Cracking and deterioration of concrete are the leading causes of a premature failing of reinforced concrete structures. To assess the condition of concrete, a variety of destructive and non-destructive testing methods have been developed. From these two methods, the non-destructive testing (NDT) is a more favorable (albeit more challenging) option since the tested target is left undamaged. The NDT modalities include acoustic, electromagnetic and radiation based techniques. In this thesis, the feasibility of electrical resistance tomography (ERT) for NDT of concrete is studied. In ERT, electric currents are injected into the target through electrodes that are are attached to the boundary of the target. The resulting voltages between the electrodes are measured and this boundary voltage data is then used to reconstruct the internal conductivity distribution of the target. The reconstruction of the internal conductivity distribution of concrete is expected to provide valuable information about the condition of the structure so that appropriate repairs can be taken in time. The difficulty in ERT, as is in any other diffuse tomography modality, is that the problem has a nature of an ill-posed inverse problem. This implies that the solutions of the problem are unstable and nonunique in the classical sense. As a consequence, extra attention must be directed to the mathematical modeling of the measurements as well as to the reconstruction methods. Furthermore, concrete is strongly heterogeneous material composed of cement matrix, aggregate and different chemical compounds that create a challenging target for electrical modalities. Previous studies have shown that ERT is a potential tool for NDT of concrete, but the quality of the reconstruction was not yet sufficient for practical applications. In this thesis, especially localizing reinforcing bars and crack identification are considered. To meet those aims, novel computational methods for the image reconstruction are developed. For the crack identification and localizing reinforcing bars, a novel adaptive meshing approach was developed. In the new approach cracks and reinforcing bars are modeled as internal structures. The results show that by employing accurate mathematical models and statistical inversion techniques based on the Bayesian framework, ERT can become an applicable tool for practical NDT of concrete.
There are many different electrical impedance tomography (EIT) systems which are either non-commercial (in-house products) or commercial products. However, these systems are usually designed for specific applications and therefore the functionality of the systems might be limited. Nowadays there are commercially available many low-cost, efficient and accurate multifunctional components for data acquisition and signal processing. Therefore, it should be possible to construct an EIT system which is mainly built from commercially available components. The main goal of this work was to study the performance of a PXI-based EIT systemPCI eXtension for Instrumentation.. In this work, a PXI-based EIT system with 16 independent current injection channels and 80 independent measurement channels was constructed and tested. The results indicate that an EIT system can be constructed using a PXI platform which decreases the construction time of the system. Moreover, the system is efficient, accurate, modular, and it is not limited to any predetermined measurement protocols.
[1] We propose an approach for imaging the dynamics of complex hydrological processes. The evolution of electrically conductive fluids in porous media is imaged using time-lapse electrical resistance tomography. The related dynamic inversion problem is solved using Bayesian filtering techniques; that is, it is formulated as a sequential state estimation problem in which the target is an evolving posterior probability density of the system state. The dynamical inversion framework is based on the state space representation of the system which involves the construction of a stochastic evolution model and an observation model. The observation model that we use in this paper consists of the complete electrode model for ERT, with Archie's law relating saturations to electrical conductivity. The evolution model is an approximate model for simulating flow through partially saturated porous media. Unavoidable modeling and approximation errors in both the observation and evolution models are considered by computing approximate statistics for these errors. These models are then included in the construction of the posterior probability density of the estimated system state. This approximation error method allows the use of approximate, and therefore computationally efficient, observation and evolution models in the Bayesian filtering. We conside7r a synthetic example and show that the incorporation of an explicit model for the model uncertainties in the state space representation can yield better estimates than the frame-by-frame imaging approach.
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