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Department of Urban and Environmental Engineering (Disaster Management Engineering)Seismic risk assessment has recently emerged as an important issue for infrastructure systems because of their vulnerability to seismic hazards. Earthquakes can have significant impacts on transportation networks such as bridge collapse and the resulting disconnections in a network. One of the main concerns is the accurate estimation of the seismic risk caused by the physical damage of bridges and the reduced performance of the associated transportation network. This requires estimating the performance of a bridge transportation network at the system level. Moreover, it is necessary to deal with various possible earthquake scenarios and the associated damage states of component bridges considering the uncertainty of earthquake locations and magnitudes. To perform the seismic risk assessment of a bridge transportation network, system reliability is required. It is a challenging task for several reasons. First, the seismic risk itself contains a great deal of uncertainty, which comprises location, magnitude, and the resulting intensity of possible earthquakes in a target network. Second, the system performance of a bridge transportation network after the seismic event needs to be estimated accurately, especially for realistic and complex networks. Third, the seismic risk assessment employing system reliability may increase the computational costs and can be time-consuming tasks, because it requires dealing with various possible earthquake scenarios and the resulting seismic fragility of component bridges. Fourth, a precise performance measure of the system needs to be introduced. In this study, a new method is proposed to assess the system-level seismic risk of bridge transportation networks considering earthquake uncertainty. In addition, a new performance measure is developed to help risk-informed decision-making regarding seismic hazard mitigation and disaster management. For the tasks, first of all, a matrix-based system reliability framework is developed, which performs the estimation of a bridge transportation network subjected to earthquakes. Probabilistic seismic hazard analysis (PSHA) is introduced to enable the seismic fragility estimation of the component bridges, considering the uncertainty of earthquake locations and magnitudes. This is systemically used to carry out a post-hazard bridge network flow analysis by employing the matrix-based framework. Secondly, two different network performance measures are used to quantify the network performance after a seismic event. Maximum flow capacity was originally used for a bridge transportation network, however the numerical example using this measure is further developed for applications to more accurate system performance analysis using total system travel time (TSTT). Finally, a new method for system-level seismic risk assessment is proposed to carry out a bridge network flow analysis based on TSTT by employing the matrix-based system reliability (MSR) method. In the proposed method, the artificial neuron network (ANN) is introduced to approximate the network performance, which can reduce the computational cost of network analysis. The proposed method can provide statistical moments of the network performance and component importance measures, which can be used by decision-makers to reduce the seismic risk of a target area. The proposed method is tested by application to a numerical example of an actual transportation network in South Korea. In the seismic risk assessment of the example, PSHA is successfully integrated with the matrix-based framework to perform system reliability analysis in a computationally efficient manner.clos

Medical image registration by neural networks: a regression-based registration approach

This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range. First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment. Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy. Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data