estimate complex local displacement and strain fields with speckle-free low definition images, even in non-linear regimes such as local buckling. Finally a three-dimensional generalization is performed through the development of a DVC framework. It takes as an input the reconstructed volumes at the different deformation states of the material and constructs automatically the cellular micro-architeture geometry. It considers either an immersed structured B-spline grid of arbitrary order or a finite-element mesh. An experimental evidence is performed by measuring the complex kinematics of a polyurethane foam under compression during an in situ test

Comportamento mecânico de espumas de ligas de alumínio modeladas com recurso a micro-tomografia computorizada de raios-X

In recent years, there has been an increase in interest in cellular materials for structural applications, especially cellular metals (e.g., metal foams made of aluminium and its alloys). These closed-cell and open-cell foams usually have complex cellular structures resulting from the foaming process and their mechanical properties are governed by their cellular structures and by the properties of the base material. However, their mechanical characterization is difficult and most of the times can result in the destruction of the foam specimen. In this study, X-ray microcomputed tomography (µCT) was used together with finite element modelling to develop numerical models to estimate the elastic moduli and evaluate the effects of processing of the information obtained with the µCT scans in the final results. Such a technique complements experimental testing and brings great versatility. In order to accomplish this task, different thresholding techniques (segmentation) were applied to the 2D slices, which are the result of µCT scans, with special focus on a manual global technique with the mass as a quality indicator. Then, some reconstruction algorithms (e.g. Marching Cubes 33) were used to create 3D tessellated models in the STL format, which were oversampled (excessive number of faces) and with errors. Therefore, a simplification/clean-up procedure was applied to solve those issues, being analysed in terms of mass maintenance, shape maintenance with the Hausdorff algorithm and face quality, i.e., face aspect ratio. Two different procedures were evaluated, with and without small structural imperfections, so that the impact of the procedures could be analysed as well as the effect of the presence of small defects. The results obtained were evaluated and compared to several analytical and theoretical models, models based on representative unit-cells and experimental results in terms of the relation between the relative density and the relative Young’s modulus. Results demonstrated that the developed procedures were very good at minimizing changes in mass and shape of the geometries while providing good face quality, i.e., face aspect ratio. The models were also shown to be able to predict the properties of metallic foams in accordance with the findings of other researchers. In addition, the process of obtaining the models and the presence of small structural imperfections were shown to have a great impact on the final results.Nos últimos anos, tem-se verificado um aumento do interesse na área dos materiais celulares, mais especificamente metais celulares, para aplicações estruturais (por exemplo, espumas metálicas de alumínios e as suas ligas). Estas espumas de célula aberta e fechada têm, normalmente, uma estrutura celular complexa resultante do processo de espumação e as suas propriedades mecânicas dependem das suas estruturas celulares e das propriedades do material base. No entanto, a caracterização mecânicas destes materiais é difícil e resulta, regularmente, na destruição dos specimens de espuma. Neste estudo, Micro-Tomografia Computorizada de Raios-X (µCT) foi aplicada juntamente com modelação por elementos finitos para desenvolver modelos numéricos que conseguem estimar os módulos de elasticidade e avaliar os efeitos do processamento da informação obtida pelos scans de µCT nos resultados finais. Esta técnica complementa os procedimentos experimentais e traz uma grande versatilidade. Para se completar a tarefa proposta, diferentes métodos de segmentação foram aplicados às fatias 2D, que são resultantes dos scans de µCT, com especial atenção num método de segmentação manual global que utiliza a massa como indicador de qualidade. Depois disso, alguns algoritmos de reconstrução, por exemplo, Marching Cubes 33, foram aplicados para criar modelos 3D de faces triangulares no formato STL que demonstram sobreamostragem (excessiva quantidade de faces) e alguns erros. Por essa razão, um procedimento de simplificação/limpeza foi aplicado para resolver estes problemas, sendo analisados em termos de preservação de massa, preservação de forma com o algoritmo de Hausdorff e qualidade das faces, ou seja, razão de proporção. Dois procedimentos diferentes foram avaliados, um com e outro sem pequenos defeitos estruturais para que se consiga analisar não só o impacto do processamento dos modelos assim como o efeito da presença de pequenos defeitos. Os resultados obtidos foram comparados com vários modelos analíticos e teóricos, modelos baseados em células unitárias representativas e resultados experimentais com base na relação entre a densidade relativa e o modulo de Young relativo. Os resultados demonstraram que os procedimentos desenvolvidos são bons a preservar a massa e forma das geometrias deixando as faces com boa qualidade. Verificou-se também que os modelos foram capazes de prever as propriedades das espumas metálicas em concordância com o trabalho de outros investigadores. Adicionalmente, mostrou-se que o processo de obtenção dos modelos e a presença de pequenas imperfeiçoes estruturais tem um impacto relevante nos resultados finais.Mestrado em Engenharia Mecânic

Virtual Reality Simulation of Glenoid Reaming Procedure

Glenoid reaming is a bone machining operation in Total Shoulder Arthroplasty (TSA) in which the glenoid bone is resurfaced to make intimate contact with implant undersurface. While this step is crucial for the longevity of TSA, many surgeons find it technically challenging. With the recent advances in Virtual Reality (VR) simulations, it has become possible to realistically replicate complicated operations without any need for patients or cadavers, and at the same time, provide quantitative feedback to improve surgeons\u27 psycho-motor skills. In light of these advantages, the current thesis intends to develop tools and methods required for construction of a VR simulator for glenoid reaming, in an attempt to construct a reliable tool for preoperative training and planning for surgeons involved with TSA. Towards the end, this thesis presents computational algorithms to appropriately represent surgery tool and bone in the VR environment, determine their intersection and compute realistic haptic feedback based on the intersections. The core of the computations is constituted by sampled geometrical representations of both objects. In particular, point cloud model of the tool and voxelized model of bone - that is derived from Computed Tomography (CT) images - are employed. The thesis shows how to efficiently construct these models and adequately represent them in memory. It also elucidates how to effectively use these models to rapidly determine tool-bone collisions and account for bone removal momentarily. Furthermore, the thesis applies cadaveric experimental data to study the mechanics of glenoid reaming and proposes a realistic model for haptic computations. The proposed model integrates well with the developed computational tools, enabling real-time haptic and graphic simulation of glenoid reaming. Throughout the thesis, a particular emphasis is placed upon computational efficiency, especially on the use of parallel computing using Graphics Processing Units (GPUs). Extensive implementation results are also presented to verify the effectiveness of the developments. Not only do the results of this thesis advance the knowledge in the simulation of glenoid reaming, but they also rigorously contribute to the broader area of surgery simulation, and can serve as a step forward to the wider implementation of VR technology in surgeon training programs

Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

Microstructure Characterization of Continuous-Discontinuous Fibre Reinforced Polymers based on Volumetric Images

Die quantitative Beschreibung der Mikrostruktur von Faserverbundwerkstoffen ist elementar für die Modellierung von thermischen und mechanischen Eigenschaften. Durch die stetige Entwicklung der Computertomographie ist es heute möglich dreidimensionale Bilddaten von Werkstoffen mit einer Auflösung von unter einem Mikrometer zu erzeugen. Moderne Computersysteme bieten ausreichend Rechenleistung um die resultierenden volumetrischen Bilddaten automatisiert auszuwerten und relevante Statistiken zu erzeugen. Die vorliegende Arbeit befasst sich mit der Quantifizierung von mikrostrukturellen Merkmalen von faserverstärkten Polymeren unter Verwendung von computertomographischen Aufnahmen. Diverse Verfahren zur Bestimmung von lokalen Faserorientierungen, -volumengehalt, -krümmungen und -längen wurden implementiert und validiert. Des Weiteren wurden zwei Ansätze zur Berechnung von lokalen Oberflächenkrümmungen zur Porositätsanalyse verglichen. Die Ergebnisse zeigen, dass einige der bereits verfügbaren Orientierungsanalyseverfahren bereits sehr robust sind und auch mit stark verrauschten Aufnahmen mit geringem Kontrast sehr gute Resultate erzielen. Faserlängenverteilungen, die mittels Fasertrackingverfahren aus computertomographischen Aufnahmen extrahiert wurden lieferten nur bis zu einer Probengröße von 5mm verlässliche Faserlängenverteilungen und sind daher nur bedingt für die Anwendung an langfaserverstärkten Polymeren geeignet

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

Evaluation of internal damage in reinforced concrete elements using ultrasonic tomography

Quality control and quality assurance (QC/QA) of the concrete infrastructure has become an important national issue, especially because construction inaccuracies and invisible internal defects can result in unexpected structural response and failure. In order to evaluate the condition of an existing concrete structure, non-destructive testing (NDT) has been widely used as an assessment tool. Ultrasonic pulse velocity (UPV) is an efficient method to characterize the condition of concrete elements, and tomographic imaging is a powerful tool for visually identifying internal damage. However, the implementation of UPV data within a tomographic imaging scheme for application to full-scale concrete (RC) structures has not been realized to date because of practical and technological restrictions. In this dissertation, some of those barriers are overcome by using contactless air-coupled ultrasonic sensors in a scanning test configuration to acquire large amounts of ultrasonic data to create ultrasonic tomograms of large-scale concrete structures. The development of the testing system is described. The measurements are carried out using an automated robotic scanning frame using new sensing technology. Image reconstruction algorithms, including synthetic aperture focusing technique (SAFT) and algebraic reconstruction technique (ART), are reviewed and evaluated for application to imaging of full-scale RC columns. The performance of the data collection system and selected optimal imaging approach are verified through tests on a RC column test sample containing embedded artificial defects. The obtained tomographic images are compared with those from a commercially available ultrasonic imaging device. A comprehensive visualization scheme to characterize the column test sample, based on fusion of integrated ultrasonic tomography and 3-D computer vision, is presented. Such integrated visualization provides holistic characterization of the test sample. Next, the utility of attenuation tomography for enhanced damage detection is evaluated, both through numerical simulation and experimental studies. Finally, the developed ultrasonic tomographic testing system is applied to full-scale RC columns and slab-beam-column sub-assemblages subjected to simulated earthquake loads. Different concrete types, including normal reinforced concrete and high performance fiber-reinforced concrete, and seismic different loading schemes are considered. Comparisons of ultrasonic tomograms and strain gauge data illustrate the potential for velocity and attenuation tomography to monitor internal damage progression of structural RC elements both at global and local levels